Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison

Lanne, Markku and Nyberg, Henri and Saarinen, Erkka (2011): Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison.

Abstract

In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models in terms of the mean square and mean absolute forecast errors. For a set of 18 quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater.

• Item Type: MPRA Paper
• Language: English
• Keywords: Noncausal autoregression; forecast comparison; macroeconomic variables; financial variables
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications

References

Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.

Breidt, J., Davis, R.A., Lii, K. S., and M. Rosenblatt (1991). Maximum likelihood estimation for noncausal autoregressive processes. Journal of Multivariate Analysis 36, 175–198.

Lanne, M., and P. Saikkonen (2011a). GMM estimation with noncausal instruments. Oxford Bulletin of Economics and Statistics, forthcoming.

Lanne, M., and P. Saikkonen (2011b). Noncausal autoregressions for economic time series. Journal of Time Series Econometrics, forthcoming.

Lanne, M., Luoma, A., and J. Luoto (2011). Bayesian model selection and forecasting in noncausal autoregressive models. Journal of Applied Econometrics, forthcoming.

Lanne, M., Luoto, J., and P. Saikkonen (2010). Optimal forecasting of noncausal autoregressive time series. HECER Discussion Paper No. 286.

Marcellino, M., Stock, J.H., and M.W. Watson (2006). A comparison of direct and iterated AR methods for forecasting macroeconomic time series. Journal of Econometrics, 135, 499–526.

Rosenblatt, M. (2000). Gaussian and Non-Gaussian Linear Time Series and Random Fields. Springer-Verlag, New York.

Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.