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	Provedor de dados 31 (3) Autor Spanos, Aris (3) McGuirk, Anya M. (2) Bergtold, Jason S. (1) Palavra-chave Research Methods/ Statistical Methods (3) Bernoulli Regression Model (1) Discrete choice (1) Generalized linear models (1) Logistic regression (1) Model specification (1) Probabilistic reduction approach (1) Mais... Tipo do documento Conference Paper or Presentation (3) Ano 2005 (1) 2004 (1) 2002 (1) País United States (3) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última Bernoulli Regression Models: Re-examining Statistical Models with Binary Dependent Variables 31 Bergtold, Jason S.; Spanos, Aris. The classical approach for specifying statistical models with binary dependent variables in econometrics using latent variables or threshold models can leave the model misspecified, resulting in biased and inconsistent estimates as well as erroneous inferences. Furthermore, methods for trying to alleviate such problems, such as univariate generalized linear models, have not provided an adequate alternative for ensuring the statistical adequacy of such models. The purpose of this paper is to re-examine the underlying probabilistic foundations of statistical models with binary dependent variables using the probabilistic reduction approach to provide an alternative approach for model specification. This re-examination leads to the development of the Bernoulli... Tipo: Conference Paper or Presentation Palavras-chave: Bernoulli Regression Model; Logistic regression; Generalized linear models; Discrete choice; Probabilistic reduction approach; Model specification; Research Methods/ Statistical Methods. Ano: 2005 URL: http://purl.umn.edu/19282 REVISITING ERROR AUTOCORRELATION CORRECTION: COMMON FACTOR RESTRICTIONS AND GRANGER CAUSALITY 31 McGuirk, Anya M.; Spanos, Aris. This paper demonstrates that linear regression models with an AR(1) error structure implicitly assume that y{t} does not Granger cause any of the exogenous variables in X{t}. An indirect test of the common factor restrictions based on this Granger non-causality is proposed and shown to outperform existing tests. Tipo: Conference Paper or Presentation Palavras-chave: Research Methods/ Statistical Methods. Ano: 2004 URL: http://purl.umn.edu/20176 THE LINEAR REGRESSION MODEL WITH AUTOCORRELATED ERRORS: JUST SAY NO TO ERROR AUTOCORRELATION 31 McGuirk, Anya M.; Spanos, Aris. This paper focuses on the practice of serial correlation correcting of the Linear Regression Model (LRM) by modeling the error. Simple Monte Carlo experiments are used to demonstrate the following points regarding this practice. First, the common factor restrictions implicitly imposed on the temporal structure of yt and xt appear to be completely unreasonable for any real world application. Second, when one compares the Autocorrelation-Corrected LRM (ACLRM) model estimates with estimates from the (unrestricted) Dynamic Linear Regression Model (DLRM) encompassing the ACLRM there is no significant gain in efficiency! Third, as expected, when the common factor restrictions do not hold the LRM model gives poor estimates of the true parameters and estimation of... Tipo: Conference Paper or Presentation Palavras-chave: Research Methods/ Statistical Methods. Ano: 2002 URL: http://purl.umn.edu/19905 Registros recuperados: 3 Primeira ... 1 ... Última

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