Reducing uncertainty in short-term projections: linkage of monthly and quarterly models
暂无分享,去创建一个
This paper shows how monthly data and forecasts can be used in a systematic way to improve the predictive accuracy of a quarterly macroeconometric model. The problem is formulated as a model pooling procedure (equivalent to non-recursive Kalman filtering) where a baseline quarterly model forecast is modified through ‘add-factors’ or ‘constant adjustments’. The procedure ‘automatically’ constructs these adjustments in a covariance-minimizing fashion to reflect the revised expectation of the quarterly model's forecast errors, conditional on the monthly information set. Results obtained using Federal Reserve Board models indicate the potential for significant reduction in forecast error variance through application of these procedures.
[1] J. S. Mehta,et al. Two methods of evaluating hoerl and kennard's ridge regression , 1978 .
[2] C. Granger,et al. Forecasting Economic Time Series. , 1988 .
[3] P. Tinsley,et al. On filtering auxiliary information in short-run monetary policy , 1977 .
[4] Edwin Kuh,et al. Structural Sensitivity in Econometric Models. , 1985 .
[5] J. M. Bates,et al. The Combination of Forecasts , 1969 .