Evaluating Automatic Model Selection
AbstractWe evaluate automatically selecting the relevant variables in an econometric model from a large candidate set.� General-to-specific selection is outlined for a constant model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors (N < T) where T is the sample size, then evaluated in simulation experiments for N = 1000.� Comparisons with Autometrics (Doornik, 2009) show similar properties, but not restricted to orthogonal cases.� Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing, and evaluate Autometrics'�capability in dynamic models by its cost of search versus costs of inference.
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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 474.
Date of creation: 01 Jan 2010
Date of revision:
Model selection; Autometrics; Post-selection bias correction; Costs of search; Costs of inference;
Other versions of this item:
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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- Jerzy Mycielski & Michal Kurcewicz, 2004. "A Specification Search Algorithm for Cointegrated Systems," Computing in Economics and Finance 2004, Society for Computational Economics 321, Society for Computational Economics.
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