The Comparison of the Computer Numbers in the Houses in Turkey with the Pooled Count Data Models&lt;sup&gt;1&lt;/sup&gt;

Arzu Kokcen,Pinar Cevik,Senay Ucdogruk,Verda Davasligil Atmaca

doi:10.19026/crjss.6.5212

Abstract

The effect of informatics technologies upon our lives gradually increases in parallel with the significant inclusion of computer and internet in every area of life. As technological developments have a positive effect upon the informatics sector, the use of computers has become widespread in houses. The objective of this study is to explain the factors affecting the number of computers owned by the household with the help of Counting Data Models. In this context, the most convenient method was tried to be determined through comparing the Standard Poisson, Poisson Quasi Maximum Likelihood and Negative Binom regression models. Being prepared by the Turkish Statistical Institute (TUIK) between 2002-2010, the data of the ‘Household Budget Survey’ were used in the study.

Highlights

Because of the transition to information society today, using the information and communication technologies in economical and social life has become widespread
The computer which has an important share among information and communication technology products was first used by General Directorate for Highways2 in 1960
Poisson Regression Model is procured from Poisson distribution which expresses the relationship between mean parameter μ and co-variances x with parameters (Cameron and Trivedi, 2001): E y |x e β for i 1,2, ... , n

Summary

INTRODUCTION

Because of the transition to information society today, using the information and communication technologies in economical and social life has become widespread. Poisson Regression Model is procured from Poisson distribution which expresses the relationship between mean parameter μ and co-variances x with parameters (Cameron and Trivedi, 2001):. An important feature of Poisson Regression Model is that as far as mean function is determined properly, it gives consistent estimators for β even there is an over or an under dispersion. Poisson and Negative binomial Regression Models are separated from each other because of the differences in the assumptions about conditional mean and conditional variance. Our first attempt to Poisson regression model is to add the parameter that lets conditional variance of y exceed conditional mean This model is Negative Binomial Regression Model (NBRM). In NBRM the change in is originated from x change between individuals and the unobservable

Smaller group

Year dummies

Middle spend

Findings

Although the parameters belonging to Poisson