A framework for economic forecasting
This paper proposes a tripartite framework of design, evaluation, and post-evaluation analysis for generating and interpreting economic forecasts. This framework?s value is illustrated by re-examining mean square forecast errors from dynamic models and nonlinearity biases from empirical forecasts of US external trade. Previous studies have examined properties such as nonlinearity bias and the possible nonmonotonicity and nonexistence of mean square forecast errors in isolation from other aspects of the forecasting process, resulting in inefficient forecasting techniques and seemingly puzzling phenomena. The framework developed reveals how each such property follows from systematically integrating all aspects of the forecasting process.
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Volume (Year): 1 (1998)
Issue (Month): ConferenceIssue ()
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- Beach, Charles M & MacKinnon, James G, 1978. "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors," Econometrica, Econometric Society, vol. 46(1), pages 51-58, January.
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- Brown, Bryan W. & Mariano, Roberto S., 1989. "Predictors in Dynamic Nonlinear Models: Large-Sample Behavior," Econometric Theory, Cambridge University Press, vol. 5(03), pages 430-452, December.
- Calzolari, Giorgio, 1979. "Antithetic variates to estimate the simulation bias in non-linear models," Economics Letters, Elsevier, vol. 4(4), pages 323-328.
- Baillie, Richard T, 1981. "Prediction from the Dynamic Simultaneous Equation Model with Vector Autoregressive Errors," Econometrica, Econometric Society, vol. 49(5), pages 1331-37, September.
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