Hausman Tests for Inefficient Estimators: Application to Demand for Health Care Service (revised)
AbstractThe Hausman (1978) test is based on the vector of differences of two estimators. It is usually assumed that one of the estimators is fully efficient, since this simplifies calculation of the test statistic. However, this assumption limits the applicability of the test, since widely used estimators such as the generalized method of moments (GMM) or quasi maximum likelihood (QML) are often not fully efficient. This paper shows that the test may easily be implemented, using well-known methods, when neither estimator is efficient. To illustrate, we present both simulation results as well as empirical results for utilization of health care services.
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Bibliographic InfoPaper provided by Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) in its series UFAE and IAE Working Papers with number 509.02.
Date of creation: 16 Apr 2002
Date of revision:
Hausman test; specification testing; health care utilization;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- I10 - Health, Education, and Welfare - - Health - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-04-25 (All new papers)
- NEP-ECM-2002-04-25 (Econometrics)
- NEP-IFN-2002-04-25 (International Finance)
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- Burnside, Craig & Eichenbaum, Martin S, 1996. "Small-Sample Properties of GMM-Based Wald Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 294-308, July.
- van Ophem, Hans, 2000. "Modeling Selectivity in Count-Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 503-11, October.
- Ruud, Paul A., 1984. "Tests of Specification in Econometrics," Department of Economics, Working Paper Series qt4kq8m0hf, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
- Andr? Romeu-Santana & ?gel M. Vera-Hern?dez, .
"A Semi-Nonparametric Estimator For Counts With An Endogenous Dummy. Variable,"
UFAE and IAE Working Papers
452.00, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
- Andres Romeu-Santana & Angel Marcos Vera-Hernndez, 2000. "A Semi-Nonparametric Estimator For Counts With An Endogenous Dummy Variable," Computing in Economics and Finance 2000 37, Society for Computational Economics.
- Whitney K. Newey & Kenneth D. West, 1986.
"A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix,"
NBER Technical Working Papers
0055, National Bureau of Economic Research, Inc.
- Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
- Donald W.K. Andrews & Christopher J. Monahan, 1990.
"An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator,"
Cowles Foundation Discussion Papers
942, Cowles Foundation for Research in Economics, Yale University.
- Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-66, July.
- Hausman, Jerry A, 1978. "Specification Tests in Econometrics," Econometrica, Econometric Society, vol. 46(6), pages 1251-71, November.
- Winfried Pohlmeier & Volker Ulrich, 1995. "An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care," Journal of Human Resources, University of Wisconsin Press, vol. 30(2), pages 339-361.
- Browning, Martin & Meghir, Costas, 1991. "The Effects of Male and Female Labor Supply on Commodity Demands," Econometrica, Econometric Society, vol. 59(4), pages 925-51, July.
- Newey, Whitney K, 1985. "Maximum Likelihood Specification Testing and Conditional Moment Tests," Econometrica, Econometric Society, vol. 53(5), pages 1047-70, September.
- Terza, Joseph V., 1998. "Estimating count data models with endogenous switching: Sample selection and endogenous treatment effects," Journal of Econometrics, Elsevier, vol. 84(1), pages 129-154, May.
- Gurmu, Shiferaw, 1997. "Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 225-43, May-June.
- Partha Deb & Ann M. Holmes, 2000. "Estimates of use and costs of behavioural health care: a comparison of standard and finite mixture models," Health Economics, John Wiley & Sons, Ltd., vol. 9(6), pages 475-489.
- Windmeijer, F A G & Silva, J M C Santos, 1997.
"Endogeneity in Count Data Models: An Application to Demand for Health Care,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 12(3), pages 281-94, May-June.
- Frank Windmeijer & Joao Santos Silva, 1996. "Endogeneity in count data models; an application to demand for health care," IFS Working Papers W96/15, Institute for Fiscal Studies.
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