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Developing a Liu estimator for the negative binomial regression model: method and application
Högskolan i Jönköping, Internationella Handelshögskolan, IHH, Statistik.
2013 (engelsk)Inngår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 83, nr 9, s. 1773-1780Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper introduces a new shrinkage estimator for the negative binomial regression model that is a generalization of the estimator proposed for the linear regression model by Liu [A new class of biased estimate in linear regression, Comm. Stat. Theor. Meth. 22 (1993), pp. 393–402]. This shrinkage estimator is proposed in order to solve the problem of an inflated mean squared error of the classical maximum likelihood (ML) method in the presence of multicollinearity. Furthermore, the paper presents some methods of estimating the shrinkage parameter. By means of Monte Carlo simulations, it is shown that if the Liu estimator is applied with these shrinkage parameters, it always outperforms ML. The benefit of the new estimation method is also illustrated in an empirical application. Finally, based on the results from the simulation study and the empirical application, a recommendation regarding which estimator of the shrinkage parameter that should be used is given.

sted, utgiver, år, opplag, sider
2013. Vol. 83, nr 9, s. 1773-1780
Emneord [en]
negative binomial regression, maximum likelihood, Liu, MSE, Monte Carlo simulations, multicollinearity, Primary 62J07, Secondary 62J02
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-29100DOI: 10.1080/00949655.2012.673127ISI: 000324088300013Scopus ID: 2-s2.0-84884258133OAI: oai:DiVA.org:hj-29100DiVA, id: diva2:894553
Tilgjengelig fra: 2016-01-15 Laget: 2016-01-15 Sist oppdatert: 2017-11-30bibliografisk kontrollert
Inngår i avhandling
1. Issues of multicollinearity and conditional heteroscedasticy in time series econometrics
Åpne denne publikasjonen i ny fane eller vindu >>Issues of multicollinearity and conditional heteroscedasticy in time series econometrics
2012 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This doctoral thesis consists of four chapters all related to the field of time series econometrics. The main contribution is firstly the development of robust methods when testing for Granger causality in the presence of generalized autoregressive conditional heteroscedasticity (GARCH) and causality-in-variance (i.e. spillover) effects. The second contribution is the development of different shrinkage estimators for count data models which may be used when the explanatory variables are highly inter-correlated.

The first essay investigated the effect of spillover on some tests for causality in a Granger sense. As a remedy to the problem of over-rejection caused by the spillover effects White’s heteroscedasticity consistent covariance matrix is proposed. In the second essay the effect of GARCH errors on the statistical tests for Granger causality is investigated. Here some wavelet denoising methods are proposed and by means of Monte Carlo simulations it is shown that the size properties of the tests based on wavelet filtered data is better than the ones based on raw data.

In the third and fourth essays ridge regression estimators for the Poisson and negative binomial (NB) regression models are investigated respectively. Then finally in the fifth essaya Liu type of estimator is proposed for the NB regression model. By using Monte Carlo simulations it is shown that the estimated MSE is lower for the ridge and Liu type of estimators than maximum likelihood (ML).

sted, utgiver, år, opplag, sider
Jönköping: Jönköping University, Jönköping International Business School, 2012. s. 15
Serie
JIBS Dissertation Series, ISSN 1403-0470 ; 075
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-31977 (URN)978-91-86345-27-3 (ISBN)
Veileder
Tilgjengelig fra: 2016-10-13 Laget: 2016-10-13 Sist oppdatert: 2016-10-13bibliografisk kontrollert

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