Using regression models with time-varying coefficients for short-term economic forecasting

Utilisation de modèles de régression à coefficients variant dans le temps pour la prévision conjoncturelle

Alain Quartier-La-Tente ()
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Alain Quartier-La-Tente: DGFiP - Direction Générale des Finances Publiques - Ministère de l’Action et des Comptes publics

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Abstract: This study describes three methods for estimating linear regression models with time-varying coefficients: piecewise regression, local regression, and regression with stochastic coefficients (state space modeling). It also details their implementation in R using the tvCoef package. Through a comparative analysis of around thirty quarterly forecasting models, we show that the use of these methods, especially thanks to the state-space modeling, reduces forecast errors when breakpoints are present in the coefficients. Moreover, even when traditional tests conclude that the coefficients are stable, regression with stochastic coefficients can still help reduce forecast errors. However, uncertainties related to estimating certain hyperparameters can increase real-time forecast errors, especially for local regression. Thus, an economic analysis of estimated parameters remains essential. This study is fully reproducible and all the codes used are available under https://github.com/InseeFrLab/DT-tvcoef.

Keywords: time series; forecast; long time series; séries temporelles; prévisions; séries longues (search for similar items in EconPapers)
Date: 2024-07
Note: View the original document on HAL open archive server: https://insee.hal.science/hal-05322067v1
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