The COUNTREG Procedure

Panel Data Analysis

Subsections:

Panel Data Poisson Regression with Fixed Effects

The count regression model for panel data can be derived from the Poisson regression model. Consider the multiplicative one-way panel data model,

\[ y_{it} \sim \mbox{Poisson}(\mu _{it}) \]

where

\[ \mu _{it} = \alpha _{i} \lambda _{it} = \alpha _{i} \exp (\mathbf{x}_{it}’\bbeta ),\; \; i=1,\ldots ,N, \; \; t=1,\ldots ,T \]

Here, $\alpha _{i}$ are the individual effects.

In the fixed-effects model, the $\alpha _{i}$ are unknown parameters. The fixed-effects model can be estimated by eliminating $\alpha _{i}$ by conditioning on $\sum _{t} y_{it}$.

In the random-effects model, the $\alpha _{i}$ are independent and identically distributed (iid) random variables, in contrast to the fixed effects model. The random-effects model can then be estimated by assuming a distribution for $\alpha _{i}$.

In the Poisson fixed-effects model, conditional on $\lambda _{it}$ and parameter $\alpha _{i}$, $y_{it}$ is iid Poisson-distributed with parameter $\mu _{it}=\alpha _{i}\lambda _{it}=\alpha _{i} \exp (\mathbf{x}_{it}’\bbeta )$, and $x_{it}$ does not include an intercept. Then, the conditional joint density for the outcomes within the ith panel is

\begin{eqnarray*} P[y_{i1},\ldots ,y_{iT_{i}}|\sum _{t=1}^{T_{i}}y_{it}] & = & P[y_{i1},\ldots ,y_{iT_{i}},\sum _{t=1}^{T_{i}}y_{it}] / P[\sum _{t=1}^{T_{i}}y_{it}] \\ & = & P[y_{i1},\ldots ,y_{iT_{i}}]/P[\sum _{t=1}^{T_{i}}y_{it}] \end{eqnarray*}

Because $y_{it}$ is iid Poisson($\mu _{it}$), $P[y_{i1},\ldots ,y_{iT_{i}}]$ is the product of $T_{i}$ Poisson densities. Also, $(\sum _{t=1}^{T_{i}} y_{it})$ is Poisson($\sum _{t=1}^{T_{i}} \mu _{it}$). Then,

\begin{eqnarray*} P[y_{i1},\ldots ,y_{iT_{i}}|\sum _{t=1}^{T_{i}}y_{it}] & = & \frac{\sum _{t=1}^{T_{i}} (\exp (-\mu _{it}) \mu _{it}^{y_{it}} / y_{it}! )}{\exp (-\sum _{t=1}^{T_{i}} \mu _{it}) \left( \sum _{t=1}^{T_{i}} \mu _{it} \right)^{\sum _{t=1}^{T_{i}} y_{it} } / \left( \sum _{t=1}^{T_{i}} y_{it} \right)!} \\ & = & \frac{\exp (-\sum _{t=1}^{T_{i}} \mu _{it}) \left( \prod _{t=1}^{T_{i}} \mu _{it}^{y_{it}} \right) \left( \prod _{t=1}^{T_{i}} y_{it}! \right) }{\exp ( -\sum _{t=1}^{T_{i}} \mu _{it}) \prod _{t=1}^{T_{i}} \left( \sum _{s=1}^{T_{i}} \mu _{is} \right)^{y_{it}} / \left( \sum _{t=1}^{T_{i}} y_{it} \right)!} \\ & = & \frac{(\sum _{t=1}^{T_{i}} y_{it})!}{(\prod _{t=1}^{T_{i}} y_{it}!)} \prod _{t=1}^{T_{i}} \left(\frac{\mu _{it}}{\sum _{s=1}^{T_{i}} \mu _{is}}\right)^{y_{it}} \\ & = & \frac{(\sum _{t=1}^{T_{i}} y_{it})!}{(\prod _{t=1}^{T_{i}} y_{it}!)} \prod _{t=1}^{T_{i}} \left(\frac{\lambda _{it}}{\sum _{s=1}^{T_{i}} \lambda _{is}}\right)^{y_{it}} \\ \end{eqnarray*}

