Special Issue "Recent Developments of Specification Testing"

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

Deadline for manuscript submissions: closed (31 December 2015).

Special Issue Editor

Guest Editor
Prof. Yongmiao Hong

Department of Economics and Department of Statistical Science, 424 Uris Hall, Cornell University, Ithaca, NY 14850, USA
Website | E-Mail
Interests: model specification testing, nonlinear time series analysis, locally stationary time series analysis, generalized spectral analysis, financial econometrics, and modeling interval-valued time series data

Special Issue Information

Dear Colleagues,

It is well known that model misspecification has important implications on the inference of and interpretation of econometric models. Specification testing plays an important role in econometric modeling and model evaluation. Substantial achievements on the theory and methods of specification testing have been made over the past three decades or so. Modern empirical studies often include the testing of various model misspecifications, and the tool kits of specification tests that are available to applied econometricians have increased enormously.

The main purpose of this Special Issue is to further promote research in this important area. We welcome the submission of high quality, original research in this field, including studies concerning new specification testing theories and methods for various econometrics models, ranging from those concerning the conditional mean to entire conditional distributions, in the contexts of cross-sectional, time series, and panel data, respectively. Both parametric and nonparametric testing approaches are encouraged, and solid applications of specification testing to modeling real economic and financial data are also welcome.

Prof. Yongmiao Hong
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind 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. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords

  • empirical analysis
  • model misspecification
  • nonparametric approach
  • parametric approach
  • specification analysis
  • specification test

Published Papers (4 papers)

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Research

Open AccessArticle
Generalized Information Matrix Tests for Detecting Model Misspecification
Econometrics 2016, 4(4), 46; https://doi.org/10.3390/econometrics4040046
Received: 29 December 2015 / Revised: 13 September 2016 / Accepted: 26 October 2016 / Published: 15 November 2016
Cited by 4 | PDF Full-text (1052 KB) | HTML Full-text | XML Full-text
Abstract
Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying, classifying, and deriving [...] Read more.
Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying, classifying, and deriving novel model misspecification tests for finite-dimensional smooth probability models. These GIMTs include previously published as well as newly developed information matrix tests. To illustrate the application of the GIMT framework, we derived and assessed the performance of new GIMTs for binary logistic regression. Although all GIMTs exhibited good level and power performance for the larger sample sizes, GIMT statistics with fewer degrees of freedom and derived using log-likelihood third derivatives exhibited improved level and power performance. Full article
(This article belongs to the Special Issue Recent Developments of Specification Testing)
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Open AccessArticle
Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables
Econometrics 2016, 4(2), 29; https://doi.org/10.3390/econometrics4020029
Received: 23 December 2015 / Revised: 12 May 2016 / Accepted: 30 May 2016 / Published: 21 June 2016
Cited by 5 | PDF Full-text (447 KB) | HTML Full-text | XML Full-text
Abstract
The Ramsey regression equation specification error test (RESET) furnishes a diagnostic for omitted variables in a linear regression model specification (i.e., the null hypothesis is no omitted variables). Integer powers of fitted values from a regression analysis are introduced as additional [...] Read more.
The Ramsey regression equation specification error test (RESET) furnishes a diagnostic for omitted variables in a linear regression model specification (i.e., the null hypothesis is no omitted variables). Integer powers of fitted values from a regression analysis are introduced as additional covariates in a second regression analysis. The former regression model can be considered restricted, whereas the latter model can be considered unrestricted; this first model is nested within this second model. A RESET significance test is conducted with an F-test using the error sums of squares and the degrees of freedom for the two models. For georeferenced data, eigenvectors can be extracted from a modified spatial weights matrix, and included in a linear regression model specification to account for the presence of nonzero spatial autocorrelation. The intuition underlying this methodology is that these synthetic variates function as surrogates for omitted variables. Accordingly, a restricted regression model without eigenvectors should indicate an omitted variables problem, whereas an unrestricted regression model with eigenvectors should result in a failure to reject the RESET null hypothesis. This paper furnishes eleven empirical examples, covering a wide range of spatial attribute data types, that illustrate the effectiveness of eigenvector spatial filtering in addressing the omitted variables problem for georeferenced data as measured by the RESET. Full article
(This article belongs to the Special Issue Recent Developments of Specification Testing)
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Open AccessArticle
Bootstrap Tests for Overidentification in Linear Regression Models
Econometrics 2015, 3(4), 825-863; https://doi.org/10.3390/econometrics3040825
Received: 13 July 2015 / Revised: 24 November 2015 / Accepted: 24 November 2015 / Published: 9 December 2015
Cited by 1 | PDF Full-text (580 KB) | HTML Full-text | XML Full-text
Abstract
We study the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent [...] Read more.
We study the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The distributions of the statistics are shown to have an ill-defined limit as the parameter that determines the strength of the instruments tends to zero and as the correlation between the disturbances of the structural and reduced-form equations tends to plus or minus one. This makes it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are not too weak. We also study their power properties. Full article
(This article belongs to the Special Issue Recent Developments of Specification Testing)
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Open AccessArticle
A Joint Specification Test for Response Probabilities in Unordered Multinomial Choice Models
Econometrics 2015, 3(3), 667-697; https://doi.org/10.3390/econometrics3030667
Received: 4 June 2015 / Revised: 28 August 2015 / Accepted: 9 September 2015 / Published: 16 September 2015
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Abstract
Estimation results obtained by parametric models may be seriously misleading when the model is misspecified or poorly approximates the true model. This study proposes a test that jointly tests the specifications of multiple response probabilities in unordered multinomial choice models. The test statistic [...] Read more.
Estimation results obtained by parametric models may be seriously misleading when the model is misspecified or poorly approximates the true model. This study proposes a test that jointly tests the specifications of multiple response probabilities in unordered multinomial choice models. The test statistic is asymptotically chi-square distributed, consistent against a fixed alternative and able to detect a local alternative approaching to the null at a rate slower than the parametric rate. We show that rejection regions can be calculated by a simple parametric bootstrap procedure, when the sample size is small. The size and power of the tests are investigated by Monte Carlo experiments. Full article
(This article belongs to the Special Issue Recent Developments of Specification Testing)
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