论文信息 - Macroeconomic Forecasting Using Pooled International Data

Macroeconomic Forecasting Using Pooled International Data

In a recent article, Garcia-Ferrer, Highfield, Palm, and Zellner (1987) used pooling techniques to forecast annual output growth rates in nine countries. Examining various model specifications, they found that, among the fixed-parameter models investigated, a third-order autoregressive (AR) model with leading indicators, AR(3)LI, produces the best forecasts overall. A reexamination using state-space methods shows that a more parsimonious fixed-parameter model, which does not have an autoregressive part, leads generally to better forecasts than the fixed-parameter AR(3)LI model.

S. Mittnik

[1] A. Zellner,et al. Leading economic indicators: Bayesian methods for forecasting turning points in economic time-series: Sensitivity of forecasts to asymmetry of loss structures , 1991 .

[2] S. Mittnik. Forecasting with balanced state space representations of multivariate distributed lag models , 1990 .

[3] S. Mittnik. MULTIVARIATE TIME SERIES ANALYSIS WITH STATE SPACE MODELS††Presented at the 1988 Annual Meeting of the Society for Economic Dynamics and Control, Tempe, Arizona. I would like to thank Frank Diebold for his helpful comments. , 1989 .

[4] S. Mittnik. Multivariate time series analysis with state space models , 1989 .

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

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

[7] Pieter Wieger Otter. Dynamic Feature Space Modelling, Filtering and Self-Tuning Control of Stochastic Systems: A Systems Approach with Economic and Social Applications , 1985 .

[8] David J. Smyth,et al. Short-run macroeconomic forecasting: The OECD performance , 1983 .

[9] G. Box,et al. On a measure of lack of fit in time series models , 1978 .