Special Issue "Econometric Model Selection"

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A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (1 December 2013)

Special Issue Editor

Guest Editor
Prof. Dr. Tomohiro Ando (Website)

Graduate School of Business Administration Keio University, 4-1-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa, 223-8526, Japan
Fax: +81 45 562 3502
Interests: bayesian econometrics; economic forecasting; financial econometrics; high-dimensional modeling; model selection; Monte Carlo simulation; supply and demand analysis

Special Issue Information

Dear Colleagues,

Model selection is fundamental part of the econometric modeling process. In principle, the econometric modeling is straightforward. Econometricians express their theoretical concepts and beliefs by specifying the structure of economic models. Parameter estimation is then implemented based on some inference procedures, including the maximum likelihood methods, generalized method of moments, Bayesian estimation, and so on. The results are then used for the decision making, forecasting, stochastic structure explorations and many other problems.
Usually, the quality of these solutions depends on the goodness of the constructed econometric models. More specifically, a range of different econometric model specifications can be considered and then an optimal model needs to be determined from a set of candidate econometric models. Together with the recent developments in information technology that permit the collection of high-dimensional data, this special issue will focus on econometric model selection theories and applications concerning the econometric analysis of high dimensional data.

The following list of potential topics is provided to stimulate ideas. Authors are not restricted to this list, but submissions must advance econometric modeling procedures and open new doors to applications.

Prof. Dr. Tomohiro Ando
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • bayesian models
  • consistency of model selection methods
  • empirical likelihood
  • econometric modeling
  • information criteria
  • moment restriction models
  • model averaging and uncertainty
  • model mis-specification
  • shrinkage methods
  • regularization

Published Papers (4 papers)

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Research

Open AccessArticle Polynomial Regressions and Nonsense Inference
Econometrics 2013, 1(3), 236-248; doi:10.3390/econometrics1030236
Received: 6 August 2013 / Revised: 28 October 2013 / Accepted: 7 November 2013 / Published: 18 November 2013
Cited by 1 | PDF Full-text (448 KB) | HTML Full-text | XML Full-text
Abstract
Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic [...] Read more.
Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340.) by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions. 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 [...] Read more.
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 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 [...] Read more.
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 On Diagnostic Checking of Vector ARMA-GARCH Models with Gaussian and Student-t Innovations
Econometrics 2013, 1(1), 1-31; doi:10.3390/econometrics1010001
Received: 15 January 2013 / Revised: 9 March 2013 / Accepted: 14 March 2013 / Published: 4 April 2013
PDF Full-text (382 KB) | HTML Full-text | XML Full-text
Abstract
This paper focuses on the diagnostic checking of vector ARMA (VARMA) models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the cross-product vector of standardized residuals. This [...] Read more.
This paper focuses on the diagnostic checking of vector ARMA (VARMA) models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the cross-product vector of standardized residuals. This is different from the traditional approach that employs only the squared series of standardized residuals. We then study two portmanteau statistics, called Q1(M) and Q2(M), for model checking. A residual-based bootstrap method is provided and demonstrated as an effective way to approximate the diagnostic checking statistics. Simulations are used to compare the performance of the proposed statistics with other methods available in the literature. In addition, we also investigate the effect of GARCH shocks on checking a fitted VARMA model. Empirical sizes and powers of the proposed statistics are investigated and the results suggest a procedure of using jointly Q1(M) and Q2(M) in diagnostic checking. The bivariate time series of FTSE 100 and DAX index returns is used to illustrate the performance of the proposed portmanteau statistics. The results show that it is important to consider the cross-product series of standardized residuals and GARCH effects in model checking. Full article
(This article belongs to the Special Issue Econometric Model Selection)

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