Economía

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e-ISSN: 2304-4306

ECONOMÍA Journal was initially established as the “Revista Economía” of the Department of Economics of the Pontificia Universidad Católica of Peru (PUCP) in 1977. It is the oldest academic journal on economics in the country. Building on that legacy, ECONOMÍA has now been relaunched as an internationally refereed journal dedicated to publishing original academic research on economics in English, with an expanded and prestigious Editorial Board, as well as a large team of Associated Editors that guarantee the highest theoretical and methodological standards.

ECONOMÍA also offers a manuscript management platform that provides an eficient workflow among authors, associate editors and referees throughout the process of manuscript submission and evaluation. In this new stage, ECONOMÍA aspires to continue leading the progress of academic literature in the country as well as position itself in a prominent place in the Latin American region.

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Start date: 2020Subject: search.filters.subject.Forecasting

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    Searching for the Best Inflation Forecasters within an Employment Survey: Microdata Evidence from Chile
    (Pontificia Universidad Católica del Perú, 2022-08-01) Medel, Carlos A.
    This article aims to evaluate quantitative inflation forecasts for the Chilean economy, taking advantage of a specific survey of consumer perceptions at the individual microdata level, which, at the same time, is linked to a survey of employment in Chile’s capital city. Thus, it is possible to link, with no error, consumer perceptions and 12-month-ahead inflation forecasts with personal characteristics such as gender, age, educational level, county of living, and the economic sector in which they are currently working. By using a sample ranging from 2005.III to 2018.IV, the results suggest that women aged between 35 and 65 years old, with a college degree, living in the North-eastern part of Santiago (the richest of the city), and working in the Community and Social Services sector are the best forecasters. Men aged between 35 and 65 years old, with a college degree, in a tie living in the North-eastern and South-eastern zones but working in Government and Financial Services and Retail sectors, respectively, come in second. Some econometric exercises reinforce and give greater support to the group of most accurate forecasters and reveal that another group of forecasters, different from the second-best in terms of forecast accuracy, displays the characteristics required of a forecasting variable. Remarkably, this group has the same specifications as the most accurate group, with the only difference being that it is composed of men instead of women. Thus, it looks promising for further consideration. Importantly, a forecast accuracy test reveals that no factor comes out as superior to the naïve random walk forecast used as a benchmark. These results are important because they help to identify the most accurate group when forecasting inflation and, thus, help refine the information provided by the survey for inflation forecasting purposes.
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    A Short Term Forecasting Model for the Spanish GDP and its Demand Components
    (Pontificia Universidad Católica del Perú. Fondo Editorial, 2020-03-10) Arencibia Pareja, Ana; Gomez-Loscos, Ana; de Luis López, Mercedes; Perez-Quiros, Gabriel
    This paper proposes a new version of the Spain-STING (Spain, Short-Term INdicator of Growth), a dynamic factor model used by the Banco de España for the short-term forecasting of the Spanish economy. The extended and revised version of the Spain-STING presented in this document includes a forecast for each of the demand components of the National Accounts. In order to select the indicators that best estimate the Spanish GDP and its demand components, several models are considered. Following this strategy, the selected models are those in which the common factor explains the highest proportion of the variance of the GDP. These models allow us to forecast GDP, private consumption, public expenditure, investment in capital goods, construction investment, exports and imports in a consistent way. We assess the predictive power of the models for GDP and its demand components for the period 2005–2017. With regard to the GDP forecast, we find some improvement of the predictive power compared to the previous version of Spain-STING. As for the demand components, we show that our proposal has better predictive power than other possible time series models.