DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA DT DECON DOCUMENTO DE TRABAJO DOES THE CENTRAL BANK OF PERU RESPOND TO EXCHANGE RATE MOVEMENTS? A BAYESIAN ESTIMATION OF NEW KEYNESIAN DSGE MODEL WITH FX INTERVENTIONS Nº 504 Gabriel Rodríguez, Paul Castillo B. y Harumi Hasegawa DOCUMENTO DE TRABAJO N° 504 Does the Central Bank of Peru Respond to Exchange Rate Movements? A Bayesian Estimation of a New Keynesian DSGE Model with FX Interventions Gabriel Rodríguez, Paul Castillo B. y Harumi Hasegawa Diciembre, 2021 DOCUMENTO DE TRABAJO 504 http://doi.org/10.18800/2079-8474.0504 http://doi.org/10.18800/2079-8474.0504 Does the Central Bank of Peru Respond to Exchange Rate Movements? A Bayesian Estimation of a New Keynesian DSGE Model with FX Interventions Documento de Trabajo 504 © Gabriel Rodríguez, Paul Castillo B. y Harumi Hasegawa Editado e Impreso: © Departamento de Economía – Pontificia Universidad Católica del Perú Av. Universitaria 1801, Lima 32 – Perú. Teléfono: (51-1) 626-2000 anexos 4950 - 4951 econo@pucp.edu.pe http://departamento.pucp.edu.pe/economia/publicaciones/documentos-de-trabajo/ Encargada de la Serie: Roxana Barrantes Cáceres Departamento de Economía – Pontificia Universidad Católica del Perú Barrantes.r@pucp.edu.pe Primera edición – Diciembre, 2021. ISSN 2079-8474 (En línea) mailto:econo@pucp.edu.pe mailto:Barrantes.r@pucp.edu.pe Does the Central Bank of Peru Respond to Exchange Rate Movements? A Bayesian Estimation of a New Keynesian DSGE Model with FX Interventions∗ Gabriel Rodríguez† Pontificia Universidad Católica del Perú Paul Castillo B.‡ Central Reserve Bank of Peru Pontificia Universidad Católica del Perú Harumi Hasegawa§ Pontificia Universidad Católica del Perú August 18, 2021 Abstract This paper assess the role played by the exchange rate and FX intervention in setting monetary policy interest rates in Peru. We estimate a Taylor rule that includes inflation, output gap and the exchange rate using a New Keynesian DSGE model that follows closely Schmitt-Grohé and Uribe (2017). The model is extended to include an explicit sterilized FX intervention rule as in Faltermeier et al. (2017). The main empirical results show, for the pre Inflation Targeting (IT) and IT periods, that the model that clearly outperforms in terms of marginal log density, features a Taylor rule that does not respond to changes in the nominal exchange rate and an active use of FX intervention by the Central Bank. We also find that the coefficient associated with the response of the Taylor rule to inflation is close to 2 and the one associated with the output gap is greater than 1; and that FX intervention has become more responsive to exchange rate fluctuations during the IT period. Finally, the estimated IRFs shows that FX intervention has contributed to reduce the volatility of GDP in response to productivity and terms of trade shocks in Peru. JEL Classification: C22, C52, F41. Keywords: Small Open Economy; Taylor Rule; Monetary Policy Rule; Exchange Rate; Bayesian Methodology; Peruvian Economy; FX interventions; New Keynesian DSGE Model. ∗This document is a substantially revised and improved version of Harumi Hasegawa’s Thesis, Faculty of Social Sciences, Department of Economics, Pontificia Universidad Católica del Perú (PUCP). A very preliminary version was presented to the 35th BCRP Meeting of Economists (Lima, October 2017) and the Meeting of the Peruvian Economic Association-APE (Lima, October 2016). We appreciate the comments received by the participants of these events as well as the comments of Marco Vega (BCRP). The views expressed in this paper are those of the authors and do not necessarily reflect the position of the BCRP. Any remaining errors are our responsibility. †Address for Correspondence: Gabriel Rodríguez, Department of Economics, Pontificia Universidad Católica del Perú, 1801 Universitaria Avenue, Lima 32, Lima, Perú, Telephone: +511-626-2000 (4998), E-Mail Address: gabriel.rodriguez@pucp.edu.pe. ORCID ID: https://orcid.org/0000-0003-1174-9642. ‡Central Reserve Bank of Peru, 441-445 Santa Rosa Street, Lima 1, Lima, Peru, E-Mail Address: paul.castillo@bcrp.gob.pe and Department of Economics, Pontificia Universidad Católica del Peru, 1801 Universitaria Avenue, Lima 32, Lima, Peru. ORCID ID: https://orcid.org/0000-0003-3769-8660. §Department of Economics, Pontificia Universidad Católica del Perú, 1801 Universitaria Avenue, Lima 32, Lima, Peru, E-Mail Address: harumi.hasegawa@pucp.pe. ➽❘❡s♣♦♥❞❡ ❧❛ P♦❧ít✐❝❛ ▼♦♥❡t❛r✐❛ ❞❡❧ ❇❛♥❝♦ ❈❡♥tr❛❧ ❞❡ ❘❡s❡r✈❛ ❞❡❧ P❡rú ❛❧ ❚✐♣♦ ❞❡ ❈❛♠❜✐♦❄ ❯♥❛ ❊st✐♠❛❝✐ó♥ ❇❛②❡s✐❛♥❛ ❞❡ ✉♥ ▼♦❞❡❧♦ ❉❙●❊ ◆❡♦ ❑❡②♥❡s✐❛♥♦ ❝♦♥ ■♥t❡r✈❡♥❝✐ó♥ ❈❛♠❜✐❛r✐❛∗ ●❛❜r✐❡❧ ❘♦❞rí❣✉❡③† P♦♥t✐✜❝✐❛ ❯♥✐✈❡rs✐❞❛❞ ❈❛tó❧✐❝❛ ❞❡❧ P❡rú P❛✉❧ ❈❛st✐❧❧♦ ❇✳‡ ❇❛♥❝♦ ❈❡♥tr❛❧ ❞❡ ❘❡s❡r✈❛ ❞❡❧ P❡rú P♦♥t✐✜❝✐❛ ❯♥✐✈❡rs✐❞❛❞ ❈❛tó❧✐❝❛ ❞❡❧ P❡rú ❍❛r✉♠✐ ❍❛s❡❣❛✇❛➓ P♦♥t✐✜❝✐❛ ❯♥✐✈❡rs✐❞❛❞ ❈❛tó❧✐❝❛ ❞❡❧ P❡rú ✶✽ ❞❡ ❆❣♦st♦ ✷✵✷✶ ❘❡s✉♠❡♥ ❊st❡ ❞♦❝✉♠❡♥t♦ ❡✈❛❧ú❛ ❡❧ r♦❧ q✉❡ ❥✉❡❣❛ ❡❧ t✐♣♦ ❞❡ ❝❛♠❜✐♦ ② ❧❛ ✐♥t❡r✈❡♥❝✐ó♥ ❝❛♠❜✐❛r✐❛ ❡♥ ❧❛ ✜❥❛❝✐ó♥ ❞❡ ❧❛ t❛s❛ ❞❡ ✐♥t❡rés ❞❡ ♣♦❧ít✐❝❛ ♠♦♥❡t❛r✐❛ ❡♥ P❡rú✳ ❊st✐♠❛♠♦s ✉♥❛ r❡❣❧❛ ❞❡ ❚❛②❧♦r q✉❡ ✐♥❝❧✉②❡ ❧❛ ✐♥✢❛❝✐ó♥✱ ❧❛ ❜r❡❝❤❛ ❞❡❧ ♣r♦❞✉❝t♦ ② ❡❧ t✐♣♦ ❞❡ ❝❛♠❜✐♦ ✉t✐❧✐③❛♥❞♦ ✉♥ ♠♦❞❡❧♦ ❉❙●❊ ◆❡♦ ❑❡②♥❡s✐❛♥♦ ❜❛s❛❞♦ ❡♥ ❙❝❤♠✐tt✲●r♦❤é ② ❯r✐❜❡ ✭✷✵✶✼✮✳ ❊❧ ♠♦❞❡❧♦ s❡ ❡①t✐❡♥❞❡ ♣❛r❛ ✐♥❝❧✉✐r ✉♥❛ r❡❣❧❛ ❡①♣❧í❝✐t❛ ❞❡ ✐♥t❡r✈❡♥❝✐ó♥ ❝❛♠❜✐❛r✐❛ ❡st❡r✐❧✐③❛❞❛ ❝♦♠♦ ❡♥ ❋❛❧t❡r♠❡✐❡r ❡t ❛❧✳ ✭✷✵✶✼✮✳ ▲♦s ♣r✐♥❝✐♣❛❧❡s r❡s✉❧t❛❞♦s ❡♠♣ír✐❝♦s ♠✉❡str❛♥ q✉❡ t❛♥t♦ ♣❛r❛ ❡❧ ♣❡r✐♦❞♦ ❛♥t❡r✐♦r ❛❧ r❡❣✐♠❡♥ ❞❡ ♠❡t❛s ❡①♣❧í❝✐t❛s ❞❡ ✐♥✢❛❝✐ó♥ ✭■❚✮ ❝♦♠♦ ♣❛r❛ ❡❧ ♣❡r✐♦❞♦ ❞❡❧ ■❚✱ ❡❧ ♠♦❞❡❧♦ q✉❡ ❝❧❛r❛♠❡♥t❡ ♠✉❡str❛ ✉♥ ♠❡❥♦r ❛❥✉st❡ ❞❡ ❧♦s ❞❛t♦s✱ ♠❡❞✐❞♦ ♣♦r ❡❧ ❧♦❣❛rt✐♠♦ ❞❡ ❧❛ ❢✉♥❝✐ó♥ ❞❡ ❞❡♥s✐❞❛❞ ♠❛r❣✐♥❛❧✱ ❡s ❛q✉é❧ q✉❡ ♣r❡s❡♥t❛ ✉♥❛ r❡❣❧❛ ❞❡ ❚❛②❧♦r q✉❡ ♥♦ r❡s♣♦♥❞❡ ❛ ❝❛♠❜✐♦s ❡♥ ❡❧ t✐♣♦ ❞❡ ❝❛♠❜✐♦ ♥♦♠✐♥❛❧ ② ❡♥ ❡❧ q✉❡ ❡❧ ❜❛♥❝♦ ❝❡♥tr❛❧ ❤❛❝❡ ✉♥ ✉s♦ ❛❝t✐✈♦ ❞❡ ❧❛ ✐♥t❡r✈❡♥❝✐ó♥ ❝❛♠❜✐❛r✐❛✳ ❆s✐♠✐s♠♦✱ ❡♥❝♦♥tr❛♠♦s q✉❡ ❡❧ ❝♦❡✜❝✐❡♥t❡ ❛s♦❝✐❛❞♦ ❛ ❧❛ r❡s♣✉❡st❛ ❞❡ ❧❛ r❡❣❧❛ ❞❡ ❚❛②❧♦r ❛ ❧❛ ✐♥✢❛❝✐ó♥ ❡s ❝❡r❝❛♥♦ ❛ ✷ ② ❡❧ ❛s♦❝✐❛❞♦ ❛ ❧❛ ❜r❡❝❤❛ ❞❡❧ ♣r♦❞✉❝t♦ ❡s ♠❛②♦r q✉❡ ✶❀ ② q✉❡ ❧❛ ✐♥t❡r✈❡♥❝✐ó♥ ❝❛♠❜✐❛r✐❛ s❡ ❤❛ ✈✉❡❧t♦ ♠ás s❡♥s✐❜❧❡ ❛ ❧❛s ✢✉❝t✉❛❝✐♦♥❡s ❞❡❧ t✐♣♦ ❞❡ ❝❛♠❜✐♦ ❞✉r❛♥t❡ ❡❧ ♣❡rí♦❞♦ ❞❡ ■❚✳ ❋✐♥❛❧♠❡♥t❡✱ ❧❛s ❢✉♥❝✐♦♥❡s ✐♠♣✉❧s♦ r❡s♣✉❡st❛ ✭❋■❘✮ ❡st✐♠❛❞❛s ♠✉❡str❛♥ q✉❡ ❧❛ ✐♥t❡r✈❡♥❝✐ó♥ ❝❛♠❜✐❛r✐❛ ❤❛ ❝♦♥tr✐❜✉✐❞♦ ❛ r❡❞✉❝✐r ❧❛ ✈♦❧❛t✐❧✐❞❛❞ ❞❡❧ P❇■ ❢r❡♥t❡ ❛ ❝❤♦q✉❡s ❞❡ ♣r♦❞✉❝t✐✈✐❞❛❞ ② ❞❡ tér♠✐♥♦s ❞❡ ✐♥t❡r❝❛♠❜✐♦ ❡♥ P❡rú✳ ❈❧❛s✐✜❝❛❝✐ó♥ ❏❊▲✿ ❈✷✷✱ ❈✺✷✱ ❋✹✶✳ P❛❧❛❜r❛s ❈❧❛✈❡s✿ ❊❝♦♥♦♠í❛ P❡q✉❡ñ❛ ❆❜✐❡rt❛❀ ❘❡❣❧❛ ❞❡ ❚❛②❧♦r❀ ❘❡❣❧❛ ❞❡ P♦❧ít✐❝❛ ▼♦♥❡t❛r✐❛❀ ❚✐♣♦ ❞❡ ❈❛♠❜✐♦❀ ▼❡t♦❞♦❧♦❣í❛ ❇❛②❡s✐❛♥❛❀ ❊❝♦♥♦♠í❛ P❡r✉❛♥❛❀ ■♥t❡r✈❡♥❝✐♦♥❡s ❈❛♠❜✐❛r✐❛s❀ ▼♦❞❡❧♦ ❉❙●❊ ◆❡♦ ❑❡②♥❡s✐❛♥♦✳ ∗❊st❡ ❞♦❝✉♠❡♥t♦ ❡s ✉♥❛ ✈❡rs✐ó♥ s✉st❛♥❝✐❛❧♠❡♥t❡ r❡✈✐s❛❞❛ ② ♠❡❥♦r❛❞❛ ❞❡ ❧❛ ❚❡s✐s ❞❡ ❍❛r✉♠✐ ❍❛s❡❣❛✇❛✱ ❋❛❝✉❧t❛❞ ❞❡ ❈✐❡♥❝✐❛s ❙♦❝✐❛❧❡s✱ ❉❡♣❛rt❛♠❡♥t♦ ❞❡ ❊❝♦♥♦♠í❛✱ P♦♥t✐✜❝✐❛ ❯♥✐✈❡rs✐❞❛❞ ❈❛tó❧✐❝❛ ❞❡❧ P❡rú ✭P❯❈P✮✳ ❯♥❛ ✈❡rs✐ó♥ ♠✉② ♣r❡❧✐♠✐♥❛r ❢✉❡ ♣r❡✲ s❡♥t❛❞❛ ❛❧ ✸✺ ❊♥❝✉❡♥tr♦ ❞❡ ❊❝♦♥♦♠✐st❛s ❞❡❧ ❇❈❘P ✭▲✐♠❛✱ ❖❝t✉❜r❡ ✷✵✶✼✮ ② ❛❧ ❈♦♥❣r❡s♦ ❞❡ ❧❛ ❆s♦❝✐❛❝✐ó♥ P❡r✉❛♥❛ ❞❡ ❊❝♦♥♦♠í❛ ✭▲✐♠❛✱ ❖❝t✉❜r❡ ✷✵✶✻✮✳ ❆❣r❛❞❡❝❡♠♦s ❛ ❧♦s ♣❛rt✐❝✐♣❛♥t❡s ❞❡ ❞✐❝❤♦s ❡✈❡♥t♦s ② ❛ ▼❛r❝♦ ❱❡❣❛ ✭❇❈❘P✮ ❧♦s ❝♦♠❡t❛r✐♦s r❡❝✐❜✐❞♦s✳ ▲❛s ♦♣✐♥✐♦♥❡s ❡①♣r❡s❛❞❛s ❡♥ ❡st❡ ❞♦❝✉♠❡♥t♦ s♦♥ ❞❡ ❧♦s ❛✉t♦r❡s ② ♥♦ r❡✢❡❥❛♥ ♥❡❝❡s❛r✐❛♠❡♥t❡ ❧❛ ♣♦s✐❝✐ó♥ ❞❡❧ ❇❈❘P✳ ❈✉❛❧q✉✐❡r ❡rr♦r q✉❡ ♣❡rs✐st❛ ❡s ♥✉❡str❛ r❡s♣♦♥s❛❜✐❧✐❞❛❞✳ †❉✐r❡❝❝✐ó♥ ❞❡ ❈♦rr❡s♣♦♥❞❡♥❝✐❛✿ ●❛❜r✐❡❧ ❘♦❞rí❣✉❡③✱ ❉❡♣❛rt❛♠❡♥t♦ ❞❡ ❊❝♦♥♦♠í❛✱ P♦♥t✐✜❝✐❛ ❯♥✐✈❡rs✐❞❛❞ ❈❛tó❧✐❝❛ ❞❡❧ P❡rú✱ ❆✈❡♥✐❞❛ ❯♥✐✈❡rs✐t❛r✐❛ ✶✽✵✶✱ ▲✐♠❛ ✸✷✱ ▲✐♠❛✱ P❡rú✱ ❚❡❧é❢♦♥♦✿ ✰✺✶✶✲✻✷✻✲✷✵✵✵ ✭✹✾✾✽✮✱ ❈♦rr❡♦ ❊❧❡❝tró♥✐❝♦✿ ❣❛❜r✐❡❧✳r♦❞r✐❣✉❡③❅♣✉❝♣✳❡❞✉✳♣❡✳ ❖❘❈■❉ ■❉✿ ❤tt♣s✿✴✴♦r❝✐❞✳♦r❣✴✵✵✵✵✲✵✵✵✸✲✶✶✼✹✲✾✻✹✷✳ ‡❇❛♥❝♦ ❈❡♥tr❛❧ ❞❡ ❘❡s❡r✈❛ ❞❡❧ P❡rú✱ ❏✐ró♥ ❙❛♥t❛ ❘♦s❛ ✹✹✶✲✹✹✺✱ ▲✐♠❛ ✶✱ ▲✐♠❛✱ P❡r✉✱ ❈♦rr❡♦ ❊❧❡❝tró♥✐❝♦✿ ♣❛✉❧✳❝❛st✐❧❧♦❅❜❝r♣✳❣♦❜✳♣❡ ② ❉❡♣❛rt❛♠❡♥t♦ ❞❡ ❊❝♦♥♦♠í❛✱ P♦♥t✐✜❝✐❛ ❯♥✐✈❡rs✐❞❛❞ ❈❛tó❧✐❝❛ ❞❡❧ P❡r✉✱ ❆✈❡♥✐❞❛ ❯♥✐✈❡rs✐t❛r✐❛ ✶✽✵✶✱ ▲✐♠❛ ✸✷✱ ▲✐♠❛✱ P❡r✉✳ ❖❘❈■❉ ■❉✿ ❤tt♣s✿✴✴♦r❝✐❞✳♦r❣✴✵✵✵✵✲✵✵✵✸✲✸✼✻✾✲✽✻✻✵✳ ➓❉❡♣❛rt❛♠❡♥t♦ ❞❡ ❊❝♦♥♦♠í❛✱ P♦♥t✐✜❝✐❛ ❯♥✐✈❡rs✐❞❛❞ ❈❛tó❧✐❝❛ ❞❡❧ P❡rú✱ ❆✈❡♥✐❞❛ ❯♥✐✈❡rs✐t❛r✐❛ ✶✽✵✶✱ ▲✐♠❛ ✸✷✱ ▲✐♠❛✱ P❡r✉✱ ❈♦rr❡♦ ❊❧❡❝tró♥✐❝♦✿ ❤❛r✉♠✐✳❤❛s❡❣❛✇❛❅♣✉❝♣✳♣❡✳ 1 Introduction The monetary policy rule proposed by Taylor (1993), has proven to be an accurate description of how Central Banks implement their monetary policy, particularly in developed economies, see Orphanides (2003). In closed economies, this rule states that interest rate can be char- acterized by a simple linear function that links interest rates to inflation and unemployment or output gap. For developed economies, Svensson (1997, 1999), Clarida et al. (1998, 2000), Judd and Rudebusch (1998), and Nelson (2001), provide empirical evidence supporting the role of inflation and inflation expectations, whereas, Favero and Rovelli (1999, 2003), Ro- dríguez (2008a, 2008b), emphasize the relevance of variables such as inflation, output gap and employment in the setting of monetary policy interest rates. In open economies, the exchange rate plays an important role in setting interest rates. De Paoli (2009) shows that optimal monetary policy in small open economies that features home bias and incomplete financial markets includes exchange rate smoothing. Consistent with