Forecasting in factor augmented regressions under structural change

TY - JOUR

T1 - Forecasting in factor augmented regressions under structural change

AU - Massacci, Daniele

AU - Kapetanios, George

N1 - Publisher Copyright: © 2022 International Institute of Forecasters

PY - 2024/1/1

Y1 - 2024/1/1

N2 - Factor augmented regressions are widely used to produce out-of-sample forecasts of macroeconomic and financial time series. However, these series are subject to occasional breaks. We study the effect of neglected structural instability on the forecasts produced by factor augmented regressions when the latent factors are estimated by cross-sectional averages from a large panel of variables. Our results show that neglecting structural instability can be very costly in terms of forecasting performance. We derive analytical results to show that instability in the factor model and in the forecasting equation impacts the produced forecasts. We further provide numerical results showing that conditioning upon the most recent break tends to produce more accurate forecasts than unconditional estimation methods based on expanding or rolling windows. However, the actual gain depends on the location and the magnitude of the breaks. Finally, an application to out-of-sample stock return forecasting using liquidity proxies illustrates the empirical relevance of our results.

AB - Factor augmented regressions are widely used to produce out-of-sample forecasts of macroeconomic and financial time series. However, these series are subject to occasional breaks. We study the effect of neglected structural instability on the forecasts produced by factor augmented regressions when the latent factors are estimated by cross-sectional averages from a large panel of variables. Our results show that neglecting structural instability can be very costly in terms of forecasting performance. We derive analytical results to show that instability in the factor model and in the forecasting equation impacts the produced forecasts. We further provide numerical results showing that conditioning upon the most recent break tends to produce more accurate forecasts than unconditional estimation methods based on expanding or rolling windows. However, the actual gain depends on the location and the magnitude of the breaks. Finally, an application to out-of-sample stock return forecasting using liquidity proxies illustrates the empirical relevance of our results.

KW - Cross-sectional averages

KW - Estimation window

KW - Factor augmented regression

KW - Out-of-sample forecasts

KW - Structural instability

UR - http://www.scopus.com/inward/record.url?scp=85146472066&partnerID=8YFLogxK

U2 - 10.1016/j.ijforecast.2022.12.004

DO - 10.1016/j.ijforecast.2022.12.004

M3 - Article

AN - SCOPUS:85146472066

SN - 0169-2070

VL - 40

SP - 62

EP - 76

JO - INTERNATIONAL JOURNAL OF FORECASTING

JF - INTERNATIONAL JOURNAL OF FORECASTING

IS - 1

ER -