Thus, the conditional log-likelihood function of the fixed-effects Poisson model is given by

\[ \mathcal{L} = \sum _{i=1}^{N} \left[ \ln \left( (\sum _{t=1}^{T_{i}}y_{it})! \right) - \sum _{t=1}^{T_{i}}\ln (y_{it}!) + \sum _{t=1}^{T_{i}}y_{it}\ln \left(\frac{\lambda _{it}}{\sum _{s=1}^{T_{i}}\lambda _{is}}\right) \right] \]

The gradient is

\begin{eqnarray*} \frac{\partial \mathcal{L}}{\partial \bbeta } & = & \sum _{i=1}^{N} \sum _{t=1}^{T_{i}} y_{it}x_{it} - \sum _{i=1}^{N} \sum _{t=1}^{T_{i}} \left[ \frac{y_{it} \sum _{s=1}^{T_{i}} \left( \exp (\mathbf{x}_{is}'\bbeta ) \mathbf{x}_{is} \right)}{\sum _{s=1}^{T_{i}} \exp (\mathbf{x}_{is}'\bbeta )} \right] \\ & = & \sum _{i=1}^{N} \sum _{t=1}^{T_{i}} y_{it} (\mathbf{x}_{it}-\mathbf{\bar{x}}_{i}) \end{eqnarray*}

where

\[ \mathbf{\bar{x}}_{i} = \sum _{s=1}^{T_{i}} \left( \frac{\exp (\mathbf{x}_{is}'\bbeta )}{\sum _{k=1}^{T_{i}} \exp (\mathbf{x}_{ik}'\bbeta )} \right) \mathbf{x}_{is} \]

Panel Data Poisson Regression with Random Effects

In the Poisson random-effects model, conditional on $\lambda _{it}$ and parameter $\alpha _{i}$, $y_{it}$ is iid Poisson-distributed with parameter $\mu _{it}=\alpha _{i}\lambda _{it}=\alpha _{i} \exp (\mathbf{x}_{it}’\bbeta )$, and the individual effects, $\alpha _{i}$, are assumed to be iid random variables. The joint density for observations in all time periods for the ith individual, $P[y_{i1},\ldots ,y_{iT}|\lambda _{i1},\ldots ,\lambda _{iT_{i}}]$, can be obtained after the density $g(\alpha )$ of $\alpha {_ i}$ is specified.

Let

\[ \alpha _{i} \sim \mbox{iid}\; \mathrm{gamma}(\theta ,\theta ) \]

so that $E(\alpha _{i})=1$ and $V(\alpha _{i}) = 1/\theta $:

\[ g(\alpha _{i}) = \frac{\theta ^{\theta }}{\Gamma (\theta )} \alpha _{i}^{\theta -1}\exp (-\theta \alpha _{i}) \]

Let $\lambda _{i} = (\lambda _{i1},\ldots ,\lambda _{iT_{i}})$. Because $y_{it}$ is conditional on $\lambda _{it}$ and parameter $\alpha _{i}$ is iid Poisson($\mu _{it}=\alpha _{i} \lambda _{it}$), the conditional joint probability for observations in all time periods for the ith individual, $P[y_{i1},\ldots ,y_{iT_{i}}|\lambda _{i},\alpha _{i}]$, is the product of $T_{i}$ Poisson densities:

\begin{eqnarray*} P[y_{i1},\ldots ,y_{iT_{i}}|\lambda _{i},\alpha _{i}] & = & \prod _{t=1}^{T_{i}} P[y_{it}| \lambda _{i}, \alpha _{i}]\\ & = & \prod _{t=1}^{T_{i}}\left[ \frac{\exp (-\mu _{it}) \mu _{it}^{y_{it}}}{y_{it}!} \right] \\ & = & \left[ \prod _{t=1}^{T_{i}} \frac{e^{-\alpha _{i}\lambda _{it}}(\alpha _{i}\lambda _{it})^{y_{it}}}{y_{it}!} \right] \\ & = & \left[ \prod _{t=1}^{T_{i}} \lambda _{it}^{y_{it}}/y_{it}! \right] \left( e^{-\alpha _{i} \sum _{t} \lambda _{it}} \alpha _{i}^{\sum _{t} y_{it}} \right) \end{eqnarray*}