this result, Taylor (1993, 2001) and Ball (1999) find that including the exchange rate in the monetary policy rule may improve its performance in stabilizing long run inflation. Similarly, Leitemo and Södeström (2005) find that the inclusion of the exchange rate in the monetary policy rule reduces the volatility of imported inflation, outperforming a standard Taylor rule. In addition, Lubik and Schofheide (2007) -based in Galí and Monacelli (2005)- estimate Taylor rules using a DSGE model of a small open economy that show that monetary policy interest rates of Australia and New Zealand do not respond to exchange rate movements while the monetary policy of Canada and the United Kingdom do respond to exchange rates movements. In emerging market economies, the role of exchange rates in setting monetary policy rates is more complex. On one hand, emerging economies have less developed financial markets which could contribute to amplify the impact of external shocks on domestic credit conditions; see Batini et al. (2010) and Gopinath (2015). On the other hand, many emerging markets, such as Peru, present financial dollarization which makes currency mismatches more frequent. Currency mismatches can amplify the impact of external shocks on the domestic financial system that, as Rossini et al. (2013) point out, increase the vulnerability of the economy to credit booms and busts associated with capital flows and the exposure of financial stability to exchange rate fluctuations. This could trigger negative balance-sheet effects, which may seriously affect the financial system and the real side of the economy see, Humala and Rodríguez (2010), Filardo et al. (2011), Morón and Winkelried (2005) and Carranza et al. (2003). In response, some emerging countries use the interest rate to smooth exchange rate fluctuations. This behavior is known in the literature as “fear of floating”, see Calvo and Reinhart (2002). Consistent with this narrative, Mohanty and Klau (2005) document that the response of the interest rate to changes in the exchange rate is even stronger than to changes in the inflation rate and the output gap in some small open economies. Instead, other Central Banks in emerging markets tend to rely on a larger number of monetary policy instruments as the use of international reserves and FX intervention, see Filardo et al. (2011). Likewise, Ostry et al. (2010, 2011), Gopinath (2019), Benes et al. (2015) and Adler and Tovar (2011), document that many emerging markets have been able to implement 1 a counter-cyclical monetary policy during the global financial crisis by using a mixture of conventional and non-conventional monetary policy instruments. Additionally, there is evidence that the FX intervention and the interest rate setting are related. For example, Ghosh et al. (2016), Ostry et al. (2011), Benes et al. (2015) and Canzoneri and Cumby (2014) point out that in the case of a capital flow shock, FX inter- ventions reduce the need of adjusting the domestic interest rate to limit a large depreciation of the domestic currency, thus, preserving financial stability by limiting damaging negative balance-sheet effects associated with currency mismatches. Also, according to Céspedes et al. (2017), FX interventions could contribute to isolate domestic credit markets from the volatility and fluctuations of international financial markets by signaling that the exchange rate will remain at levels consistent to a “good equilibrium”. Therefore, it is important to understand to what extent exchange rate fluctuations affect the monetary policy interest rate setting and how FX intervention can be used to isolate interest rate determination from exchange rate fluctuations. This paper tackles these issues using as a case study Peru, a small open economy with dollarization. Our main objective is to assess the role played by the exchange rate and FX interventions in the setting of the interest rate. We do so by estimating a Taylor rule that includes inflation, output gap and exchange rate movements using a New Keynesian DSGE model that follows closely Schmitt-Grohé and Uribe (2017). We test simultaneously whether a model with a Taylor rule that includes exchange rate fluctuations outperforms a model with a Taylor rule that excludes exchange rate movements, similarly to the work of Lubik and Schorfheide (2007)1. To enrich the evaluation method of Lubik and Schorfheide (2007), we extended the model to include sterilized FX intervention under two alternative specifications: one in which the Central Bank follows a explicit rule of FX intervention that lean against external financial conditions as in Faltermeier et al. (2017), and a second specification where FX intervention is modelled as a deviation from the uncovered interest rate parity following Benes et al. (2015). Peru, a small open commodity exporting economy with partial dollarization, offers a relevant case study to evaluate the role of FX intervention and exchange rate in the set- ting of interest rate. Since 2002, the Peruvian Central Bank follows an inflation-targeting (IT) regime that uses the short-term interest rate as its main instrument. Additionally, the Central Bank of Peru (BCRP henceforth) complement the use of interest rates with FX inter- ventions, reserve requirements and international reserves accumulation to reduces the risks stemming from financial dollarization. In the last 20 years, the monetary policy authority has managed to keep the inflation low and stable. In fact, from 2007Q1 to 2008Q2 –before the financial crisis–, during the commodity price boom, the BCRP accumulated the equivalent to 18.5% of GDP of international reserves whereas during the financial crisis (2008Q3-2009Q1), the Central Bank used international reserves for an equivalent of 8.4% of GDP to mitigate exchange rate volatility and to preserve financial stability. We estimate four different versions of the baseline model to assess the role of FX inter- vention in setting of monetary policy. The models differ in the restrictions imposed to the Taylor rule and FX intervention specifications. Model 1 is the less restrictive one, which con- siders a Taylor rule that responds to GDP growth, inflation rate and changes in the exchange 1See Del Negro and Schorfheide (2004) for previous works. 2 rate and considers an active FX intervention rule. The Model 2 contemplates the exchange rate in the Taylor rule and impose the restriction that the Central Bank does not intervene in indirectly in the FX market. The Model 3, imposes the restriction that the Taylor rule does not respond to changes in the exchange rate, but the Central Bank intervenes indirectly in the FX market. Finally, the Model 4 considers a Taylor rule that responds to exchange rate movements and a Central Bank that does not intervene in the FX market. The estimation employs quarterly time series for the period 1997Q1-2017Q4. All models are estimated using two samples. The first one that correspond to the pre-IT period (1997-2003) and the second, that coincide with the period where the IT regime period (2004-2017) has been in place. We rank the different estimations using the log density ratio. The main empirical results show that the model that clearly outperforms all models in terms of marginal log density for the pre-IT and IT periods is the model in which the monetary policy rate does not respond to changes in the nominal exchange rate, and in which the BCRP actively intervenes in the FX market. This result is consistent with the IT plus control risk policy framework of the BCRP. In both samples, we find that the coefficient associated with the response of the interest rate to inflation in the Taylor rule is close to 2, consistent to the Taylor principle that guarantees that inflation is anchored. This result is also consistent with the low average inflation observed in Peru for the sample period. In addition, we find that the coefficient associated of GDP growth in the Taylor rule is greater than 1, which is in line with a counter-cyclical response of monetary policy, particularly in case of aggregate demand shocks. Also, we find that the response of FX intervention to fluctuations in the exchange rate has become stronger during the IT period, a feature that has contributed to reduce the volatility of GDP during this period. Finally, the estimation results also show that FX intervention, characterized by a larger response of FX to exchange rate reduces the volatility of GDP in response to productivity and terms of trade shocks, and that the forecast error variance decomposition shows that productivity and terms of trade shocks are the main source of uncertainty of the Peruvian economy. All our results are robust to the way FX intervention is modelled and to alternative priors of the estimated parameters. The remainder of the paper is organized as follows. Section 2 presents and explains the DSGE model used in the estimation. Section 3 analyzes the main results and the robustness analysis. Section 4 concludes and the Appendix shows the equations of the model. 