Modelling and Estimating Individual and Firm Effects with Count Panel Data

2015 ◽  
Author(s):  
Jean-Frannois Angers ◽  
Denise Desjardins ◽  
Georges Dionne ◽  
Frannois Guertin
Keyword(s):  
Panel Data ◽  
Count Panel Data

scholarly journals A Simple Approach to Fit the Beta-binomial Model

2005 ◽  
Vol 5 (3) ◽  
pp. 385-394 ◽  
Author(s):  
Paulo Guimarães
Keyword(s):  
Panel Data ◽  
Binomial Distribution ◽  
Regression Models ◽  
Multinomial Distribution ◽  
Simple Approach ◽  
Binomial Model ◽  
Multivariate Generalization ◽  
Count Panel Data

In this paper, I show how to estimate the parameters of the beta-binomial distribution and its multivariate generalization, the Dirichlet-multinomial distribution. This approach involves no additional programming, as it relies on an existing Stata command used for overdispersed count panel data. Including covariates to allow for regression models based in these distributions is straightforward.

MODELLING AND ESTIMATING INDIVIDUAL AND FIRM EFFECTS WITH COUNT PANEL DATA

2018 ◽  
Vol 48 (3) ◽  
pp. 1049-1078 ◽  
Author(s):  
Jean-François Angers ◽  
Denise Desjardins ◽  
Georges Dionne ◽  
François Guertin
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Parametric Model ◽  
Random Effects Model ◽  
Road Accidents ◽  
Fixed Effects Model ◽  
Insurance Pricing ◽  
Count Panel Data ◽  
Unobservable Factors

AbstractWe propose a new parametric model for the modelling and estimation of event distributions for individuals in different firms. The analysis uses panel data and takes into account individual and firm effects in a non-linear model. Non-observable factors are treated as random effects. In our application, the distribution of accidents is affected by observable and non-observable factors from vehicles, drivers and fleets of vehicles. Observable and unobservable factors are significant to explain road accidents, which mean that insurance pricing should take into account all these factors. A fixed effects model is also estimated to test the consistency of the random effects model.

Count Panel Data

2015 ◽  
Author(s):  
Badi H. Baltagi ◽  
A. Colin Cameron ◽  
Pravin K. Trivedi
Keyword(s):  
Panel Data ◽  
Count Panel Data

scholarly journals Score-driven dynamic patent count panel data models

2016 ◽  
Vol 149 ◽  
pp. 116-119 ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Count Panel Data
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