Then, the joint density for the ith panel conditional on just the $\lambda $ can be obtained by integrating out $\alpha _{i}$:

\begin{eqnarray*} P[y_{i1},\ldots ,y_{iT_{i}}|\lambda _{i}] & = & \int _{0}^{\infty } P[y_{i1},\ldots ,y_{iT}|\lambda _{i},\alpha _{i}] g(\alpha _{i}) d\alpha _{i} \\ & = & \frac{\theta ^{\theta }}{\Gamma (\theta )} \left[ \prod _{t=1}^{T_{i}} \frac{\lambda _{it}^{y_{it}}}{y_{it}!} \right] \int _{0}^{\infty } \exp (-\alpha _{i} \sum _{t} \lambda _{it}) \alpha _{i}^{\sum _{t} y_{it}} \alpha _{i}^{\theta -1} \exp (-\theta \alpha _{i}) d\alpha _{i} \\ & = & \frac{\theta ^{\theta }}{\Gamma (\theta )} \left[ \prod _{t=1}^{T_{i}} \frac{\lambda _{it}^{y_{it}}}{y_{it}!} \right] \int _{0}^{\infty } \exp \left[ -\alpha _{i} \left( \theta + \sum _{t} \lambda _{it} \right) \right] \alpha _{i}^{\theta + \sum _{t} y_{it}-1} d\alpha _{i} \\ & = & \left[ \prod _{t=1}^{T_{i}} \frac{\lambda _{it}^{y_{it}}}{y_{it}!} \right] \frac{\Gamma (\theta + \sum _{t} y_{it})}{\Gamma (\theta )} \\ & & \times \left(\frac{\theta }{\theta +\sum _{t} \lambda _{it}} \right)^{\theta } \left(\theta + \sum _{t} \lambda _{it} \right)^{-\sum _{t} y_{it}} \\ & = & \left[ \prod _{t=1}^{T_{i}} \frac{\lambda _{it}^{y_{it}}}{y_{it}!} \right] \frac{\Gamma (\alpha ^{-1}+ \sum _{t} y_{it})}{\Gamma (\alpha ^{-1})} \\ & & \times \left(\frac{\alpha ^{-1}}{\alpha ^{-1}+\sum _{t} \lambda _{it}} \right)^{\alpha ^{-1}} \left(\alpha ^{-1} + \sum _{t} \lambda _{it} \right)^{-\sum _{t} y_{it}} \end{eqnarray*}

where $\alpha (=1/\theta )$ is the overdispersion parameter. This is the density of the Poisson random-effects model with gamma-distributed random effects. For this distribution, $E(y_{it})=\lambda _{it}$ and $V(y_{it})=\lambda _{it} +\alpha \lambda _{it}^{2}$; that is, there is overdispersion.

Then the log-likelihood function is written as

\begin{eqnarray*} \mathcal{L} & = & \sum _{i=1}^{N} \left\{ \sum _{t=1}^{T_{i}} \ln (\frac{\lambda _{it}^{y_{it}}}{y_{it}!}) + \alpha ^{-1} \ln (\alpha ^{-1}) -\alpha ^{-1} \ln (\alpha ^{-1}+\sum _{t=1}^{T_{i}}\lambda _{it}) \right\} \\ & & + \sum _{i=1}^{N} \left\{ - \left( \sum _{t=1}^{T_{i}}y_{it} \right) \ln \left(\alpha ^{-1}+\sum _{t=1}^{T_{i}}\lambda _{it}\right) \right. \\ & & \left. \; \; \; \; \; \; \; + \ln \left[\Gamma \left(\alpha ^{-1}+ \sum _{t=1}^{T_{i}}y_{it} \right)\right] -\ln (\Gamma (\alpha ^{-1})) \right\} \end{eqnarray*}

