Poisson regression
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Poisson Regression. A presentation by Jeffry A. Jacob Fall 2002 Eco 6375. Poisson Distribution. A Poisson distribution is given by:. Where, is the average number of occurrences in a specified interval. Assumptions: Independence

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Poisson Regression

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Poisson regression

Poisson Regression

A presentation by Jeffry A. Jacob

Fall 2002

Eco 6375


Poisson distribution

Poisson Distribution

  • A Poisson distribution is given by:

Where, is the average number of occurrences in a specified interval

  • Assumptions:

  • Independence

  • Prob. of occurrence In a short interval is proportional to the length of the interval

  • Prob. of another occurrence in such a short interval is zero


Poisson model

Poisson Model

  • The dependent variable is a count variable taking small values (less than 100).

  • It has been proposed that the count dependent variable follows a Poisson process whose parameters are determined by the exogenous variables and the coefficients

  • Justified when the variable considered describes the number of occurrences of an event in a give time span eg. # of job-related accidents=f(factory charact.), ship damage=f(type, yr.con., pd.op.)


Specification of the model

Specification of the Model

  • The primary equation of the model is

  • The most common formulation of this model is the log-linear specification:

  • The expected number of events per period is given by


Specification

Specification….

  • The major assumption of the Poisson model is :

  • Thus:

  • Later on when we do diagnostic testing, we will test this assumption. It is called testing for over-dispersion (if Var[y]>E[y]) or under-

  • dispersion (if Var[y]<E[y])


Estimation

Estimation

  • We estimate the model using MLE. The Likelihood function is non linear:

  • The parameters of this equation can be estimated using maximum likelihood method

  • Note that the log-likelihood function is concave in and has a unique maxima. (Gourieroux[1991])


Estimation1

Estimation….

  • The Hessian of this function is:

  • From this, we can get the asymptotic variance- covariance matrix of the ML estimator:

  • Finally, we use the Newton-Raphson iteration to find the parameter estimates:


Interpretation of the coefficients

Interpretation of the coefficients

  • Once we obtain the parameter estimates, i.e. estimates , we can calculate the conditional mean:

Which gives us the expected number of eventsper period.

  • Further, if xik is the log of an economic variable, i.e. xik = logXki, can be interpreted as an elasticity


Diagnostic testing

Diagnostic Testing

  • As we had mentioned before, a major assumption of the Poisson model is:

  • Here the diagnostic tests are concerned with checking for this assumption

  • Cameron and Trivedi (1990) test H0 : Var (yi) = H1 : Var (yi) = + g( ), usually g( )= or

  • Test for over or under dispersion is =0 in

We check the t-ratio for


Diagnostic testing1

Diagnostic Testing…

  • An alternative approach is by Wooldridge(1996) which involves regressing the square of standardized residuals-1 on the forecasted value and testing alpha = 0 in the following test equation

  • In case of miss-specification, we can compute QMLestimators, which are robust – they are consistent estimates as long as the conditional mean in correctlyspecified, even if the distribution is incorrectlyspecified.


Diagnostic testing2

Diagnostic Testing…

  • With miss-specification, the std errors will not be consistent. We can compute robust std errors using Huber/White (QML) option or GLM , which corrects the std errors for miss-specification.

  • For Poisson, MLE are also QMLE

  • The respective std errors are:

And,

Where,


Empirical examples

Empirical Examples

Done in Eviews


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