2 The Model The model is based on Schmitt-Grohé and Uribe (2017). It is a model of a small open economy composed by households, two productive export sectors (commodities and manufacturing), a monetary policy authority and a foreign sector. Additionally, the model includes nominal rigidities a la Calvo on the final consumption goods prices. The Central Bank sets its mone- tary policy using a Taylor rule that responds to inflation, output and changes in the exchange rate. In addition, the Central Bank uses sterilized FX intervention to reduce the volatility of the exchange rate as in Faltermeier et al. (2017). The model captures two key features of the Peruvian economy: the large commodity exporting sector, mainly of minerals, which represent around 10% of its GDP and the frequent intervention of the Central Bank in the FX market, a distinctive feature of its monetary policy. The model considers households that consume final goods, supply labor, own commodity exporting firms, manufacturing firms and 3 final goods firms and save using domestic and foreign assets. The model also considers firms that produce commodities, manufactured and final goods, using labor, capital and imported intermediate goods. 2.1 Households Households demand final goods produced by the manufacturing sector and supply their labor hours (ht) to the manufacturing firms for a salary (wt). Also, households decide how much to consume (ct), save in local currency (dt) and in foreign currency (dft ). They maximize their utility function: Ut (ct, ht) = [ ct − ( ht ω )ω]1−σ 1− σ , (1) where ω is the inverse of the labor supply elasticity and σ is the inverse of the intertemporal substitution of consumption elasticity. The restriction of households is given by: wtht + Γx t + Γt + stp m t d f t−1 ( 1 + r f t−1 ) + dt−1 (1 + rt−1) + uxt k x t−1 + utkt−1 = ptct + pt ( kxt − (1− δ) kxt−1 ) + ptΦx,t ( kxt − kxt−1 ) +pt (kt − (1− δ) kt−1) + ptΦt (kt − kt−1)− stp m t d f t − dt, (2) where st is the nominal exchange rate, pmt is the price of imported goods, pt is the price of the final goods, rt−1 is the interest rate in t − 1 in local currency, r f t−1 is the interest rate faced by domestic agents in foreign currency, Γx t are profits from commodity exporting firms, Γt are profits from domestic manufacturing firms, uxt is the cost of capital that commodities export firms pay to households and ut is the cost of capital the manufacturing firms pay to households. The capital of the commodities export sector is kxt with the following law of movement: kxt = (1− δ) kxt−1 + δixt , (3) where δ is the depreciation rate and ixt is the investment in the commodities export sector. The law of movement of the capital of the manufacturing sector kt is kt = (1− δ) kt−1 + δimt , (4) where imt is the investment in the manufacturing export sector. Furthermore, Φx,t = ϕ 2 (kxt − kxt−1) 2 is the capital adjustment cost of the commodities export sector and Φt = ϕ 2 (kt−kt−1) 2 is the capital adjustment cost of the manufacturing sector, where ϕ captures the intensity of the adjustment costs. Solving the problem of households, the Lagrange multiplier is defined by λt = [ ct − ( ht ω )ω]−σ , (5) 4 and the labor supply of households is given by hω−1 t = wt pt . (6) The Euler equations in local currency and in foreign currency, respectively, are: λt = βEt [ λt+1 ( pt pt+1 ) (1 + rt) ] , (7) λtst = βEt [ λt+1st+1 ( pt pt+1 ) ( 1 + r f t ) ] , (8) where β is the discount factor. 2.2 Commodities Export Firms The firms of this sector are price-takers as commodity prices are determined in international markets. Therefore, domestic commodities exporting firms take these prices (pxt ) as exoge- nous. This sector only uses capital to produce yxt : yxt = Ax t ( kxt−1 )αx , (9) where αx is the parameter associated with capital in the production function and Ax t is the productivity of the commodities exporters defined by an AR(1) equation: ln Ax t Ax = ρAx ln Ax t−1 Ax + ǫA x t , (10) where ρAx is the persistence of the productivity in the commodity export sector, Ax is the steady state of the productivity in the commodity export sector and ǫA x t is the productivity shock in the sector. Commodity exporters firms maximize their profits following: Γx t = stp x t y x t − uxt k x t−1, (11) where pxt is the export price and the cost of capital is given by uxt = αx stp x t pt yxt kxt−1 . (12) Finally, solving the problem of the firm, we have that the Tobin’s Q of the commodities export firms is λt ( 1 + Φ′ x,t ) = βEt ( λt+1Xt+1u x t+1 + 1− δ + Φ′ x,t+1 ) , (13) where Xt+1 = stp x t pt . 5 2.3 Manufacturing Firms The firms use intermediate goods that are produced with a combination of imported goods and labor. These firms follow a production function defined by ywt = At (kt−1) αk mαm t (ht) 1−αk−αm , (14) where kt−1 is the stock of capital of the manufactured goods producer, ht is the number of hours used in this productive sector, αm is the participation of the imported goods in the production function of the manufactured goods and αk is the participation of the capital in the production function. Further, At is the productivity of the manufacturing firms and follows an AR(1) dynamics such that: ln At A = ρA ln At−1 A + ǫAt + λggǫ tot t , (15) where ρA is the persistence of the productivity in the manufacturing export sector firms, A is the steady state of the productivity, ǫAt is the productivity shock, and λgg is the correlation between the terms of trade shock and the productivity shock; see Castillo and Rojas (2014) for empirical evidence that shows a positive correlation between total factor productivity and terms of trade shocks in Peru. The optimal demand of imported goods (mt) used in the production of the intermediate goods is: RERt = αk ywt mt , (16) where RERt is the real exchange rate defined as the relative price of the consumption basket of foreign goods to domestic consumption goods, RERt = stp m t pt . This relative price follows the following dynamics: RERt = (RERt−1) λq ( st (st−1) πt )(1−λq) , (17) where λq is the rigidity of the real exchange rate and πt is the domestic inflation rate. The manufacturing firms maximize their profits such that: Γt=p w t y w t − utkt−1 − stp m t mt − wtht, (18) where pwt is the wholesale price of manufacturing goods, whereas ut is the cost of capital defined by ut pwt = αk ywt kt−1 . Solving the problem of the firm, we define the Tobin’s Q of the manufacturing firms as: 6 λt (1 + Φt) = βEt [ λt+1 ( ut+1 pwt+1 + 1− δ + Φ′ t+1 )] ; (19) and the optimal demand for labor in the manufacturing goods producing sector is: (1− αm − αk) ywt ht = wt pwt . (20) 2.4 Final Goods Producers The manufacturing goods are transformed into intermediate differentiated goods using a one to one technology. Firm z production function is described next: yt(z) = ywt (z). (21) There is a mass of size 1 of intermediate good producers that sell inputs to final good firms. Intermediate goods are transformed into final goods using the following CES aggregator function: yt = [ ∫ 1 0 yt(z) ε−1 ε dz ] ε ε−1 , (22) where, ε > 1 represents the elasticity of substitution among intermediate inputs. Firms producing intermediate inputs set prices optimally taking into account nominal rigidities a la Calvo (1983). The optimal price of consumption goods is therefore determined by the cost of producing optimally one unit of final good and it is given by the following equation: pt = [ ∫ 1 0 pt(z) 1−εdz ] 1 1−ε . (23) Cost minimizing implies that the demand of final good producers for each type of inter- mediate good is given by: yt(z) = ( pt(z) pt ) −ε yt, (24) where yt stands for the aggregate demand of final goods, which is determined by the demand generated from household consumption, investment of exporting and manufacturing goods, and exports and adjustment costs, as follows: yt = (ct + ixt + Φx,t+1 + it + Φt+1) + xNT t . (25) where xNT t is the non-traditional exports. 