The gradient is

\begin{eqnarray*} \frac{\partial \mathcal{L}}{\partial \bbeta } & = & \sum _{i=1}^{N} \left\{ \sum _{t=1}^{T_{i}} y_{it}\mathbf{x}_{it} -\frac{\alpha ^{-1}\sum _{t=1}^{T_{i}} \lambda _{it} \mathbf{x}_{it}}{\alpha ^{-1}+\sum _{t=1}^{T_{i}} \lambda _{it}}\right\} \\ & & -\sum _{i=1}^{N} \left\{ \left( \sum _{t=1}^{T_{i}} y_{it} \right) \frac{\sum _{t=1}^{T_{i}} \lambda _{it} \mathbf{x}_{it}}{\alpha ^{-1}+\sum _{t=1}^{T_{i}} \lambda _{it}} \right\} \\ \frac{\partial \mathcal{L}}{\partial \bbeta } & = & \sum _{i=1}^{N} \left\{ \sum _{t=1}^{T_{i}} y_{it}\mathbf{x}_{it} -\frac{(\alpha ^{-1}+\sum _{t=1}^{T_{i}} y_{it}) (\sum _{t=1}^{T_{i}}\lambda _{it}\mathbf{x}_{it})}{\alpha ^{-1}+\sum _{t=1}^{T_{i}} \lambda _{it}} \right\} \end{eqnarray*}

and

\begin{eqnarray*} \frac{\partial \mathcal{L}}{\partial \alpha } & = & \sum _{i=1}^{N} \left\{ -\alpha ^{-2} \left[ [1+ \ln (\alpha ^{-1})] - \frac{(\alpha ^{-1}+\sum _{t=1}^{T_{i}} y_{it})}{(\alpha ^{-1})+ \sum _{t=1}^{T_{i}}\lambda _{it}} - \ln \left(\alpha ^{-1} + \sum _{t=1}^{T_{i}} \lambda _{it} \right) \right] \right\} \\ & + & \sum _{i=1}^{N} \left\{ -\alpha ^{-2} \left[ \frac{\Gamma '(\alpha ^{-1}+ \sum _{t=1}^{T_{i}} y_{it})}{\Gamma (\alpha ^{-1} +\sum _{t=1}^{T_{i}} y_{it})} -\frac{\Gamma '(\alpha ^{-1})}{\Gamma (\alpha ^{-1})} \right] \right\} \end{eqnarray*}

where $\lambda _{it} = \exp (\mathbf{x}_{it}’\bbeta )$, $\Gamma ’(\cdot ) = d \Gamma (\cdot )/d (\cdot )$ and $\Gamma ’(\cdot )/\Gamma (\cdot )$ is the digamma function.

Panel Data Negative Binomial Regression with Fixed Effects

This section shows the derivation of a negative binomial model with fixed effects. Keep the assumptions of the Poisson-distributed dependent variable

\[ y_{it}\sim Poisson\left(\mu _{it}\right) \]

But now let the Poisson parameter be random with gamma distribution and parameters $\left(\lambda _{it},\delta \right)$,

\[ \mu _{it}\sim \Gamma \left(\lambda _{it},\delta \right) \]

where one of the parameters is the exponentially affine function of independent variables $\lambda _{it}=\exp \left(\mathbf{x}_{it}’\beta \right)$. Use integration by parts to obtain the distribution of $y_{it}$,

\begin{eqnarray*} P\left[y_{it}\right]& = & \int _{0}^{\infty }\frac{e^{-\mu _{it}}\mu _{it}^{y_{it}}}{y_{it}!}f\left(\mu _{it}\right)d\mu _{it}\\ & = & \frac{\Gamma \left(\lambda _{it}+y_{it}\right)}{\Gamma \left(\lambda _{it}\right)\Gamma \left(y_{it}+1\right)}\left(\frac{\delta }{1+\delta }\right)^{\lambda _{it}}\left(\frac{1}{1+\delta }\right)^{y_{it}} \end{eqnarray*}

which is a negative binomial distribution with parameters $\left(\lambda _{it},\delta \right)$. Conditional joint distribution is given as

\begin{eqnarray*} P[y_{i1},\ldots ,y_{iT_{i}}|\sum _{t=1}^{T_{i}}y_{it}]& =& \left(\prod _{t=1}^{T_{i}}\frac{\Gamma \left(\lambda _{it}+y_{it}\right)}{\Gamma \left(\lambda _{it}\right)\Gamma \left(y_{it}+1\right)}\right)\\ & & \times \left(\frac{\Gamma \left(\sum _{t=1}^{T_{i}}\lambda _{it}\right)\Gamma \left(\sum _{t=1}^{T_{i}}y_{it}+1\right)}{\Gamma \left(\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}\right) \end{eqnarray*}

