(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.
(Pontificia Universidad Católica del Perú, 2022-08-01) Pincheira Brown, Pablo
In this paper we introduce a “power booster factor” for out-of-sample tests of predictability. The relevant econometric environment is one in which the econometrician wants to compare the population Mean Squared Prediction Errors (MSPE) of two models: one big nesting model, and another smaller nested model. Although our factor can be used to improve finite sample properties of several out-of-sample tests of predictability, in this paper we focus on the widely used test developed by Clark and West (2006, 2007). Our new test multiplies the Clark and West t-statistic by a factor that should be close to one under the null hypothesis that the short nested model is the true model, but that should be greater than one under the alternative hypothesis that the big nesting model is more adequate. We use Monte Carlo simulations to explore the size and power of our approach. Our simulations reveal that the new test is well sized and powerful. In particular, it tends to be less undersized and more powerful than the test by Clark and West (2006, 2007). Although most of the gains in power are associated to size improvements, we also obtain gains in size-adjusted-power. Finally we illustrate the use of our approach when evaluating the ability that an international core inflation factor has to predict core inflation in a sample of 30 OECD economies. With our “power booster factor” more rejections of the null hypothesis are obtained, indicating a strong influence of global inflation in a selected group of these OECD countries.