7 2.5 The Phillips Curve Intermediate goods producers set prices optimally. Each period t intermediate goods produc- ers face an exogenous probability of changing prices given by (1−φ). Following Calvo (1983) and Yun (1996), we assume that this probability is independent of the price level chosen by the firm in previous periods and on the last time the firm changed its price. Also, there is an exogenous indexation rule by which, prices increase automatically by a fraction γ < 1 of the previous period inflation rate. Under these conditions a typical intermediate goods firm chooses an optimal price pot (z) to maximize the present discounted value of its expected flow of profits, given by: Et [ ∞ ∑ k=0 (φβ)k ( λt+k ( pot (z) pt+k ( pt+k−1 pt−2 )γ −mct+k ) ỹt+k (z) ) ] . (26) The inverse of the cumulative inflation rate of the consumer price index is denote by Ψt+k, which is defined as follows: Ψt+k = pt pt+k (27) and by ỹt+k(z) the conditional demand for input z, t+ k periods ahead on pot (z): ỹt+k (z) = ( pot (z) pt π γ t−1Π γ t+k−1 ) −ε Ψ−ε t+kyt+k, (28) and the cumulative inflation is denoted by Πt+k = pt+k−1 pt−1 . (29) The first order condition of the final good producers that determines the optimal price level is given by: Et [ ∞ ∑ k=0 (φβ)k ( λt+k ( pot (z) pt π γ t−1Π γ t+k−1Ψt+k − ε ε− 1 mct+k ) ỹt+k (z) ) ] , (30) where mct is the marginal cost of manufacturing firms. Rearranging the previous condition, we obtain the optimal price, pot (z): pot (z) pt π γ t−1= ε ε− 1 Et [ ∑ ∞ k=0 (φβ) k Ψ−ε t+kΠ −γε t+k−1λt+kmct+kyt+k ] Et [ ∑ ∞ k=0 (φβ) k λt+kyt+kΨ (1−ε) t+k Π γ(1−ε) t+k−1 ] = V N t V D t . (31) Using the law of large numbers, the aggregate price level of final goods can be written as follows: p1−ε t = φp1−ε t−1 + (1− φ) pot (z) 1−ε. (32) 8 Dividing the previous equation by pt we obtain: 1 = φΠε−1 t + (1− φ) pot (z) pt 1−ε . (33) Following Benigno and Woodford (2005), we can write this first order condition of the firms problem recursively using the two auxiliary variables V N t and V D t : φπε−1 t = 1− (1− φ) ( V N t V D t π −γ t−1 )1−ε , (34) where γ is a parameter the controls the the persistence of inflation and V N t and V D t are given by V N t = ΛtYtµmct (z) + βφEt ( πε t+1V N t+1π −εγ t ) , (35) V D t = ΛtYt + βφEt ( π1−ε t+1V D t+1π (1−ε)γ t ) , (36) where Λt is the firm’s stochastic discount factor and µ is the firm’s mark up. These three last equations are the non-linear representation of the Phillips Curve. 2.6 Foreign Sector The equation that determines the balance of payments is obtained by aggregating the con- sumption demand, the investment and the exports of manufactured goods. This gives: xnt pt = yt + stp x t pt yxt − stp m t pt mt − ( ct + ixt + Φx ( kxt+1 − kxt ) + it + Φ(kt+1 − kt) ) , (37) where xnt represents the net exports. The net asset position is: stdt pt + st pt pt−1 st−1 ( st−1FXt−1 pt−1 ) = st pt pt−1 st−1 st−1dt−1 pt−1 (1 + rt−1) + stFXt pt − xnt pt , (38) where FX is the foreign exchange intervention of the Central Bank. The non-traditional exports are: xNT t = (RERt) ξ C∗ t , (39) where ξ is the elasticity of the non-traditional exports, and C∗ t is the foreign demand of goods. The terms of trade are defined as follows: tott = pxt pmt , (40) 9 and the dynamic of the terms of trade is an AR(1) equation: ln tott tot = ρtot ln tott−1 tot + ǫtott , (41) where ρtot is the persistence of the terms of trade, tot is the steady state of the terms of trade and ǫtott is a shock of terms of trade. The relative price of exported goods are: stp x t pt = xt = xλx t−1 [ stp x t st−1p x t−1πt ](1−λx) , where λx is the rigidity of the relative price of exported goods. We follow Adolfson et at. (2008) in the inclusion of the expected change in the exchange rate Et ( st+1 st ) in the modified uncovered interest rate parity equation equation. This is based on the observation that risk premium are strongly negatively correlated with the expected change in the exchange rate, see Duarte and Stockman (2005) and Fama (1984). This pattern is often referred to as the “forward premium puzzle”. In this way, we can capture the hump- shaped response of the real exchange rate after a shock to monetary policy, which is commonly found in estimated VARs, see Eichenbaum and Evans (1995) and Faust and Rogers (2003). The modify uncovered interest rate parity that captures this empirical feature implies a gradual and less than complete response of the exchange rate to changes in the spread between domestic and foreign interest rates, which we parameterize as follows: ( st st−1 )λs = Et ( st+1 st )( 1 + ifr 1 + rt )1−λs . (42) The country risk premium is given by r f t = r∗t + ψ exp( dt d − 1)− λxi log( tott tot ) + ε rf t , (43) where r∗t is the international interest rate, ψ is the risk premium debt elasticity, λxi is the risk premium terms of trade elasticity and ǫrf is country risk premium shock that evolves following a law of movement given by ε rf t = ρrfε rf t−1 + ǫt,RR, (44) where ρrf is the persistence of the risk premium shock and ǫt,RR represents the innovation of the risk premium shock. 2.7 Gross Domestic Product and Total Investment We define the GDP as yGDP t = pyt + pxyxt , (45) 10 where pt is the price of manufactured goods in steady state, px is the export price of com- modities in steady state. The total investment is given by it = ixt + imt . (46) 2.8 Monetary Policy We assume that the Central Bank adjusts its interest rate in response to changes in inflation rate, movements in the nominal exchange rate and the fluctuations in the output gap2 such that: (1 + rt) = ( 1 β )1−ρR (1 + rt−1) ρR (Πt) (1−ρR)φπ yGDP (1−ρR)φy t ( st st−1 )(1−ρR)φe . (47) The coefficient φπ > 1 measures the response of the interest rate to changes in the inflation rate, φe > 0 is the quantifies the response of the interest rates to changes in the nominal exchange rate and φy > 0 measures the response of the interest rate to changes in the output. The persistence of the interest rate is captured by 0 < ρR < 1. One of the purposes of this study is to evaluate the response of the monetary policy rate to exchange rate movements by analyzing if φe > 0. Additionally, we consider that the Central Bank intervenes in the FX market following a lean against the wind type of rule for FX intervention: FXt FX = ( st st−1 ) −δfx exp(ǫfxt ), (48) where FX represents the steady-state value of foreign exchange reserves, and δfx captures the extent in which the Central Bank responds to exchange rate movements. FX intervention affects the net foreign asset position of the economy, which in turn has a direct impact on the country risk premium, and through the modified UIP on the exchange rate. 3 Empirical Results 3.1 Choice of Priors Parameters associated to the steady-state of the model are calibrated to the Peruvian econ- omy using quarterly data (see Table 1). The remaining set of parameters were estimated using Bayesian methods. Most of the priors are obtained from the BCRP macroeconomic quarterly model, see Salas (2011) and Vega et al. (2009) are in line with the literature on small open economies exposed to commodity prices shocks. For standard deviations of struc- tural shocks and for parameters associated with monetary policy we use a Inverse Gamma distribution as prior. In addition, for persistence parameters we use Beta distributions as priors. 2Defined as the difference between the real GDP and the trend of the GDP. 11 3.2 Data Description The observable variables used in the estimation are real gross domestic product (GDP), total exports, real private consumption, consumer price index (CPI), monetary policy rate, nominal exchange rate and terms of trade. The time series of monetary policy interest rate is constructed using the average interbank rate in domestic currency from 1997 to 2003 and, from 2004 onwards, we use the BCRP’s monetary policy rate. GDP, consumption, exports and CPI inflation are seasonally adjusted and introduced in the model as the first log difference in deviation from its mean. The data is at quarterly frequency from 1997Q1 to 2017Q4 and it was obtained from the statistics published at the webpage of the BCRP. 3.3 Estimation Results We estimate four different versions of the baseline model that differ in the restrictions imposed to the Taylor rule and whether or not the FX intervention is active (see Table 2). Model 1 is the less restrictive one, which considers a Taylor rule that responds to GDP growth, inflation rate, changes in the exchange rate (φe > 0) and an active FX intervention rule (δfx > 0). The Model 2 contemplates the exchange rate in the Taylor rule (φe > 0) and impose the restriction that the Central Bank does not intervene in the FX market (δfx = 0). The Model 3, imposes the restriction that the Taylor rule does not respond to changes in the exchange rate (φe = 0), but the Central Bank intervenes in the FX market (δfx > 0). Finally, the Model 4 considers a Taylor rule that does not respond to exchange rate movements (φe = 0) and a Central Bank that does not intervene in the FX market (δfx = 0). We split the sample into two subsamples. The first one, from 1997 to 2003, which corresponds to the pre-IT regime. The second sample, from 2004 to 2017, corresponds to the period in which Peru follows an IT regime. Table 3 panel (a) and (b) present the marginal log density and the Bayes Factor of the four models in both subsamples. We use the marginal log density to rank the estimated models. First, we find that for the pre-IT and for the IT regimes, the model that clearly performs better in terms of marginal log density is Model 3. This result is consistent with IT plus control risk framework that the BCRP uses to implement its monetary policy. In this framework, the BCRP uses the short-term interest rate to anchor inflation expectations and FX interventions and reserve requirements to limit risks associated to financial dollarization3. In the pre-IT period, Model 2 is the version of the model with the lowest marginal log density. This model considers that the BCRP does not intervene in the FX market (δfx = 0). Instead, the BCRP uses the interest rate to respond to exchange rate fluctuations, which further support the importance of FX intervention to properly characterize monetary policy in Peru. In the IT period, Model 4 is the version with the lowest marginal log density which considers that the BCRP does no include changes in the exchange rate in the monetary policy rule (φe = 0) and does not intervene in the FX market (δfx = 0). Figure 1 shows the identification strength-plot. The bar charts depict the identification strength of the parameters based on the Fisher information matrix normalized by either the 3 See Rossini and Santos (2015) for a detailed description of the BCRP monetary policy framework. 