A Ridge Regression estimator for the zero-inflated Poisson model

B. M. Golam Kibria, Kristofer Månsson () and Ghazi Shukur
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B. M. Golam Kibria: Florida International University, Postal: Department of Mathematics and Statistics , Florida International University, Modesto A. Maidique Campus , Miami, Florida, USA

No 257, Working Paper Series in Economics and Institutions of Innovation from Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies

Abstract: The zero inflated Poisson regression model is very common when analysing economic data that comes in the form of non-negative integers since it accounts for excess zeros and over-dispersion of the dependent variable. This model may be used in innovation analysis to see for example the impact on different economic factors has on the number of patents of companies, how frequently mortgages or credit-card fail to meet their financial obligations, the amount of take-over bids firms receives and the number of products different firms export, etc. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression estimators and some methods of estimating the ridge parameter k for the non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both MSE and MAE are considered as performance criterion. The simulation study shows that some estimators are better than the commonly applied maximum likelihood estimator and some other ridge regression estimators. Some useful estimators are recommended for the practitioners.

Keywords: Count data; Innovation analysis; Multicollinearity; Zero Inflated Poisson; Ridge Regression (search for similar items in EconPapers)
JEL-codes: C13 C16 C31 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2011-10-18
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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