Assessing the forecasting accuracy of monthly vector autoregressive models: The case of five OECD countries

Abstract Vector autoregressions have been proposed as good forecasting models in the recent past. This paper presents five different VAR specifications of the level of industrial production in five major industrial countries estimated on monthly data. The forecasting performance of the various specifications is analysed and compared with the accuracy of univariate time-series models. In general, the VAR forecasts perform better than the alternative procedures. Their performance in forecasting the calender outcome 1988:1–1988:12 (on an ex ante basis after the stock market crash in October 1987) reveals, however, that all VAR's have underestimated the growth rates in industrial production.

[1]  C. Sims.  MACROECONOMICS AND REALITY , 1977 .

[2]  H. Theil.  Principles of econometrics , 1971 .

[3]  G. C. Tiao,et al.  Modeling Multiple Time Series with Applications , 1981 .

[4]  A. Zellner,et al.  Forecasting international growth rates using Bayesian shrinkage and other procedures , 1989 .

[5]  Robert B. Litterman.  Forecasting with Bayesian Vector Autoregressions-Five Years of Experience , 1984 .

[6]  G. Chow,et al.  MULTIPERIOD PREDICTIONS FROM STOCHASTIC DIFFERENCE EQUATIONS BY BAYESIAN METHODS , 1973 .

[7]  C. Granger,et al.  Forecasting Economic Time Series. , 1988 .

[8]  Robert B. Litterman,et al.  Forecasting and Conditional Projection Using Realistic Prior Distributions , 1983 .

[9]  Tests of Granger causality by the selection of the orders of a bivariate autoregressive model , 1986 .

[10]  Franz C. Palm,et al.  Structural econometric modelling and time series analysis: an integrated approach , 1981 .

[11]  Robert B. Litterman,et al.  Specifying vector autoregressions for macroeconomic forecasting , 1984 .

[12]  R. MacDonald,et al.  On the specification of granger-causality tests using the cointegration methodology , 1987 .

[13]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[14]  R. Webb,et al.  Defining and Improving the Accuracy of Macroeconomic Forecasts: Contributions from a VAR Model , 1984 .

[15]  ARIMA-Modelle: Eine Alternative zu ökonometrischen Konjunkturmodellen?/ARIMA Models — An Alternative to Econometric Short-Term Models? , 1987 .

[16]  John L. Kling,et al.  A comparison of multivariate forecasting procedures for economic time series , 1985 .

[17]  Gerald Nickelsburg,et al.  Small-sample properties of dimensionality statistics for fitting VAR models to aggregate economic data: A Monte Carlo study , 1985 .

[18]  Cheng Hsiao,et al.  AUTOREGRESSIVE MODELLING AND MONEY-INCOME CAUSALITY DETECTION , 1981 .

[19]  P. Caines,et al.  Causality Analysis and Multivariate Autoregressive Modelling with an Application to Supermarket Sales Analysis , 1980 .

[20]  R. Fair,et al.  VAR Models as Structural Approximations , 1988 .

[21]  G. C. Tiao,et al.  Multiple Time Series Modeling and Extended Sample Cross-Correlations , 1983 .

[22]  Arnold Zellner,et al.  Macroeconomic Forecasting Using Pooled International Data , 1987 .

[23]  Robert B. Litterman.  Forecasting and policy analysis with Bayesian vector autoregression models , 1984 .

[24]  Arnold Zellner,et al.  Statistical Analysis of Econometric Models , 1979 .

[25]  A. Zellner,et al.  Time series analysis and simultaneous equation econometric models , 1974 .

[26]  Robert B. Litterman.  Techniques of forecasting using vector autoregressions , 1979 .

[27]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[28]  On the seasonality of vector autoregression residuals , 1985 .

[29]  C. Nelson,et al.  Trends and random walks in macroeconmic time series: Some evidence and implications , 1982 .

[30]  Robert M. Kunst,et al.  A forecasting comparison of some var techniques , 1986 .

[31]  P. Perron,et al.  Trends and random walks in macroeconomic time series : Further evidence from a new approach , 1988 .