12 parameter at the prior mean (blue bars) or by the standard deviation at the prior mean (orange bars), see Ratto and Iskrev (2011). Intuitively, the bars represent the normalized curvature of the log likelihood function at the prior mean in the direction of the parameter. Note that the graphs generally use a log-scale except for parameters that are unidentified, which are shown with a bar length of exactly 0 as the likelihood function is flat in this direction. In contrast, the larger the value, the stronger is the identification. The parameters are ordered in the direction of increasing identification strength relative to the parameter value. As this Figure shows, all the estimated parameters are identified in the Jacobian of mean and spectrum, see Qu and Tkachenko (2012), and according to the Jacobian of first two moments, see Iskrev (2010). Tables 5 and 7 show the posterior mode of the estimated parameters of Models 3 and 4. We compare these two models as Model 3 is the best model in terms of log density and Model 4 is the most restrictive model. In this way, we can illustrate more precisely the role that FX intervention plays in the propagation mechanism of macroeconomic shocks. A second interesting result is that, in both subsamples, the size of the response of the interest rate to inflation (φπ) is close to 2. This value is consistent with an interest rate rule that satisfies the Taylor principle, which guarantees that the inflation rate is anchored to the Central Bank inflation target, a finding that is consistent with the performance of inflation in Peru since the adoption of the IT framework4. A third result is that the response of interest rate to the GDP gap (φy) is greater than 1 which is consistent with a counter-cyclical response of the monetary policy, particularly in case of aggregate demand shocks. A fourth result shows that the response of FX interventions to fluctuations in the ex- change rate (δfx) has become stronger during the IT period. In fact, in the IT period, this parameter is almost nine times larger than in the pre-IT period. This finding is consistent with a more active participation of the BCRP in the FX market during the IT regime or a better signalling of the lean against the wind type of FX intervention. Since 2004, Peru faced an increasing volatility of capital flows as gradually gained access to the global financial mar- kets and it became more active in the use of macro-prudential tools, including a lean against the wind type FX intervention strategy. During 2007Q1-2008Q2, the BCRP bought foreign currency for an amount equivalent to 18.5% of GDP to smooth the impact on the domestic economy of abundant capital inflows, whereas during the financial crisis (2008Q3-2009Q1) the Central Bank sold foreign currency for an amount equivalent to 8.4% of GDP, a response that contributed to limit the impact of the sudden stop of capital flows that the global finan- cial generated on domestic credit conditions5. It is important to highlight that the estimated FX rule is coherent with a floating exchange rate regime where the BCRP intervenes aiming at reducing the volatility of the exchange rate. Since Peru adopted a floating exchange regime in 1990 (Vega and Lahura, 2012) the BCRP has continued using FX interventions to reduce the risk associated to financial dollarization and the spillover effects of the global financial cycle into domestic credit conditions6. 4Average inflation in Peru since 2001 to 2017 is 2.6%, within its inflation target of 1%-3%. 5See Arena and Tuesta (1999), Humala and Rodríguez (2010) and Rossini and Vega (2008) for an assessment of the FX intervention policy of the BCRP. 6See Armas et al. (2001) and Rossini (2002) for a detailed discussion of the transition of Peru to the IT framework. 13 3.4 Bayesian Impulse Response Functions We present the Bayesian impulse response functions (IRFs) of the best estimated version of the baseline model (Model 3) in comparison to the most restrictive version of the baseline model (Model 4) for four structural shocks: productivity of the manufacturing sector, cost- push, risk premium and terms of trade shocks. In this way we can highlight the effects of FX intervention in the dynamics of the BCRP policy response. Figure 2 shows the response of a selected set of variables to an increase in productivity of the manufacturing sector. Panel (a) shows the IRFs for the pre-IT regime, whereas panel (b) shows the IRFs during the IT regime period. As panel (a) shows, an increase in produc- tivity generates a persistent increase in GDP, consumption, and investment. In contrast, the inflation rate decreases as marginal costs falls and trade balance improves, which triggers a reduction in the net foreign debt position and a lower country risk premium that appreciate the domestic currency. As the inflation weight on the Taylor rule is larger than the weight of the GDP growth, the fall in the inflation rate more than offset the effect of the increase in the GDP growth on the Taylor rule. Consequently, the BCRP responds by cutting its policy rate and purchasing in the FX market. As a result, in Model 3, the nominal exchange rate falls less than the inflation rate and the appreciation of the domestic currency is mitigated, which generates a real depreciation of domestic currency. Figure 2 panel (b) shows the response of the economy during the IT regime, which are qualitatively similar to the ones during the pre-IT regime but are quantitatively different. The GDP and consumption respond to a lesser extent to a productivity shock of the same magnitude than in the pre-IT period. Also, the nominal exchange rate falls less and the real exchange rate increases more. A key difference between the two periods is the coefficient associated with the response of the FX intervention to changes in exchange rate (δfx). During the pre-IT period, this coefficient is less than 1, whereas during the IT regime period for the same model is 5.3. This different response implies that the nominal exchange rate and the inflation rate fall less during the IT period, which induces the Central Bank to cut to a lesser extent its interest rate. The smaller cut in the policy rate, in turn, generates a smaller increase of GDP, aggregate consumption and investment in comparison with the ones observed during the pre-IT period. Overall, these results shows that the policy response of the BCRP generates lower GDP and inflation volatility in response to productivity shocks during the IT period. Comparing the response of the economy to a productivity shock in Model 3 to those in Model 4, it is clear that the key difference is the response of the nominal and the real exchange rate. In Model 4 (δfx = 0) a positive productivity shock generates a larger nominal appreciation and a smaller real depreciation of the domestic currency whereas in the Model 3 (δfx > 0), this shock generates a larger real depreciation of the domestic currency, a smaller reduction in inflation and interest rate, which generates a smaller expansion in consumption, and GDP in comparison to Model 4. From this results it is clear that the use of FX interven- tion by BCRP reduces the volatility of output and the inflation rate in response to domestic productivity shocks. Figure 3 panel (a), shows the response of the economy to a persistent cost-push shock during the pre-IT regime. The inflation rate increases sharply and the BCRP rises its interest 14 rate to maintain the inflation expectations anchored. Therefore, consumption, investment and GDP fall. As output declines, external net borrowing increases and consequently the country risk premium rises pushing the exchange rate upwards. FX intervention prevents a large nominal depreciation, which together with higher inflation result in a fall in the real exchange rate, which generates a larger trade deficit. During the IT regime period (see Figure 3 panel (b)), the Central Bank uses more strongly FX interventions, which prevents a large depreciation of the domestic currency. In the short-term, a smaller exchange rate pass-through to inflation reduces the need of the Central Bank to increase the nominal interest rate. In fact, the Central Bank cuts its policy rate instead of increasing