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In: Pearson series in economics
In: Advances in econometrics 26
The economics and statistics literature using computer simulation based methods has grown enormously over the past decades. Maximum Simulated Likelihood is a statistical tool useful for incorporating individual differences (called heterogeneity in the econometrics literature) and variations into a statistical analysis. Problems that can be intractable with traditional methods are solved using computer simulation integrated with classical methods. Instead of assuming that everyone responds to stimuli in the same way, allowances are made for the possibility that different decision makers will respond in different ways. The techniques can be applied to problems of individual choice, such as the choice of a transportation model, or choice among health care options, as well as to the problem of making financial and macroeconomic predictions. Contributors to the volume discuss alternative simulation methods that permit faster and more accurate inference, as well as applications of established methods.
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Band 19, Heft 2, S. 170-172
ISSN: 1476-4989
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Band 19, Heft 2, S. 135-146
ISSN: 1476-4989
Plümper and Troeger (2007) propose a three-step procedure for the estimation of a fixed effects (FE) model that, it is claimed, "provides the most reliable estimates under a wide variety of specifications common to real world data." Their fixed effects vector decomposition (FEVD) estimator is startlingly simple, involving three simple steps, each requiring nothing more than ordinary least squares (OLS). Large gains in efficiency are claimed for cases of time-invariant and slowly time-varying regressors. A subsequent literature has compared the estimator to other estimators of FE models, including the estimator of Hausman and Taylor (1981) also (apparently) with impressive gains in efficiency. The article also claims to provide an efficient estimator for parameters on time-invariant variables (TIVs) in the FE model. None of the claims are correct. The FEVD estimator simply reproduces (identically) the linear FE (dummy variable) estimator then substitutes an inappropriate covariance matrix for the correct one. The consistency result follows from the fact that OLS in the FE model is consistent. The "efficiency" gains are illusory. The claim that the estimator provides an estimator for the coefficients on TIVs in an FE model is also incorrect. That part of the parameter vector remains unidentified. The "estimator" relies upon a strong assumption that turns the FE model into a type of random effects model.
In: Political analysis: official journal of the Society for Political Methodology, the Political Methodology Section of the American Political Science Association, Band 19, Heft 2, S. 135-147
ISSN: 1047-1987
In: Political analysis: official journal of the Society for Political Methodology, the Political Methodology Section of the American Political Science Association, Band 19, Heft 2, S. 170-173
ISSN: 1047-1987
SSRN
Working paper
In: IDS bulletin: transforming development knowledge, Band 35, Heft 3, S. 66-75
ISSN: 1759-5436
In: IDS bulletin, Band 35, Heft 3, S. 66-75
ISSN: 0265-5012, 0308-5872
In: The Economic Journal, Band 101, Heft 407, S. 1023
In: Working paper / United States International Trade Commission, Office of Economics 2007-05-A