Estimating the effect of a variable in a high-dimensional regression model
A problem encountered in some empirical research, e.g. growth empirics, is that the potential number of explanatory variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to determine whether a particular variable has an effect. We assume that the effect is identified in a high-dimensional linear model specified by unconditional moment restrictions. We consider properties of the following methods, which rely on lowdimensional models to infer the effect: Extreme bounds analysis, the minimum t-statistic over models, Sala-i-Martin’s method, BACE, BIC, AIC and general-tospecific. We propose a new method and show that it is well behaved compared to existing methods.
|Date of creation:||24 Nov 2010|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.econ.au.dk/afn/|
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- Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000.
"Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (Bace) Approach,"
OECD Economics Department Working Papers
266, OECD Publishing.
- Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
- Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," NBER Working Papers 7750, National Bureau of Economic Research, Inc.
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