it. Overall, in the IT period the fall in consumption and GDP are smaller than during the pre-IT regime. In this way, the policy response of the Central Bank contributes to reduce the volatility of output and consumption in response to a cost-push shock. Figure 4 panel (a) shows the IRFs of an increase in the risk premium during the pre-IT regime period. The nominal and the real exchange rate rises, as a result, the inflation rate jumps. The BCRP increases its interest rate to maintain the inflation rate anchored and GDP falls. The FX intervention of the BCRP smooths the depreciation of the exchange rate, therefore, the nominal and real exchange rate increase less than in Model 4. During pre-IT regime, consumption and investment do not fall in the short-term, as the depreciation of the real exchange rate boosts net exports, which compensates the negative impact of higher interest rate in consumption and investment. Figure 4 panel (b) shows the IRFs to an unexpected increase in risk premium during the IT period. This shock negatively impact the GDP and consumption. Also generates a nominal and real depreciation of the domestic currency. The inflation rate increases in response to the higher exchange rate. As a result, the Central Bank rises its policy rate. This increase in the policy rate is larger as inflation remains elevated for a longer period of time. Figure 5 panel (a), shows the IRFs to a positive terms of trade shock during the pre-IT regime period. This shock generates a fall in the nominal exchange rate and a trade surplus. The BCRP accumulates international reserves through FX purchases and inflation rate falls in response to a lower nominal exchange rate. However, due to BCRP’s FX intervention, both the fall in the inflation rate and the appreciation of the nominal exchange rate are mitigated. As a result, the real exchange rate increases, which boosts net exports. In addition, higher terms of trade increase the profitability of the commodity sector, which in turn increases investment and generates a positive wealth effect that expands the aggregate consumption. The fall in the inflation rate more than offsets the impact of higher GDP growth rate on the Taylor rule and induces to the BCRP to cut its policy rate. Figure 5 panel (b) shows the response of the economy to a positive terms of trade shock during the IT regime period. The IRFs show that the GDP, investment and consumption expand but to a lesser extent than during the pre-IT period. The nominal exchange rate and the inflation rate fall less than during the pre-IT period, whereas the real exchange rate depreciates more. A key difference of the response of the economy during the IT period is the stronger FX intervention and the less intense response of the nominal interest rate to GDP. A more intensive use of FX intervention limits the appreciation of the nominal exchange rate 15 which, in turn, induces a smaller fall in inflation and a smaller cut of the interest rate. The combination of an active Taylor rule and a more intense FX intervention, generates a lower volatility in output, investment and aggregate consumption in response to terms of trade shocks. 3.5 Forecast Error Variance Decomposition (FEVD) In Figure 6 panel (a) and (b) we present the FEVD for the GDP and in Figure 7 panel (a) and (b) the FEVD for the interest rate. A first interesting result is that the productivity shock is the most important determinant for the FEVD of GDP, during the pre-IT and IT periods. This shock explains about 40% of the variation of the FEVD of GDP in the long term in both samples. Second, the participation of terms of trade shocks has increased during the IT period, from around 10% pre-IT to almost 20% during the IT period and is one the most important sources of volatility for GDP. In the case of the FEVD of the interest rate, the productivity shock explains 75% of the FEVD in the pre-IT period and around 65% in the IT period. This is consistent with Humala and Rodríguez (2009) who find the the natural interest rate and the observed interest rate are highly dependent on the productivity. Likewise, we find that the contribution of the terms of trade shock has almost double from the pre-IT to the post adoption IT period of the interest rate FEVD. This result is consistent with the findings of Schmitt-Grohé and Uribe (2017) which shows that the terms of trade shock are an important determinant of GDP in the Peruvian economy. 4 Conclusions We estimate a quarterly DSGE model for a small open economy based on Schmitt-Grohé and Uribe (2017) for the Peruvian economy. The model considers households, two produc- tive sectors, a monetary policy authority and a foreign sector. We include a Taylor rule that responds to inflation, output gap and changes in the nominal exchange rate. Additionally, we enriched the model by incorporating a explicit sterilized FX intervention rule, as in Fal- termeier et al. (2017), that the Central Bank uses to reduce the volatility of the exchange rate. We estimate four different versions of the baseline model with different restrictions imposed to the Taylor rule and to FX intervention rule. We also estimate the models for two subsamples: pre-IT and IT regime periods. We find that the best model in terms of log marginal density for the two subsamples is the Model 3, which considers that the monetary policy rate does not respond to changes in the nominal exchange rate (φe = 0), but the BCRP intervenes in the FX market (δfx > 0). We also find that the interest rate response to the inflation rate is close to 2 and the response to GDP gap is greater than 1. These results are consistent with the monetary policy framework that the BCRP has been following during the period of study. Furthermore, we find that the response of the FX intervention to changes in the nominal exchange rate (δfx) in the IT period is almost nine times bigger than in the pre-IT period. This is also coherent with the BCRP monetary and exchange operations and the adoption of the floating exchange regime. 16 The estimated IRFs show that BCRP’s FX interventions are effective reducing the ex- change rate volatility and, therefore, the inflation rate. This support the literature branch that in emerging economies, like Peru, FX interventions and IT regime contribute to the same goal of keeping the inflation rate within the inflation target. 17 Appendix A Linear Model A.1 Households Lagrange multiplier: λt = −σ ( c c− ( h ω )ω ) ct + σ ( h ( h ω )ω c− ( h ω )ω ) ht (A.1) Labor supply: (ω − 1)ht = w (A.2) Euler equation: λt = λt+1 + rt − πt+1 (A.3) A.2 Commodities Export Firms Production function of the commodities export firms: yxt = ax + αxk x t−1 (A.4) Law of movement of capital: kxt = (1− δ) kxt−1 + δixt (A.5) Productivity law of movement: axt = ρAxaxt−1 + εAx t (A.6) Cost of capital: uxt = xt + y − kxt−1 (A.7) Tobin’s Q: λt + Φx,t = λt+1 + ux (ux + 1− δ) ( uxt+1 ) + Φx,t (ux + 1− δ) (A.8) A-1 A.3 Manufacturing Firms Production function: yωt = at + αkkt−1 + αmmt + (1− αm − αk) ht (A.9) Law of movement of capital: kt = (1− δ) kt−1 + δimt (A.10) Productivity law of movement: at = ρaat−1 + εat + λggε tot t (A.11) Demand for imported goods: mt = yt − RERt+mct (A.12) Dynamic of relative prices: RERt = λqRERt−1 + (1− λq) (st − πt) (A.13) Cost of capital: ut = yt − kt−1 + αk (A.14) Demand of labor: yωt − ht + αk = w (A.15) Tobin’s Q: λt + Φt = λt+1 + u (u+ 1− δ) (ut+1) + Φt+1 (u+ 1− δ) (A.16) A.4 Final Good Producer The aggregate demand for final goods is given by: yt = ( c y ct + i y it ) + Φ(kt − kt−1) + Φx t ( kxt − kxt−1 ) + x y xNT t , (A.17) A.5 Phillips Curve πt = βπt+1 + (1− βε) (1− ε) ε mct + γ (1− ε) ε πt−1 + µ (A.18) where the firm’s mark up: µt = ρuµt−1 + εinv (A.19) A-2 A.6 Foreign Sector Net exports: xntxn = yty + pxyxt (xt + yxt )−mRERmt − ctc− iti− Φt − Φx,t+1 (A.20) Risk premium: i f t = r∗ + ψdt − λxitott + ε rf t (A.21) Risk premium shock: ε rf t = ρrfε rf t−1 + εRR t (A.22) Net asset position: ( dRER dRER + xn ) (dt +RERt − FXt) + ( XNtxn dRER + xn ) xnt = dt−1 + FXt−1 + i f t−1 +RERt (A.23) Non-traditional exports: xNT t = ξRERt, (A.24) Relative price of exports: xt = λxxt−1 + (1− λx) ( dst + pxt − pxt−1 − πt ) (A.25) Definition of the terms of trade: tott = pxt (A.26) Law of movement of the terms of trade: tott = ρtottott−1 + εtott (A.27) A.7 Gross Domestic Product yGDP t = ( pxyx pxyx + yω ) yxt + ( yω pxyx + yω ) yωt (A.28) A.8 Total Investment it = ( ix ix + im ) ixt + ( 1− ix ix + im ) imt (A.29) A-3 A.9 Monetary Policy Monetary policy rule: rt = ρrrt−1 + (1− ρr) (φππt+1 + φyyt + φest) (A.30) Modified uncovered interest rate parity: λsst = st+1 + (1− λs) ( i f t − rt ) (A.31) Intervention rule: FX = −λfxst + ε fx t (A.32) A-4 References [1] Adler, G. and Tovar, C. 