A method for taking models to the data
Abstract
This paper develops a method for combining the power of a dynamic, stochastic, general equilibrium model with the flexibility of a vector autoregressive time-series model to obtain a hybrid that can be taken directly to the data. It estimates this hybrid model via maximum likelihood and uses the results to address a number of issues concerning the ability of a prototypical real business cycle model to explain movements in aggregate output and employment in the postwar US economy, the stability of the real business cycle model's structural parameters, and the performance of the hybrid model's out-of-sample forecasts.(This abstract was borrowed from another version of this item.)
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Bibliographic Info
Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.
Volume (Year): 28 (2004)
Issue (Month): 6 (March)
Pages: 1205-1226
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Web page: http://www.elsevier.com/locate/jedc
Related research
Keywords:Other versions of this item:
- Peter N. Ireland, 1999. "A Method for Taking Models to the Data," Boston College Working Papers in Economics 421, Boston College Department of Economics.
- Tom Doan, . "RATS programs to replicate Ireland's JEDC 2004 estimation of DSGE model," Statistical Software Components RTZ00104, Boston College Department of Economics.
- Peter N. Ireland, 1999. "A method for taking models to the data," Working Paper 9903, Federal Reserve Bank of Cleveland.
- Peter Ireland, 1999. "A Method for Taking Models to the Data," Computing in Economics and Finance 1999 1233, Society for Computational Economics.
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
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