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Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks

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Author Info
Pesaran, M.H.
Timmermann, A.

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Abstract

Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural breaks. This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under a structural break. Our approach is quite general and allows for unit roots both pre- and post-break. We derive finite-sample results for the mean squared forecast error of one-step-ahead forecasts, both conditionally and unconditionally and present numerical results for different types of break specifications. Implication of breaks for the determination of the optimal window size are also discussed.

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Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0331.

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Length: 36
Date of creation: Jun 2003
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Handle: RePEc:cam:camdae:0331

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Related research
Keywords: small sample properties of forecasts; RMSFE; structural breaks; autoregression;

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Find related papers by JEL classification:
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    Other versions:
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Guillaume Chevillon, 2006. "Multi-step Forecasting in Unstable Economies: Robustness Issues in the Presence of Location Shifts," Economics Series Working Papers 257, University of Oxford, Department of Economics. [Downloadable!]
  2. Pesaran, M. Hashem, 2004. "General Diagnostic Tests for Cross Section Dependence in Panels," IZA Discussion Papers 1240, Institute for the Study of Labor (IZA). [Downloadable!]
    Other versions:
  3. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90. [Downloadable!]
  4. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," CEPR Discussion Papers 7008, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  5. Pesaran, M.H. & Pick, A. & Timmermann, A., 2009. "Variable Selection and Inference for Multi-period Forecasting Problems," Cambridge Working Papers in Economics 0901, Faculty of Economics, University of Cambridge. [Downloadable!]
    Other versions:
  6. Junsoo Lee & John A. List & Mark Strazicich, 2005. "Nonrenewable Resource Prices: Deterministic or Stochastic Trends?," NBER Working Papers 11487, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  7. Pesaran, M.H. & Pettenuzzo, D. & Timmermann, A., 2004. "‘Forecasting Time Series Subject to Multiple Structural Breaks’," Cambridge Working Papers in Economics 0433, Faculty of Economics, University of Cambridge. [Downloadable!]
    Other versions:
  8. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," Economics Working Papers ECO2009/31, European University Institute. [Downloadable!]
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