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Journal of Monetary Economics 37(2), 345-370. doi: 10.1016/S0304-3932(96)90040-9 R-4 http://10.1111/iere.12263 http://10.2139/ssrn.1334 http://10.1111/1467-9442.00160 http://10.1016/0167-2231(93)90009-L http://10.1257/aer.91.2.263 http://10.1016/S0304-3932(96)90040-9 Table 1: Priors Distributions Parameters Description Distribution Prior Mean Prior Std. Dev. ρrf Persistence of the shock of the country risk premium Inverse Gamma 0.65 0.10 δfx Extent in which the Central Bank responds to exchange rate movements through FX interventions Inverse Gamma 0.70 2.00 ρR Persistence of the interest rate Inverse Gamma 0.30 0.01 Φx Capital adjustment cost function of the commodities export sector Inverse Gamma 0.80 0.20 Φ Capital adjustment cost function of the manufacturing export sector Inverse Gamma 1.50 0.15 ψ Risk premium and debt elasticity Beta 0.30 0.10 ω Inverse of the labor supply elasticity Inverse Gamma 2.30 0.20 φe Response of the monetary policy rate to changes in the exchange rate Inverse Gamma 0.70 0.15 φy Response of the monetary policy rate to changes in the GDP Inverse Gamma 1.30 0.10 φπ Response of the monetary policy rate to inflation Inverse Gamma 2.20 0.01 λq Persistence of the real exchange rate Beta 0.36 0.10 λs Degree of exchange rate stickiness Beta 0.60 0.20 λgg Correlation between terms of trade shocks and productivity of the manufacturing sector shocks Beta 0.70 0.10 ρtot Persistence of the terms of trade Beta 0.95 0.01 ρAx Persistence of the productivity in the commodity export sector Beta 0.83 0.01 ρA Persistence of the productivity in the manufacturing export sector Beta 0.80 0.10 σεRR Standard deviation of the premium risk shock Inverse Gamma 0.01 2.00 σεtot Standard deviation of the terms of trade shock Inverse Gamma 0.30 2.00 σεAx Standard deviation of the productivity in the commodity export sector shock Inverse Gamma 0.01 2.00 σεY Y Standard deviation of the measurement error of the GDP Inverse Gamma 0.01 2.00 σεAA Standard deviation of the productivity in the manufacturing export sector shock Inverse Gamma 0.10 2.00 σεINV Standard deviation of the margin shock Inverse Gamma 0.01 2.00 σεCC Standard deviation of the measurement error of consumption Inverse Gamma 0.01 2.00 σεY Yx Standard deviation of the measurement error of the production in the commodity export sector Inverse Gamma 0.01 2.00 T-1 Table 2: Description of Model Versions Model Description 1 The Central Bank considers changes in the nominal exchange rate in the monetary policy rule and intervenes in the FX market φe > 0 δfx > 0 2 The Central Bank considers changes in the nominal exchange rate in the monetary policy rule and does not intervene in the FX market φe > 0 δfx = 0 3 The Central Bank does not consider changes in the nominal exchange rate in the monetary policy rule and intervenes in the FX market φe = 0 δfx > 0 4 The Central Bank does not consider changes in the nominal exchange rate in the monetary policy rule and does not intervene in the FX market φe = 0 δfx = 0 T-2 Table 3: Estimated Model Versions (a) Pre-IT period: 1997Q1-2003Q4 Model 1 2 3 4 Log marginal density 280.87 269.90 284.25 271.08 Bayes Factor 2E+04 3E-01 5E+05 1E+00 (b) Post IT period: 2004Q1-2017Q4 Model 1 2 3 4 Log marginal density 655.25 633.05 662.34 629.73 Bayes Factor 1E+11 3E+01 1E+14 1E+00 Note: The prior density over the model is the same for the four model specifications. The Bayes Factor is calculated against Model 4. T-3 Table 4: Posterior Estimation, Pre-IT Period: 1997Q1-2003Q4 Parameters Model 1 Model 2 Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. ρrf 0.68 0.51 0.83 0.10 0.61 0.50 0.74 0.07 δfx 1.04 0.53 1.59 0.33 - - - - ρR 0.30 0.29 0.32 0.01 0.30 0.29 0.32 0.01 Φx 0.80 0.49 1.10 0.24 0.71 0.48 0.95 0.16 Φ 1.48 1.24 1.71 0.15 1.41 1.20 1.62 0.13 ψ 0.28 0.09 0.43 0.10 0.09 0.01 0.19 0.06 ω 2.26 1.95 2.57 0.19 2.12 1.85 2.38 0.17 φe 0.62 0.44 0.78 0.11 0.59 0.42 0.76 0.11 φy 1.57 1.36 1.78 0.13 1.58 1.36 1.80 0.14 λq 0.38 0.21 0.55 0.11 0.38 0.22 0.54 0.10 λs 0.77 0.65 0.88 0.07 0.44 0.24 0.64 0.12 λgg 0.58 0.39 0.77 0.12 0.55 0.35 0.74 0.12 ρAx 0.83 0.69 0.98 0.09 0.79 0.63 0.96 0.11 ρA 0.85 0.82 0.89 0.02 0.90 0.87 0.94 0.02 φπ 1.97 1.86 2.10 0.07 1.96 1.82 2.09 0.08 σεRR 0.01 0.00 0.02 0.01 0.00 0.00 0.01 0.00 σεTOT 0.07 0.05 0.08 0.01 0.07 0.05 0.08 0.01 σεAx 0.02 0.00 0.03 0.01 0.01 0.00 0.01 0.00 σεY Y 0.03 0.02 0.04 0.00 0.03 0.02 0.04 0.00 σεAA 0.08 0.06 0.10 0.01 0.07 0.05 0.09 0.01 σεINV 0.14 0.10 0.17 0.02 0.13 0.10 0.15 0.02 σεCC 0.02 0.02 0.03 0.00 0.03 0.02 0.03 0.00 σεY Yx 0.05 0.04 0.07 0.01 0.05 0.04 0.06 0.01 T -4 Table 5: Posterior Estimation, Pre-IT Period: 1997Q1-2003Q4 Parameters Model 3 Model 4 Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. ρrf 0.65 0.49 0.81 0.10 0.59 0.46 0.72 0.08 δfx 0.60 0.33 0.85 0.17 - - - - ρR 0.30 0.28 0.32 0.01 0.30 0.29 0.32 0.01 Φx 0.76 0.53 1.02 0.16 0.75 0.46 1.02 0.19 Φ 1.45 1.24 1.68 0.14 1.43 1.22 1.65 0.13 ψ 0.24 0.10 0.39 0.09 0.15 0.03 0.27 0.07 ω 2.30 1.98 2.61 0.20 2.24 1.93 2.55 0.19 φy 1.54 1.34 1.74 0.12 1.54 1.34 1.74 0.13 φe - - - - - - - - λq 0.37 0.20 0.54 0.10 0.38 0.21 0.55 0.10 λs 0.61 0.47 0.74 0.08 0.24 0.08 0.41 0.10 λgg 0.54 0.35 0.72 0.11 0.49 0.32 0.68 0.11 ρAx 0.81 0.68 0.97 0.09 0.76 0.57 0.94 0.11 ρA 0.87 0.83 0.90 0.02 0.92 0.91 0.94 0.01 φπ 1.98 1.86 2.10 0.08 2.00 1.87 2.12 0.08 σεRR 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 σεTOT 0.07 0.05 0.08 0.01 0.07 0.05 0.08 0.01 σεAx 0.01 0.00 0.02 0.00 0.00 0.00 0.01 0.00 σεY Y 0.03 0.02 0.04 0.00 0.03 0.02 0.04 0.00 σεAA 0.07 0.05 0.08 0.01 0.05 0.04 0.07 0.01 σεINV 0.13 0.10 0.15 0.02 0.12 0.09 0.14 0.02 σεCC 0.02 0.02 0.02 0.00 0.02 0.02 0.03 0.00 σεY Yx 0.05 0.04 0.06 0.01 0.05 0.04 0.06 0.01 T -5 Table 6: Posterior Estimation, IT Period: 2004Q1-2017Q4 Parameters Model 1 Model 2 Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. ρrf 0.63 0.50 0.75 0.08 0.70 0.53 0.87 0.10 δfx 14.53 1.76 29.33 11.15 - - - - ρR 0.30 0.29 0.32 0.01 0.30 0.28 0.32 0.01 Φx 1.13 0.51 1.74 0.56 1.02 0.58 1.53 0.32 Φ 1.56 1.29 1.82 0.17 1.49 1.26 1.70 0.14 ψ 0.31 0.14 0.46 0.10 0.03 0.01 0.07 0.02 φe 0.54 0.41 0.68 0.08 0.61 0.47 0.74 0.09 φy 1.68 1.44 1.91 0.14 1.60 1.40 1.82 0.13 λq 0.33 0.20 0.48 0.09 0.36 0.18 0.52 0.10 λs 0.77 0.55 0.91 0.13 0.89 0.84 0.94 0.03 ρAx 0.83 0.68 0.97 0.10 0.87 0.76 0.98 0.07 ρA 0.75 0.71 0.79 0.03 0.74 0.71 0.78 0.02 φπ 1.97 1.84 2.10 0.08 1.99 1.88 2.12 0.07 σεRR 0.13 0.02 0.25 0.09 0.02 0.01 0.03 0.01 σεTOT 0.12 0.10 0.13 0.01 0.12 0.10 0.13 0.01 σεAx 0.01 0.00 0.02 0.01 0.02 0.00 0.02 0.01 σεY Y 0.02 0.01 0.02 0.00 0.02 0.01 0.02 0.00 σεAA 0.09 0.07 0.10 0.01 0.09 0.07 0.10 0.01 σεINV 0.13 0.11 0.15 0.01 0.13 0.11 0.15 0.01 σεCC 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.00 σεY Yx 0.03 0.03 0.04 0.00 0.03 0.03 0.04 0.00 T -6 Table 7: Posterior Estimation, IT Period: 2004Q1-2017Q4 Parameters Model 3 Model 4 Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. Posterior Mean Lower Credibility Band Upper Credibility Band Posterior Std. Dev. ρrf 0.65 0.51 0.80 0.09 0.63 0.50 0.77 0.08 δfx 5.30 1.99 9.13 2.38 - - - - ρR 0.30 0.29 0.32 0.01 0.30 0.28 0.31 0.01 Φx 1.02 0.56 1.53 0.33 1.11 0.55 1.68 0.38 Φ 1.53 1.28 1.80 0.16 1.52 1.29 1.78 0.15 ψ 0.23 0.09 0.35 0.09 0.02 0.00 0.03 0.01 φe - - - - - - - - φy 1.64 1.42 1.86 0.14 1.56 1.37 1.77 0.13 λq 0.35 0.19 0.51 0.10 0.38 0.21 0.56 0.11 λs 0.85 0.79 0.92 0.04 0.92 0.88 0.96 0.03 ρAx 0.82 0.68 0.97 0.09 0.91 0.84 0.99 0.05 ρA 0.74 0.70 0.79 0.03 0.73 0.69 0.76 0.02 φπ 1.94 1.82 2.06 0.07 2.00 1.89 2.11 0.07 σεRR 0.05 0.02 0.08 0.02 0.03 0.01 0.04 0.01 σεTOT 0.12 0.10 0.14 0.01 0.12 0.10 0.14 0.01 σεAx 0.01 0.00 0.02 0.01 0.02 0.01 0.03 0.00 σεY Y 0.02 0.01 0.02 0.00 0.02 0.01 0.02 0.00 σεAA 0.08 0.07 0.10 0.01 0.09 0.07 0.10 0.01 σεINV 0.12 0.10 0.15 0.01 0.13 0.10 0.15 0.01 σεCC 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.00 σεY Yx 0.03 0.03 0.04 0.00 0.04 0.03 0.04 0.00 T -7 (a) Pre-IT Period: 1997Q1-2003Q4 (b) IT Period: 2004Q1-2017Q4 Figure 1: Identification Strength F-1 (a) Pre-IT Period: 1997Q1-2003Q4 (b) IT Period: 2004Q1-2017Q4 Figure 2: Impulse Response Functions: Manufactuing Sector Productivity Shock. Blue and Orange shaded regions indicate the 90% HPD interval of Model 3 and 4, respectively. F-2 (a) Pre-IT Period: 1997Q1-2003Q4 (b) IT Period: 2004Q1-2017Q4 Figure 3: Impulse Response Functions: Cost-Push Shock. Blue and Orange shaded regions indicate the 90% HPD interval of Model 3 and 4, respectively. F-3 (a) Pre-IT Period: 1997Q1-2003Q4 (b) IT Period: 2004Q1-2017Q4 Figure 4: Impulse Response Functions: Risk Premium Shock. Blue and Orange shaded regions indicate the 90% HPD interval of Model 3 and 4, respectively. F-4 (a) Pre-IT Period: 1997Q1-2003Q4 (b) IT Period: 2004Q1-2017Q4 Figure 5: Impulse Response Functions: Terms of Trade Shock. Blue and Orange shaded regions indicate the 90% HPD interval of Model 3 and 4, respectively. F-5 (a) Pre-IT Period: 1997Q1-2003Q4 (b) IT Period: 2004Q1-2017Q4 Figure 6: Forecast Error Variance Decomposition for GDP F-6 (a) Pre IT-Period: 1997Q1-2003Q4 (b) IT Period: 2004Q1-2017Q4 Figure 7: Forecast Error Variance Decomposition for Interest Rate F-7 ÚLTIMAS PUBLICACIONES DE LOS PROFESORES DEL DEPARTAMENTO DE ECONOMÍA  Libros Alfredo Dammert Lira 2021 Economía minera. Lima, Fondo Editorial PUCP. Adolfo Figueroa 2021 The Quality of Society, Volume II – Essays on the Unified Theory of Capitalism. New York, Palgrave Macmillan. Carlos Contreras Carranza (Editor) 2021 La Economía como Ciencia Social en el Perú. Cincuenta años de estudios económicos en la Pontificia Universidad Católica del Perú. Lima, Departamento de Economía PUCP. José Carlos Orihuela y César Contreras 2021 Amazonía en cifras: Recursos naturales, cambio climático y desigualdades. Lima, OXFAM. Alan Fairlie 2021 Hacia una estrategia de desarrollo sostenible para el Perú del Bicentenario. Arequipa, Editorial UNSA. Waldo Mendoza e Yuliño Anastacio 2021 La historia fiscal del Perú: 1980-2020. Colapso, estabilización, consolidación y el golpe de la COVID-19. Lima, Fondo Editorial PUCP. Cecilia Garavito 2020 Microeconomía: Consumidores, productores y estructuras de mercado. Segunda edición. