Fixed Effects Regression ModelsThis book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. |
Contents
Basics | |
A FirstDifference Method for Three or More Periods | |
Fixed Effects Logistic Models | |
Fixed Effects Models for Count Data | |
Fixed Effects Models for Events History Data | |
Structural Equation Models With Fixed Effects | |
Stata Programs for Examples in Chapters 2 to 5 | |
Mplus Programs for Examples in Chapter 6 | |
Author Index | |
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Allison antisocial behavior birth intervals bootstrap standard errors censored Chapter chisquare command in Stata conditional likelihood conventional correlations count data covariate Cox regression data set dependent variable deviation coefficients deviation variables difference scores dmother DSELF dummy variables event history fixed effects analysis fixed effects estimates fixed effects methods fixed effects model fixed effects regression gender Hausman hazard hispanic husband’s death husdead hybrid method interactions lagged latent variable likelihood function linear models logistic regression logistic regression model logit model LOGSIZE momage momwork Mplus multiple negative binomial model negative binomial regression NLSY data observations odds ratios overdispersion panel data parameters personspecific means Poisson model Poisson regression poverty predictor variables random effects estimates random effects model response variable sample selfesteem spouse standard errors statistically significant time timeinvariant predictors timeinvariant variables timevarying twoperiod unconditional values variation vary vector wife’s death xtlogit xtreg