Small sample properties of forecasts from autoregressive models under structural breaks

This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under structural breaks. Finite-sample results for the mean squared forecast error of one-step ahead forecasts are derived, both conditionally and unconditionally,...

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Vydáno v:Journal of econometrics Ročník 129; číslo 1; s. 183 - 217
Hlavní autoři: Pesaran, M. Hashem, Timmermann, Allan
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.11.2005
Elsevier
Elsevier Sequoia S.A
Edice:Journal of Econometrics
Témata:
Applications
Autoregression
Bias
Chaos theory
Econometric models
Econometrics
Economic forecasts
Errors
Exact sciences and technology
Forecasting
Inference from stochastic processes; time series analysis
Insurance, economics, finance
Macroeconomics
Manycountries
Mathematical methods
Mathematics
Modelling
Monte Carlo simulation
MSFE
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Property
Regression analysis
Rolling window estimator
Sampling
Sciences and techniques of general use
Small sample properties of forecasts
Statistics
Structural breaks
Structural change
Structural models
Studies
Time series
C53
Structural breaks
MSFE
C22
Autoregression
Rolling window estimator
Small sample properties of forecasts
Monte Carlo method
Data analysis
Error estimation
Time series
Autoregressive model
Forecast model
Economic data
Statistical method
Forecasting theory
Unbiased estimation
Theoretical model
C53 Small sample properties of forecasts
Rolling window estimation
Structural model
Mean error
Macroeconomics
Econometrics
Small sample property
Structural break
ISSN:0304-4076, 1872-6895
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Popis
Shrnutí:This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under structural breaks. Finite-sample results for the mean squared forecast error of one-step ahead forecasts are derived, both conditionally and unconditionally, and numerical results for different types of break specifications are presented. It is established that forecast errors are unconditionally unbiased even in the presence of breaks in the autoregressive coefficients and/or error variances so long as the unconditional mean of the process remains unchanged. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and on a range of macroeconomic time series from G7 countries. The results are used to draw practical recommendations for the choice of estimation window when forecasting from autoregressive models subject to breaks.
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2004.09.007