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On ridge estimators for the negative binomial regression model
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
2012 (English)In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 29, no 2, 178-184 p.Article in journal (Refereed) Published
Abstract [en]

The negative binomial (NB) regression model is very popular in applied research when analyzing count data. The commonly used maximum likelihood (ML) estimator is very sensitive to highly intercorrelated explanatory variables. Therefore, a NB ridge regression estimator (NBRR) is proposed as a robust option of estimating the parameters of the NB model in the presence of multicollinearity. To investigate the performance of the NBRR and the traditional ML approach the mean squared error (MSE) is calculated using Monte Carlo simulations. The simulated result indicated that some of the proposed NBRR methods should always be preferred to the ML method.

Place, publisher, year, edition, pages
2012. Vol. 29, no 2, 178-184 p.
Keyword [en]
Negative binomial regression; Maximum likelihood; Ridge regression; MSE; Monte Carlo simulations; Multicollinearity
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:hj:diva-17738OAI: oai:DiVA.org:hj-17738DiVA: diva2:506837
Available from: 2012-03-01 Created: 2012-03-01 Last updated: 2016-10-13Bibliographically approved
In thesis
1. Issues of multicollinearity and conditional heteroscadasticity in time series econometrics
Open this publication in new window or tab >>Issues of multicollinearity and conditional heteroscadasticity in time series econometrics
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Jönköping: Jönköping International Business School, 2012. 19 p.
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-17739 (URN)9789186345273 (ISBN)
Public defence
2012-03-16, 09:41 (English)
Opponent
Supervisors
Available from: 2012-03-01 Created: 2012-03-01 Last updated: 2016-03-09Bibliographically approved
2. Issues of multicollinearity and conditional heteroscedasticy in time series econometrics
Open this publication in new window or tab >>Issues of multicollinearity and conditional heteroscedasticy in time series econometrics
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
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).

Place, publisher, year, edition, pages
Jönköping: Jönköping University, Jönköping International Business School, 2012. 15 p.
Series
JIBS Dissertation Series, ISSN 1403-0470 ; 075
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-31977 (URN)978-91-86345-27-3 (ISBN)
Supervisors
Available from: 2016-10-13 Created: 2016-10-13 Last updated: 2016-10-13Bibliographically approved

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