{smcl}
{cmd:help cquadequr}{right:also see: {help clogit}, {help cquadbasicr}, {help cquadextr}}
{hline}
{title:Title}
{p2colset 5 17 21 2}{...}
{p2col :{hi:cquadequr} {hline 2}}Conditional maximum likelihood estimation for the modified
version of the quadratic exponential model proposed by Bartolucci, Nigro & Pigini (2013){p_end}
{p2colreset}{...}
{title:Syntax}
{p 8 16 2}{cmd:cquadequr} id {depvar} [{indepvars}]
{title:Description}
{pstd}
Fit by conditional maximum likelihood a modified version of the model for binary logitudinal
data proposed by Bartolucci & Nigro (2010), in which the interaction terms have an extended
form. This modified version is used to test for state dependence as described in Bartolucci
et al. (2013).
{pstd}
For a vector y_i of T observations (y_{i,1},...,y_{i,T}) for unit i, the model is based on the assumption:
{pstd}
p(y_i) {proportional to} exp[(y_{i,2}x_{i,2} + ... + y_{i,T}x_{i,T})'beta + (1{y_{i,1}==y_{i,2}} + ... + 1{y_{i,T-1}==y_{i,T}})gamma]
{pstd}
where x_{i,t} is a column vector of covariates and the first observation is taken as initial condition
and 1{.} is the indicator function. The function can be also used with unbalanced panel data.
{pstd}
id (compulsory) is the list of the reference unit of each observation{p_end}
{title:Examples}
{pstd}Setup{p_end}
{phang}{cmd:. webuse union}{p_end}
{pstd}Fit (modified) quadratic exponential model{p_end}
{phang}{cmd:. cquadequr idcode union age grade}{p_end}
{title:Saved results}
{pstd}
{cmd:cquadequr} saves the following in matrix list {cmd:return matrix list}:
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Matrices}{p_end}
{synopt:{cmd:matrix list r(coefficients)}}coefficient vector{p_end}
{synopt:{cmd:matrix list r(ser)}}standard errors{p_end}
{synopt:{cmd:matrix list r(serr)}}robust standard errors{p_end}
{synopt:{cmd:matrix list r(He)}}Hessian matrix of the conditional likelihood function{p_end}
{synopt:{cmd:matrix list r(vcov)}}coefficients covariance matrix{p_end}
{title:Authors}
{pstd}Francesco Bartolucci{p_end}
{pstd}Department of Economics, University of Perugia {p_end}
{pstd}Perugia, Italy{p_end}
{pstd}francesco.bartolucci@unipg.it{p_end}
{pstd}Claudia Pigini{p_end}
{pstd}Department of Economics and Social Science, Marche Polytechnic University{p_end}
{pstd}Ancona, Italy{p_end}
{pstd}c.pigini@univpm.it{p_end}
{pstd}Francesco Valentini{p_end}
{pstd}Department of Economics and Social Science, Marche Polytechnic University{p_end}
{pstd}Ancona, Italy{p_end}
{pstd}f.valentini@pm.univpm.it{p_end}
{title:References}
{pstd}
Bartolucci, F. & Nigro, V. (2010). A dynamic model for binary panel data with unobserved heterogeneity admitting a root-n consistent conditional estimator. Econometrica, 78, pp. 719-733.
{pstd}
Bartolucci, F., Nigro, V. & Pigini, C. (2013). Testing for state dependence in binary panel data with individual covariates, MPRA Paper 48233, University Library of Munich, Germany.
{pstd}
cquadr User guide. https://github.com/fravale/cquadr/blob/master/cquadr-guide.pdf{p_end}