Some Diagnostic Tools in Robust Econometrics

Jan Kalina

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica (2011)

  • Volume: 50, Issue: 2, page 55-67
  • ISSN: 0231-9721

Abstract

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Highly robust statistical and econometric methods have been developed not only as a diagnostic tool for standard methods, but they can be also used as self-standing methods for valid inference. Therefore the robust methods need to be equipped by their own diagnostic tools. This paper describes diagnostics for robust estimation of parameters in two econometric models derived from the linear regression. Both methods are special cases of the generalized method of moments estimator based on implicit weighting of individual observations. This has the effect of down-weighting less reliable observations and ensures a high robustness and low sub-sample sensitivity of the methods. Firstly, for a robust regression method efficient under heteroscedasticity we derive the Durbin–Watson test of independence of random regression errors, which is based on the approximation to the exact null distribution of the test statistic. Secondly we study the asymptotic behavior of the Durbin–Watson test statistic for the weighted instrumental variables estimator, which is a robust analogy of the classical instrumental variables estimator.

How to cite

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Kalina, Jan. "Some Diagnostic Tools in Robust Econometrics." Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica 50.2 (2011): 55-67. <http://eudml.org/doc/196316>.

@article{Kalina2011,
abstract = {Highly robust statistical and econometric methods have been developed not only as a diagnostic tool for standard methods, but they can be also used as self-standing methods for valid inference. Therefore the robust methods need to be equipped by their own diagnostic tools. This paper describes diagnostics for robust estimation of parameters in two econometric models derived from the linear regression. Both methods are special cases of the generalized method of moments estimator based on implicit weighting of individual observations. This has the effect of down-weighting less reliable observations and ensures a high robustness and low sub-sample sensitivity of the methods. Firstly, for a robust regression method efficient under heteroscedasticity we derive the Durbin–Watson test of independence of random regression errors, which is based on the approximation to the exact null distribution of the test statistic. Secondly we study the asymptotic behavior of the Durbin–Watson test statistic for the weighted instrumental variables estimator, which is a robust analogy of the classical instrumental variables estimator.},
author = {Kalina, Jan},
journal = {Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica},
keywords = {robust regression; autocorrelated errors; heteroscedastic regression; instrumental variables; least weighted squares; robust regression; autocorrelated errors; heteroscedastic regression; instrumental variables; least weighted squares},
language = {eng},
number = {2},
pages = {55-67},
publisher = {Palacký University Olomouc},
title = {Some Diagnostic Tools in Robust Econometrics},
url = {http://eudml.org/doc/196316},
volume = {50},
year = {2011},
}

TY - JOUR
AU - Kalina, Jan
TI - Some Diagnostic Tools in Robust Econometrics
JO - Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
PY - 2011
PB - Palacký University Olomouc
VL - 50
IS - 2
SP - 55
EP - 67
AB - Highly robust statistical and econometric methods have been developed not only as a diagnostic tool for standard methods, but they can be also used as self-standing methods for valid inference. Therefore the robust methods need to be equipped by their own diagnostic tools. This paper describes diagnostics for robust estimation of parameters in two econometric models derived from the linear regression. Both methods are special cases of the generalized method of moments estimator based on implicit weighting of individual observations. This has the effect of down-weighting less reliable observations and ensures a high robustness and low sub-sample sensitivity of the methods. Firstly, for a robust regression method efficient under heteroscedasticity we derive the Durbin–Watson test of independence of random regression errors, which is based on the approximation to the exact null distribution of the test statistic. Secondly we study the asymptotic behavior of the Durbin–Watson test statistic for the weighted instrumental variables estimator, which is a robust analogy of the classical instrumental variables estimator.
LA - eng
KW - robust regression; autocorrelated errors; heteroscedastic regression; instrumental variables; least weighted squares; robust regression; autocorrelated errors; heteroscedastic regression; instrumental variables; least weighted squares
UR - http://eudml.org/doc/196316
ER -

References

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