Departamento Académico de Economía
URI permanente para esta comunidadhttp://54.81.141.168/handle/123456789/124141
El Departamento de Economía de la Pontificia Universidad Católica del Perú fue creado en agosto de 1969 y desde entonces el equipo de profesores que lo conforman se ha caracterizado tanto por su labor docente como por su dedicación permanente a la investigación de los temas relevantes para la sociedad y la economía peruana.
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Ítem Acceso Abierto Time-Varying Effects of Financial Uncertainty Shocks on Macroeconomic Fluctuations in Peru(Pontificia Universidad Católica del Perú. Departamento de Economía, 2024-01) Alvarado, Mauricio; Rodríguez, GabrielThis article employs a family of VAR models with time-varying parameters and stochastic volatility (TVP-VAR-SV) to estimate the impact of external financial uncertainty shocks on a set of macroeconomic variables in Peru for the period from 1996Q1 to 2022Q4. The main findings can be summarized as follows: (i) a simple VAR model with stochastic volatility is sufficient to capture uncertainty dynamics compared to TVP-VAR alternatives; (ii) uncertainty shocks have a negative and significant impact on private investment growth in the medium and long term; (iii) the impact on private investment growth is three times greater than that on GDP growth; (iv) uncertainty shocks behave like aggregate supply shocks, leading to an increase in the inflation rate; and (v) uncertainty shocks have stronger effects in scenarios characterized by unfavorable financial conditions.Ítem Acceso Abierto External Shocks and Economic Fluctuations in Peru: Empirical Evidence using Mixture Innovation TVP-VAR-SV Models(Pontificia Universidad Católica del Perú. Departamento de Economía, 2024-01) Guevara, Brenda; Rodriguez, Gabriel; Yamuca Salvatierra, LorenaWe employ a family of mixture innovation, time-varying parameter VAR models with stochastic volatility (TVP-VAR-SV) to analyze the impact of external shocks on Peru’s GDP growth, inflation, and interest rate from 1998Q1 to 2019Q4. Our key findings are as follows: (i) the model best fitting the data features time-varying parameters and variances with a certain likelihood; (ii) impulse-response functions reveal that a 1% increase in the growth rate of Peru’s major trading partners (China and the U.S.) leads to a domestic GDP growth expansion of 0.65% and 0.21%, respectively; (iii) the forecast error variance decomposition shows that external shocks account for 65% of the long-term variability in output, 65% in inflation, and 67% in the interest rate; (iv) historical decomposition indicates that external shocks account for 50% of domestic GDP growth, particularly from 2002 onward. Lastly, we validate the results obtained in the primary specification through four robustness exercisesÍtem Acceso Abierto Time changing effects of external shocks on macroeconomic fluctuations in Peru: empirical application using regime-switching VAR models with stochastic volatility(Pontificia Universidad Católica del Perú. Departamento de Economía, 2022-03) Rodríguez, Gabriel; Chávez, PauloThis article quantifies and analyzes the evolving impact of external shocks on Peru’s macroeconomic fluctuations in 1994Q1-2019Q4. For this purpose, we use a group of models with regimeswitching time-varying parameters and stochastic volatility (RS-VAR-SV), as proposed by Chan and Eisenstat (2018). The data suggest a model with contemporaneous coefficients and constant lags and intercepts, but with regime-switching variances; and point to the existence of two regimes. The IRFs, FEVDs, and HDs show that: (i) China growth shocks have a higher impact on Peru’s output growth (around 0.8%); (ii) financial shocks contract domestic output growth by 0.3% and domestic monetary policy is synchronized with Fed rate movements; (iii) external shocks explain 35% and 70% of output fluctuations under regimes 1 and 2, respectively; and (iv) China growth shocks contributed 1.0 p.p. to the 1.1-p.p. increase (around 89%) in Peru’s output growth between regimes 1 and 2. Additionally, we validate these results by performing seven robustness exercises consisting in changing priors, reordering variables, changing variables, and using four different specications for the baseline model.