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Forecasting with Difference-Stationary and Trend-Stationary Models

Author

Listed:
  • David Hendry
  • Michael P. Clements

Abstract

Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations illustrate the analysis, to evaluate the biases in conventional standard errors when each model is mis-specified, compute the relative mean-square forecast errors of the two models for both DGPs, and investigate autocorrelated errors, so both models can better approximate the converse GDP. The outcomes are surprisingly different from established results.

Suggested Citation

  • David Hendry & Michael P. Clements, 2000. "Forecasting with Difference-Stationary and Trend-Stationary Models," Economics Series Working Papers 5, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:5
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    File URL: https://ora.ox.ac.uk/objects/uuid:7551e74a-6403-4ec1-b312-7bb46d497aff
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    Keywords

    difference stationary; trend stationary; forecastability;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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