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Econometrics, Volume 1, Issue 2 (September 2013), Pages 141-206

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Research

Open AccessArticle Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging
Econometrics 2013, 1(2), 141-156; doi:10.3390/econometrics1020141
Received: 13 May 2013 / Revised: 26 June 2013 / Accepted: 27 June 2013 / Published: 3 July 2013
Cited by 4 | PDF Full-text (433 KB) | HTML Full-text | XML Full-text
Abstract
This paper develops model selection and averaging methods for moment restriction models. We first propose a focused information criterion based on the generalized empirical likelihood estimator. We address the issue of selecting an optimal model, rather than a correct model, for estimating a
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This paper develops model selection and averaging methods for moment restriction models. We first propose a focused information criterion based on the generalized empirical likelihood estimator. We address the issue of selecting an optimal model, rather than a correct model, for estimating a specific parameter of interest. Then, this study investigates a generalized empirical likelihood-based model averaging estimator that minimizes the asymptotic mean squared error. A simulation study suggests that our averaging estimator can be a useful alternative to existing post-selection estimators. Full article
(This article belongs to the Special Issue Econometric Model Selection)
Open AccessArticle Parametric and Nonparametric Frequentist Model Selection and Model Averaging
Econometrics 2013, 1(2), 157-179; doi:10.3390/econometrics1020157
Received: 27 June 2013 / Revised: 17 July 2013 / Accepted: 13 September 2013 / Published: 20 September 2013
Cited by 1 | PDF Full-text (452 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference
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This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated. Full article
(This article belongs to the Special Issue Econometric Model Selection)
Open AccessFeature PaperArticle Structural Panel VARs
Econometrics 2013, 1(2), 180-206; doi:10.3390/econometrics1020180
Received: 30 May 2013 / Revised: 6 August 2013 / Accepted: 20 August 2013 / Published: 24 September 2013
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Abstract
The paper proposes a structural approach to VAR analysis in panels, which takes into account responses to both idiosyncratic and common structural shocks, while permitting full cross member heterogeneity of the response dynamics. In the context of this structural approach, estimation of the
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The paper proposes a structural approach to VAR analysis in panels, which takes into account responses to both idiosyncratic and common structural shocks, while permitting full cross member heterogeneity of the response dynamics. In the context of this structural approach, estimation of the loading matrices for the decomposition into idiosyncratic versus common shocks is straightforward and transparent. The method appears to do remarkably well at uncovering the properties of the sample distribution of the underlying structural dynamics, even when the panels are relatively short, as illustrated in Monte Carlo simulations. Finally, these simulations also illustrate that the SVAR panel method can be used to improve inference, not only for properties of the sample distribution, but also for dynamics of individual members of the panel that lack adequate data for a conventional time series SVAR analysis. This is accomplished by using fitted cross sectional regressions of the sample of estimated panel responses to correlated static measures, and using these to interpolate the member-specific dynamics. Full article
(This article belongs to the Special Issue Panel Time Series Methods)

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