Iqbal, Javed (2011): Forecasting Performance of Alternative Error Correction Models.
Download (76Kb) | Preview
It 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).
|Item Type:||MPRA Paper|
|Original Title:||Forecasting Performance of Alternative Error Correction Models|
|English Title:||Forecasting Performance of Alternative Error Correction Models|
|Keywords:||Co-integration, Error Correction Models, Forecasting|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
|Depositing User:||Javed Iqbal|
|Date Deposited:||30. Mar 2011 22:55|
|Last Modified:||11. Feb 2013 16:30|
Chambers , M.J. (1993). A note on forecasting in co-integrated systems. Computers Method Application s, 25, 93-99.
Clements, M. P. and D. F. Hendry (1995), Forecasting in co-integrated systems. Journal of Applied Econometrics10, 127-146.
Engle, R.F. and Granger, C.W.J. (1987).Co-integration and error correction: Representation, estimation and testing, Econometrica 55,251-276
Engle, R.F. and Yoo, B.S. (1987). Forecasting and testing in co-integrated systems, the Journal of Econometrics, 35,143-159.
Haigh, M.S. (2000). Co-integration, unbiased expectations and forecasting in the BIFFEX freight futures market. Journal of Futures Market, 20, 545-571
Hoffman, D.L and Rasche, R. H. (1996). Assessing Forecast Performance in a Co-integrated System. Journal of Applied Econometrics, Vol. 11,495-517.
Jansen, D.W and Wang, Z. (2006). Evaluating the ‘Fed Model’ of Stock Price Valuation: an Out-of-Sample Forecasting Perspective. Econometric Analysis of Financial and Economic Time Series/Part B Advances in Econometrics, Volume 20, 179– 204.
Johansens S. (1998). Statistical analysis of cointegrating vectors. Journal of Economics of Economics Dynamics and Control, 12, 231-54.
Lin, J.L. and Tsay, R.S. (1996). Co-integration constraint and Forecasting: An empirical examination. Journal of Applied Econometrics, Vol. 11,519-538.
Pesaran, M. H., Shin, Y., and Smith, R. J. (1996) “Bounds Testing Approaches to the Analysis of Level Relationships” DEA working paper 9622, Department of Applied Economics, University of Cambridge.
Pesaran, M. H., Shin, Y. and Smith, R. J. (2001) “Bounds Testing Approaches to the Analysis of Level Relationships” Journal of Applied Econometrics 16, 289-326.
Stock, J.H.(1995). Point forecasts and prediction intervals for long-horizon forecast. Manuscript, J.F.K. School of Government, Harvard University.
Wang, Z and Bessler, D.A. (2004). Forecasting performance of multivariate time series models with full and reduced rank: An empirical examination. International Journal of Forecasting 20, 683-695.