Evaluating heterogeneous forecasts for vintages of macroeconomic variables

Max Welz,Philip Hans Franses

doi:10.1002/for.2835

Abstract

AbstractThere are various reasons why professional forecasters may disagree in their quotes for macroeconomic variables. One reason is that they target at different vintages of the data. We propose a novel method to test forecast bias in case of such unobserved heterogeneity. The method is based on so‐called symbolic regression, where the variables of interest become interval variables. We associate the interval containing the vintages of data with the intervals of the forecasts. An illustration to 18 years of forecasts for annual US real GDP growth, given by the Consensus Economics forecasters, shows the relevance of the method.

Highlights

Introduction and motivationThis paper is all about the well-known Mincer Zarnowitz (1969) (MZ) auxiliary regression, which is often used to examine bias in forecasts
Forecasts created by professional forecasters can show substantial dispersion
A recent contribution to this literature by Clements (2017) adds another potential source of heterogeneity, and this is that forecasters may target different vintages of the macroeconomic data

Summary

Introduction and motivation

This paper is all about the well-known Mincer Zarnowitz (1969) (MZ) auxiliary regression, which is often used to examine (the absence of) bias in forecasts. We will apply the novel MZ Symbolic Regression to the USA growth rates data and compare the outcomes with what one would have obtained if specific vintages were considered. To run a Mincer Zarnowitz (MZ) regression, the forecasts per month are usually summarized by taking the median, by using a variance measure, or by the mean (“the consensus”), that is, by considering. One can run this MZ test for each vintage of the data, but still it is unknown what the estimated parameters in the MZ regression reflect. As the dependent variable, instead of yytiti, and to consider These two new variables are intervals, and often they are called symbolic variables. Instead of points in the simple regression case, the data can be represented as rectangles

How does Symbolic Regression work?

Standard errors

Analysis of forecasts

Conclusion and discussion

Gross Domestic

Forecast origin