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. Adolfo Figueroa 2019 The Quality of Society Essays on the Unified Theory of Capitalism. New York. Palgrave MacMillan. Carlos Contreras y Stephan Gruber (Eds.) 2019 Historia del Pensamiento Económico en el Perú. Antología y selección de textos. Lima, Facultad de Ciencias Sociales PUCP. Barreix, Alberto Daniel; Corrales, Luis Fernando; Benitez, Juan Carlos; Garcimartín, Carlos; Ardanaz, Martín; Díaz, Santiago; Cerda, Rodrigo; Larraín B., Felipe; Revilla, Ernesto; Acevedo, Carlos; Peña, Santiago; Agüero, Emmanuel; Mendoza Bellido, Waldo; Escobar Arango y Andrés. 2019 Reglas fiscales resilientes en América Latina. Washington, BID. https://departamento.pucp.edu.pe/economia/libro/10743/ https://departamento.pucp.edu.pe/economia/libro/la-economia-ciencia-social-peru-cincuenta-anos-estudios-economicas-la-pontificia-universidad-catolica-del-peru/ https://departamento.pucp.edu.pe/economia/libro/la-economia-ciencia-social-peru-cincuenta-anos-estudios-economicas-la-pontificia-universidad-catolica-del-peru/ https://departamento.pucp.edu.pe/economia/libro/la-historia-fiscal-del-peru-1980-2020-colapso-estabilizacion-consolidacion-golpe-la-covid-19/ https://departamento.pucp.edu.pe/economia/libro/la-historia-fiscal-del-peru-1980-2020-colapso-estabilizacion-consolidacion-golpe-la-covid-19/ http://departamento.pucp.edu.pe/economia/libro/the-quality-of-society-essays-on-the-unified-theory-of-capitalism/ José D. Gallardo Ku 2019 Notas de teoría para para la incertidumbre. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. Úrsula Aldana, Jhonatan Clausen, Angelo Cozzubo, Carolina Trivelli, Carlos Urrutia y Johanna Yancari 2018 Desigualdad y pobreza en un contexto de crecimiento económico. Lima, Instituto de Estudios Peruanos. Séverine Deneulin, Jhonatan Clausen y Arelí Valencia (Eds.) 2018 Introducción al enfoque de las capacidades: Aportes para el Desarrollo Humano en América Latina. Flacso Argentina y Editorial Manantial. Fondo Editorial de la Pontificia Universidad Católica del Perú. Mario Dammil, Oscar Dancourt y Roberto Frenkel (Eds.) 2018 Dilemas de las políticas cambiarias y monetarias en América Latina. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. http://departamento.pucp.edu.pe/economia/libro/las-alianzas-publico-privadas-app-en-el-peru-beneficios-y-riesgos/ http://departamento.pucp.edu.pe/economia/libro/las-alianzas-publico-privadas-app-en-el-peru-beneficios-y-riesgos/ http://departamento.pucp.edu.pe/economia/libro/las-alianzas-publico-privadas-app-en-el-peru-beneficios-y-riesgos/  Documentos de trabajo No. 503 “La no linealidad en la relación entre la competencia y la sostenibilidad financiera y alcance social de las instituciones microfinancieras reguladas en el Perú”. Giovanna Aguilar y Jhonatan Portilla. Noviembre, 2021. No. 502 “Approximate Bayesian Estimation of Stochastic Volatility in Mean Models using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets”. Carlos A. Abanto-Valle, Gabriel Rodríguez, Luis M. Castro Cepero y Hernán B. Garrafa-Aragón. Noviembre, 2021. No. 501 “El impacto de políticas diferenciadas de cuarentena sobre la mortalidad por COVID-19: el caso de Brasil y Perú”. Angelo Cozzubo, Javier Herrera, Mireille Razafindrakoto y François Roubaud. Octubre, 2021. No. 500 “Determinantes del gasto de bolsillo en salud en el Perú”. Luis García y Crissy Rojas. Julio, 2021. No. 499 “Cadenas Globales de Valor de Exportación de los Países de la Comunidad Andina 2000-2015”. Mario Tello. Junio, 2021. No. 498 “¿Cómo afecta el desempleo regional a los salarios en el área urbana? Una curva de salarios para Perú (2012-2019)”. Sergio Quispe. Mayo, 2021. No. 497 “¿Qué tan rígidos son los precios en línea? Evidencia para Perú usando Big Data”. Hilary Coronado, Erick Lahura y Marco Vega. Mayo, 2021. No. 496 “Reformando el sistema de pensiones en Perú: costo fiscal, nivel de pensiones, brecha de género y desigualdad”. Javier Olivera. Diciembre, 2020. No. 495 “Crónica de la economía peruana en tiempos de pandemia”. Jorge Vega Castro. Diciembre, 2020. No. 494 “Epidemia y nivel de actividad económica: un modelo”. Waldo Mendoza e Isaías Chalco. Setiembre, 2020. No. 493 “Competencia, alcance social y sostenibilidad financiera en las microfinanzas reguladas peruanas”. Giovanna Aguilar Andía y Jhonatan Portilla Goicochea. Setiembre, 2020. No. 492 “Empoderamiento de la mujer y demanda por servicios de salud preventivos y de salud reproductiva en el Perú 2015-2018”. Pedro Francke y Diego Quispe O. Julio, 2020. No. 491 “Inversión en infraestructura y demanda turística: una aplicación del enfoque de control sintético para el caso Kuéalp, Perú”. Erick Lahura y Rosario Sabrera. Julio, 2020. No. 490 “La dinámica de inversión privada. El modelo del acelerador flexible en una economía abierta”. Waldo Mendoza Bellido. Mayo, 2020. No. 489 “Time-Varying Impact of Fiscal Shocks over GDP Growth in Peru: An Empirical Application using Hybrid TVP-VAR-SV Models”. Álvaro Jiménez y Gabriel Rodríguez. Abril, 2020. No. 488 “Experimentos clásicos de economía. Evidencia de laboratorio de Perú”. Kristian López Vargas y Alejandro Lugon. Marzo, 2020. No. 487 “Investigación y desarrollo, tecnologías de información y comunicación e impactos sobre el proceso de innovación y la productividad”. Mario D. Tello. Marzo, 2020. No. 486 “The Political Economy Approach of Trade Barriers: The Case of Peruvian’s Trade Liberalization”. Mario D. Tello. Marzo, 2020. No. 485 “Evolution of Monetary Policy in Peru. An Empirical Application Using a Mixture Innovation TVP-VAR-SV Model”. Jhonatan Portilla Goicochea y Gabriel Rodríguez. Febrero, 2020. No. 484 “Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models”. Jean Pierre Fernández Prada Saucedo y Gabriel Rodríguez. Febrero, 2020. No. 483 “Macroeconomic Effects of Loan Supply Shocks: Empirical Evidence”. Jefferson Martínez y Gabriel Rodríguez. Febrero, 2020. No. 482 “Acerca de la relación entre el gasto público por alumno y los retornos a la educación en el Perú: un análisis por cohortes”. Luis García y Sara Sánchez. Febrero, 2020. No. 481 “Stochastic Volatility in Mean. Empirical Evidence from Stock Latin American Markets”. Carlos A. Abanto-Valle, Gabriel Rodríguez y Hernán B. Garrafa- Aragón. Febrero, 2020. No. 480 “Presidential Approval in Peru: An Empirical Analysis Using a Fractionally Cointegrated VAR2”. Alexander Boca Saravia y Gabriel Rodríguez. Diciembre, 2019. No. 479 “La Ley de Okun en el Perú: Lima Metropolitana 1971 – 2016.” Cecilia Garavito. Agosto, 2019. No. 478 “Peru´s Regional Growth and Convergence in 1979-2017: An Empirical Spatial Panel Data Analysis”. Juan Palomino y Gabriel Rodríguez. Marzo, 2019.  Materiales de Enseñanza No. 5 “Matemáticas para Economistas 1”. Tessy Váquez Baos. Abril, 2019. No. 4 “Teoría de la Regulación”. Roxana Barrantes. Marzo, 2019. No. 3 “Economía Pública”. Roxana Barrantes, Silvana Manrique y Carla Glave. Marzo, 2018. No. 2 “Macroeconomía: Enfoques y modelos. Ejercicios resueltos”. Felix Jiménez. Marzo, 2016. No. 1 “Introducción a la teoría del Equilibrio General”. Alejandro Lugon. Octubre, 2015. Departamento de Economía - Pontificia Universidad Católica del Perú Av. Universitaria 1801, San Miguel, 15008 – Perú. Telf. 626-2000 anexos 4950 - 4951 http://departamento.pucp.edu.pe/economia/ DDD504 caratula DDD504-Segunda hoja DDD504-Contratapa DDD504-Abstract y texto wp_rch_page_1_18_08_2021 wp_rch_text_18_08_2021 Introduction The Model Households Commodities Export Firms Manufacturing Firms Final Goods Producers The Phillips Curve Foreign Sector Gross Domestic Product and Total Investment Monetary Policy Empirical Results Choice of Priors Data Description Estimation Results Bayesian Impulse Response Functions Forecast Error Variance Decomposition (FEVD) Conclusions Linear Model Households Commodities Export Firms Manufacturing Firms Final Good Producer Phillips Curve Foreign Sector Gross Domestic Product Total Investment Monetary Policy wp_rch_tables_18_08_2021 wp_rch_figures_18_08_2021 DDD504-ultimas publicaciones