Hence, the conditional fixed-effects negative binomial log-likelihood is

\begin{eqnarray*} \mathcal{L}& = & \sum _{i=1}^{N}\left[\log \Gamma \left(\sum _{t=1}^{T_{i}}\lambda _{it}\right)+\log \Gamma \left(\sum _{t=1}^{T_{i}}y_{it}+1\right)-\log \Gamma \left(\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)\right]\\ & & +\sum _{i=1}^{N}\sum _{t=1}^{T_{i}}\left[\log \Gamma \left(\lambda _{it}+y_{it}\right)-\log \Gamma \left(\lambda _{it}\right)-\log \Gamma \left(y_{it}+1\right)\right] \end{eqnarray*}

The gradient is

\begin{eqnarray*} \frac{\partial \mathcal{L}}{\partial \beta }& = & \sum _{i=1}^{N}\left[\left(\frac{\Gamma '\left(\sum _{t=1}^{T_{i}}\lambda _{it}\right)}{\Gamma \left(\sum _{t=1}^{T_{i}}\lambda _{it}\right)}-\frac{\Gamma '\left(\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}{\Gamma \left(\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}\right)\sum _{t=1}^{T_{i}}\lambda _{it}\mathbf{x}_{it}\right]\\ & & +\sum _{i=1}^{N}\frac{\Gamma '\left(\sum _{t=1}^{T_{i}}y_{it}+1\right)}{\Gamma \left(\sum _{t=1}^{T_{i}}y_{it}+1\right)}\\ & & +\sum _{i=1}^{N}\sum _{t=1}^{T_{i}}\left[\left(\frac{\Gamma '\left(\lambda _{it}+y_{it}\right)}{\Gamma \left(\lambda _{it}+y_{it}\right)}-\frac{\Gamma '\left(\lambda _{it}\right)}{\Gamma \left(\lambda _{it}\right)}\right)\lambda _{it}\mathbf{x}_{it}-\frac{\Gamma '\left(y_{it}+1\right)}{\Gamma \left(y_{it}+1\right)}\right] \end{eqnarray*}

Panel Data Negative Binomial Regression with Random Effects

This section describes the derivation of negative binomial model with random effects. Suppose

\[ y_{it}\sim Poisson\left(\mu _{it}\right) \]

with the Poisson parameter distributed as gamma,

\[ \mu _{it}\sim \Gamma \left(\nu _{i}\lambda _{it},\delta \right) \]

where its parameters are also random:

\[ \nu _{i}\lambda _{it}=\exp \left(\mathbf{x}_{it}’\beta +\eta _{it}\right) \]

Assume that the distribution of a function of $\nu _{i}$ is beta with parameters $\left(a,b\right)$:

\[ \frac{\nu _{i}}{1+\nu _{i}}\sim Beta\left(a,b\right) \]

Explicitly, the beta density with $\left[0,1\right]$ domain is

\[ f\left(z\right)=\left[B\left(a,b\right)\right]^{-1}z^{a-1}\left(1-z\right)^{b-1} \]

where $B\left(a,b\right)$ is the beta function. Then, conditional joint distribution of dependent variables is

\[ P[y_{i1},\ldots ,y_{iT_{i}}|\mathbf{x}_{i1},\ldots ,\mathbf{x}_{iT_{i}},\nu _{i}]=\prod _{t=1}^{T_{i}}\frac{\Gamma \left(\lambda _{it}+y_{it}\right)}{\Gamma \left(\lambda _{it}\right)\Gamma \left(y_{it}+1\right)}\left(\frac{1}{1+\nu _{i}}\right)^{\lambda _{it}}\left(\frac{\nu _{i}}{1+\nu _{i}}\right)^{y_{it}} \]

Integrating out the variable $\nu _{i}$ yields the following conditional distribution function:

\begin{eqnarray*} P[y_{i1},\ldots ,y_{iT_{i}}|\mathbf{x}_{i1},\ldots ,\mathbf{x}_{iT_{i}}]& = & \int _{0}^{1}\left[\prod _{t=1}^{T_{i}}\frac{\Gamma \left(\lambda _{it}+y_{it}\right)}{\Gamma \left(\lambda _{it}\right)\Gamma \left(y_{it}+1\right)}z_{i}^{\lambda _{it}}\left(1-z_{i}\right)^{y_{it}}\right]f\left(z_{i}\right)dz_{i}\\ & = & \frac{\Gamma \left(a+b\right)\Gamma \left(a+\sum _{t=1}^{T_{i}}\lambda _{it}\right)\Gamma \left(b+\sum _{t=1}^{T_{i}}y_{it}\right)}{\Gamma \left(a\right)\Gamma \left(b\right)\Gamma \left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}\\ & & \times \prod _{t=1}^{T_{i}}\frac{\Gamma \left(\lambda _{it}+y_{it}\right)}{\Gamma \left(\lambda _{it}\right)\Gamma \left(y_{it}+1\right)} \end{eqnarray*}

