Econometric Models With Panel Data. Applications With STATA
- Length: 366 pages
- Edition: 1
- Language: English
- Publisher: lulu.com
- Publication Date: 2021-04-10
- ISBN-10: B093X6YFDF
- ISBN-13: 9781008984134
- Sales Rank: #2102205 (See Top 100 Books)
The data panels are a special type of samples in which the behavior of a certain number of economic agents is followed over time. In this way, the researcher can perform economic analysis and specify models with the data of cross section that are obtained when all operators are considered in an instant of time. Different patterns of behaviour of all agents together studied in the different temporal moments may thus be assessed. Alternatively, you can perform the same analysis considering time series given by the evolution of each economic agent throughout all the periods of the sample. This book explores the panel data econometrics through STATA. The most important topics are the following: Linear regression estimators in panel data models, fixed and random effects, heteroskedasticity and autocorrelation in panel data models, instrumental variables and two stage least squares in panel data models, dynamic panel data models, logit and probit panel data models, censored panel data models, count panel data models, Tobit panel data models, Poisson panel data models, negative binomial panel data models and others models with panel data.
models WITH PANEL data 1.1 Introduction TO PANEL data: Data structures 1.1.1 Cross-sectional data 1.1.2 Time series data 1.1.3 Combinations of cross sections 1.1.4 Panel data or longitudinal data 1.2 ECONOMETRIC Models with PANEL data 1.3 Panel DATA Models with constant coefficients 1.4 Panel DATA Models WITH Fixed effects 1.5 PANEL DATA Models WITH Random effects 1.6 DYNAMIC PANEL data Models 1.7 LOGIT and PROBIT PANEL DATA Models PANEL data models with STATA 2.1 Stata And PANEL data models 2.1.1 Random effects model 2.1.2 Model estimated using the between regression estimator 2.1.3 Fixed effects model 2.1.4 Random effects by maximum likelihood model 2.1.5 Model in population mean 2.2 Examples MODELS with PANEL data 2.3 Logit, probit and Poisson models with panel data 2.4 Estimation of dynamic panels using the Arellano - Bond methodology LINEAR REGRESSION ESTIMATORS IN PANEL DATA MODELS 3.1 STATA COMMANDS IN PANEL DATA MODELS LINEAR REGRESSION 3.2 FIXED AN RANDOM EFFECTS, AND POPULATION-AVERAGED EFECTS LINEAR MODELS. XTREG 3.2.1 Methodological notes 3.2.2 Betwenn-effects model 3.2.3 Fixed-effects model 3.2.4 Random-effects model 3.2.5 Random-effects model using ML 3.2.6 Population-averaged models 3.2.7 Fixed-effects models with robust standard errors 3.2.8 Fixed-effects model with robust standard errors. Breus and Pagan test 3.2.9 Hausman especification test 3.3 PANELS WITH AUTOCORRELATION. XTREGAR 3.3.1 Fixed effects model 3.3.2 Fixed effects model: Baltagi-Wu LBI test 3.3.3 Random effects model 3.4 HETEROSKEDASTICITY AN AUTOCORRELATION IN PANEL DATA MODELS. XTGLS 3.4.1 Heteroskedasticity in panels. XTGLS 3.4.2 Crosssectional correlation in panels. XTGLS 3.4.3 Autocorrelation within panels. XTGLS 3.5 PANEL-CORRECTED STANDARD ERRORS. XTPCSE 3.6 INSTRUMENTAL VARIABLES AND TWO-STAGE LEAST SQUARES IN PANEL DATA. XTIVREG 3.6.1 Fixed effects model. XTIVREG 3.6.2 GLS randon effects model. XTIVREG 3.7 panel-data models with random coefficients. XTRC 3.8 panel-data models with multilevel mixed effects. XTMIXED 3.8.1 Two level models, XTMIXED 3.8.2 Covariance estructures. XTMIXED 3.8.3 Likelihood versus restricted likelihood. XTMIXED 3.8.4 Three level models. XTMIXED 3.8.5 Blocked diagonal covariance estructures. XTMIXED 3.8.6 Heteroskedastic random effects. XTMIXED 3.8.7 Heteroskedastic residual errors. XTMIXED 3.9 ERROR-COMPONENTS MODEL across Hausman–Taylor estimator . XTHTAYLOR 3.10 Stochastic frontier models for panel data. XTFRONTIER 3.10.1 Timeinvariant Model. XTFRONTIER 3.10.2 Timevarying decay model. XTFRONTIER DYNAMIC PANEL DATA Models 4.1 ESTIMATORS FOR DYNAMIC PANEL DATA MODELS 4.2 ARELLANO-BOND LINEAR DYNAMIC PANEL DATA. XTABOND COMMAND 4.3 LINEAR DYNAMIC PANEL-DATA ESTIMATION. XTPD 4.4 ARELLANO–BOVER/BLUNDELL–BOND LINEAR DYNAMIC PANEL-DATA ESTIMATION. XTDPDSYS LOGIT AND PROBIT PANEL DATA Models 5.1 METHODOLOGICAL NOTES 5.2 STATA COMMAnds FOR ESTIMATE LOGIT AND PROBIT PANEL DATA MODELS 5.3 Fixed-effects, random-effects, and population-averaged logit models. XTLOGIT 5.4 Random-effects and population-averaged probit models. Xtprobit 5.5 Random-effects and population-averaged cloglog models. xtcloglog: 5.6 Multilevel mixed-effects logistic regression. Xtmelogit 5.6.1 Two-level models 5.6.2 Three-level models 5.6.3 Crossed-effects models CENSORED AND COUNT Panel DATA MODELS. TOBIT, POISSON AND NEGATIVE BINOMIAL MODELS 6.1 CENSORED AND COUNT PANEL DATA MODELS 6.2 CENSORED PANEL DATA MODELS 6.2.1 Tobit Random-effects tobit models: XTTOBIT 6.2.2 Random-effects interval-data regression models: XTINTREG 6.3 COUNT PANEL DATA MODELS 6.3.1 Fixed-effects, random-effects, and population-averaged Poisson models: XTPOISSON 6.3.2 Fixed-effects, random-effects, and population-averaged Negative Binomial models: XTNBREG
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