Searching for the Best Inflation Forecasters within an EmploymentSurvey: Microdata Evidence from Chile

Carlos A. Medel ()

Revista Economía, 2022, vol. 45, issue 89, 184-216

Abstract: 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.II to 2018.IV, the results suggest that women aged between 35 and 65 years old, with a college degree, living in theNorth-eastern part of Santiago (the richest of the city), and working in theCommunity 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 South-eastern and North-eastern part of the city but working in Retail and Government and Financial Services 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.

Keywords: Employment survey; Inflation; Consumer sentiment; Microdata; Forecasting; Survey data (search for similar items in EconPapers)
JEL-codes: C53 C83 E31 E37 (search for similar items in EconPapers)
Date: 2022
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