Selecting Estimated Models Using Chi-Square Statistics

Vuong,; Wang, Hong

doi:10.2307/20075910

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…When observations are i.i.d. and the competing models are nondynamic, constructing a consistent estimator of the asymptotic variance σ n 2 is straightforward (see Vuong (1989), Vuong and Wang (1991), Vuong and Wang (1993a), Vuong and Wang (1993b)). This is because and Y nt j do not depend on n and because the (q + k)‐dimensional vectors appearing in (8) are i.i.d.…”

Section: Consistent Variance Estimationmentioning

confidence: 99%

Model selection tests for nonlinear dynamic models

Rivers

Vuong²

2002

The Econometrics Journal

265

251

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This paper generalizes Vuong (1989) asymptotically normal tests for model selection in several important directions. First, it allows for incompletely parametrized models such as econometric models defined by moment conditions. Second, it allows for a broad class of estimation methods that includes most estimators currently used in practice. Third, it considers model selection criteria other than the models' likelihoods such as the mean squared errors of prediction. Fourth, the proposed tests are applicable to possibly misspecified nonlinear dynamic models with weakly dependent heterogeneous data. Cases where the estimation methods optimize the model selection criteria are distinguished from cases where they do not. We also consider the estimation of the asymptotic variance of the difference between the competing models' selection criteria, which is necessary to our tests. Finally, we discuss conditions under which our tests are valid. It is seen that the competing models must be essentially nonnested.

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Section: Consistent Variance Estimationmentioning

confidence: 99%

Model selection tests for nonlinear dynamic models

Rivers

Vuong²

2002

The Econometrics Journal

265

251

View full text Add to dashboard Cite

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Identifying Export Industries Using Parametric Density Functions

Nichols

Mushinski

2003

International Regional Science Review

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In this article, the authors present a new way of identifying the set of industries that may constitute a region’s economic base. The authors focus on the differences among regions in their regional employment shares (the percentage of total employment in a particular region attributable to a particular industry). They find that the distribution across regions of regional employment shares can be characterized by what they call a mixed-exponential distribution for industries that are easy to classify as being export industries—such as automotive manufacturing—while the distributions of regional employment shares for some easy-to-classify local industries tend to be normal. The authors then attempt to classify each of the remaining industries as being either export or local to determine whether the empirical distribution of employment shares in each industry is more like the mixed-exponential distribution or more like the normal distribution. The attempt is partially successful.

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Selecting Estimated Models Using Chi-Square Statistics

Cited by 2 publications

References 9 publications

Model selection tests for nonlinear dynamic models

Model selection tests for nonlinear dynamic models

Identifying Export Industries Using Parametric Density Functions

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