Forecasting Performance of Alternative Error Correction Models
AbstractIt is well established that regression analysis on non-stationary time series data may yield spurious results. An earlier response to this problem was to run regression with first difference of variables. But this transformation destroys any long-run information embodied in the levels of variables. According to ‘Granger Representation Theorem’ (Engle and Granger, 1987) if variables are co-integrated, there exist an error correction mechanism which incorporates long run information in modeling changes in variables. This mechanism employs an additional lag value of the disequilibrium error as an additional variable in modeling changes in variables. It has been argued that ECM performs better for long run forecast than a simple first difference or level regression. This process contributes to the literature in two important ways. Firstly empirical evidence does not exist on the relative merits of ECM arrived at using alternative co-integration techniques. The three popular co-integration procedures considered are the Engle-Granger (1987) two step procedure, the Johansen (1988) multivariate system based technique and the recently developed Auto regressive Distributed Lag based technique of Pesaran et al. (1996, 2001). Secondly, earlier studies on the forecasting performance of the ECM employed macroeconomic data on developed economies i.e. the US and the UK. By employing data form the Asian countries and using absolute version of the purchasing power parity and money demand function this paper compares forecast accuracy of the three alternative error correction models in forecasting the nominal exchange rate and monetary aggregate (M2).
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 29826.
Date of creation: 19 Mar 2011
Date of revision: 19 Mar 2011
Co-integration; Error Correction Models; Forecasting;
Find related papers by 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
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-04-09 (All new papers)
- NEP-ETS-2011-04-09 (Econometric Time Series)
- NEP-FOR-2011-04-09 (Forecasting)
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.:
- Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, Elsevier, vol. 35(1), pages 143-159, May.
- M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
- Wang, Zijun & Bessler, David A., 2004. "Forecasting performance of multivariate time series models with full and reduced rank: an empirical examination," International Journal of Forecasting, Elsevier, Elsevier, vol. 20(4), pages 683-695.
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