{smcl} {cmd:help cquadequ}{right:also see: {help clogit}, {help cquadbasic}, {help cquadext}} {hline} {title:Title} {p2colset 5 17 21 2}{...} {p2col :{hi:cquadequ} {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:cquadequ} {depvar} id [{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:. cquadequ union idcode age grade}{p_end} {title:Saved results} {pstd} {cmd:cquadequ} saves the following in {cmd:e()}: {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Scalars}{p_end} {synopt:{cmd:e(lk)}}final conditional log-likelihood{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Macros}{p_end} {synopt:{cmd:e(cmd)}}{cmd:cquadequ}{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Matrices}{p_end} {synopt:{cmd:e(be)}}coefficient vector{p_end} {synopt:{cmd:e(se)}}standard errors{p_end} {synopt:{cmd:e(ser)}}robust standard errors{p_end} {synopt:{cmd:e(tstat)}}t-statistics{p_end} {synopt:{cmd:e(pv)}}p-values{p_end} {title:Author} {pstd}Francesco Bartolucci{p_end} {pstd}Department of Economics, University of Perugia {p_end} {pstd}Perugia, Italy{p_end} {pstd}bart@stat.unipg.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.