Consequently, the conditional log-likelihood function for a negative binomial model with random effects is

\begin{eqnarray*} \mathcal{L}& = & \sum _{i=1}^{N}\left[\log \Gamma \left(a+b\right)+\log \Gamma \left(a+\sum _{t=1}^{T_{i}}\lambda _{it}\right)+\log \Gamma \left(b+\sum _{t=1}^{T_{i}}y_{it}\right)\right]\\ & & -\sum _{i=1}^{N}\left[\log \Gamma \left(a\right)+\log \Gamma \left(b\right)+\log \Gamma \left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)\right]\\ & & +\sum _{i=1}^{N}\sum _{t=1}^{T_{i}}\left[\log \Gamma \left(\lambda _{it}+y_{it}\right)-\log \Gamma \left(\lambda _{it}\right)-\log \Gamma \left(y_{it}+1\right)\right] \end{eqnarray*}

The gradient is

\begin{eqnarray*} \frac{\partial \mathcal{L}}{\partial \beta }& = & \sum _{i=1}^{N}\left[\frac{\Gamma '\left(a+\sum _{t=1}^{T_{i}}\lambda _{it}\right)}{\Gamma \left(a+\sum _{t=1}^{T_{i}}\lambda _{it}\right)}\sum _{t=1}^{T_{i}}\lambda _{it}\mathbf{x}_{it}+\frac{\Gamma '\left(b+\sum _{t=1}^{T_{i}}y_{it}\right)}{\Gamma \left(b+\sum _{t=1}^{T_{i}}y_{it}\right)}\right]\\ & & -\sum _{i=1}^{N}\left[\frac{\Gamma '\left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}{\Gamma \left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}\sum _{t=1}^{T_{i}}\lambda _{it}\mathbf{x}_{it}\right]\\ & & +\sum _{i=1}^{N}\sum _{t=1}^{T_{i}}\left[\left(\frac{\Gamma '\left(\lambda _{it}+y_{it}\right)}{\Gamma \left(\lambda _{it}+y_{it}\right)}-\frac{\Gamma '\left(\lambda _{it}\right)}{\Gamma \left(\lambda _{it}\right)}\right)\lambda _{it}\mathbf{x}_{it}-\frac{\Gamma '\left(y_{it}+1\right)}{\Gamma \left(y_{it}+1\right)}\right] \end{eqnarray*}

and

\begin{eqnarray*} \frac{\partial \mathcal{L}}{\partial a}& = & \sum _{i=1}^{N}\left[\frac{\Gamma '\left(a+b\right)}{\Gamma \left(a+b\right)}+\frac{\Gamma '\left(a+\sum _{t=1}^{T_{i}}\lambda _{it}\right)}{\Gamma \left(a+\sum _{t=1}^{T_{i}}\lambda _{it}\right)}\right]\\ & & -\sum _{i=1}^{N}\left[\frac{\Gamma '\left(a\right)}{\Gamma \left(a\right)}+\frac{\Gamma '\left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}{\Gamma \left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}\right] \end{eqnarray*}

and

\begin{eqnarray*} \frac{\partial \mathcal{L}}{\partial b}& = & \sum _{i=1}^{N}\left[\frac{\Gamma '\left(a+b\right)}{\Gamma \left(a+b\right)}+\frac{\Gamma '\left(b+\sum _{t=1}^{T_{i}}y_{it}\right)}{\Gamma \left(b+\sum _{t=1}^{T_{i}}y_{it}\right)}\right]\\ & & -\sum _{i=1}^{N}\left[\frac{\Gamma '\left(b\right)}{\Gamma \left(b\right)}+\frac{\Gamma '\left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}{\Gamma \left(a+b+\sum _{t=1}^{T_{i}}\lambda _{it}+\sum _{t=1}^{T_{i}}y_{it}\right)}\right] \end{eqnarray*}