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A Ridge Regression estimator for the zero-inflated Poisson model

Author

Listed:
  • Kibria, B. M. Golam

    (Florida International University)

  • Månsson, Kristofer

    (Jönköping University)

  • Shukur, Ghazi

    (Linnaeus University)

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.

Suggested Citation

  • Kibria, B. M. Golam & Månsson, Kristofer & Shukur, Ghazi, 2011. "A Ridge Regression estimator for the zero-inflated Poisson model," Working Paper Series in Economics and Institutions of Innovation 257, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
  • Handle: RePEc:hhs:cesisp:0257
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    References listed on IDEAS

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    More about this item

    Keywords

    Count data; Innovation analysis; Multicollinearity; Zero Inflated Poisson; Ridge Regression;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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