Dynamic Econometrics Models with SAS, Stata, and EViews

Dynamic Econometrics Models with SAS, Stata, and EViews

Author: Cesar Perez Lopez

Publisher: Apress

Published: 2015-03-04

Total Pages: 180

ISBN-13: 9781484202876

DOWNLOAD EBOOK

Dynamic Econometrics Models with SAS, Stata, and EViews covers a wide array of dynamic econometrics models, including models with distributed delays, models with stochastic regressors, models with structural change, and dynamic panel data models. You'll discover core information and solutions around the theory of unit roots, co-integration, and error correction models. This book offers a practical, hands-on treatment of these models from multiple perspectives, so you'll find examples and solutions using SAS, Stat and EViews - the major solutions on the market to solve these non-trivial econometric tasks. You'll begin by learning about dynamic models such as those with delays in exogenous variables, and those with delays in the endogenous variable, and each of these simultaneously. Special types of dynamic econometric models are also explored, including finite distributed delays, and infinite distributed delays. In particular, you'll work with EViews to explore these initial dynamic econometric models. Then stable econometric models are considered and those with structural change, including time constant parameters, and you'll examine the Chow prediction test, recursive models, and CUSUM and CUSUMQ tests. Once you've explored stable models, you'll learn more about unstable models, including spurious regressions, stationary time series, seasonality detection, and unit roots test, including the Dickey-Fuller Unit Roots Tests, and the Phillips-Perron Unit Roots Test. Error correction models (ECM), Unit roots and co-integration in seasonal series are explored with both EViews and Stata, following practical examples and exercises. In the final section of this book, panel data models are considered, with constant coeffecients, and fixed effects. Dynamic panel data models, Logit and Probit panel data models are also examined using EViews and SAS. You will also see EViews in action with panel data and the Arellano and Bond methodology. What you’ll learn An introduction to Dynamic Econometric Models Special types of Dynamic Models Using EViews and specific dynamic models EViews and dynamic models with stochastic regressors Using SAS and dynamic econometric models Stable econometric models, including time constant parameters Using the Chow Prediction test with SAS, Stat and EViews Recursive models, and the CUSUM and CUSUMQ tests Unstable models and spurious regressions Unit roots tests, including Dickey-Fuller and Phillips-Perron Error Correction Models Stationary and Seasonal models with EViews Unit roots with Stata Panel data models and dynamic panels Logit and Probit panel data models Who this book is for For those who use SAS, Stata or EViews, this is a handy reference. For quants, researchers, economists, business consulting, risk managers these are tools that should be known to you which is the purpose of this book.


Advanced Econometrics. Dynamic Models. Exercises with SPSS, SAS, Stata and Eviews

Advanced Econometrics. Dynamic Models. Exercises with SPSS, SAS, Stata and Eviews

Author: César Pérez López

Publisher: CreateSpace

Published: 2013-10

Total Pages: 222

ISBN-13: 9781493628193

DOWNLOAD EBOOK

Usually variables that appear how explanatory in econometric models are supposed related at one time with the endogenous variable, so usually the temporary subscripts of all variables are equal. However, economic theory, econometrics, and other sciences lead us to relationship dynamic between the variables, since the impacts between variables can become manifest in later periods or extended to many periods. In this way appear dynamic models with variables out in time. Dynamic models usually seen three different situations according to the variables affected by delays. It may be that the delays involved only to exogenous variables, only the endogenous variable or simultaneously to endogenous and exogenous variables. This book covers a wide typology of dynamic models including models with distributed delays, models with stochastic regressors, models with structural change and dynamic panel data models. Widely is the theory of unit roots, the Cointegration and error correction models. And all this from a perspective multi-software, using the latest software on the market suitable for these non-trivial econometric tasks (SAS, EVIEWS, SPSS and STATA). The book develops the following themes: Dynamic models Dynamic models with delays in exogenous variables Dynamic models with delays in the endogenous variable Dynamic models with delays in the endogenous variable and the exogenous variables simultaneously Special types of dynamic models Models with finite distributed delays Models with distributed delays infinite EVIEWS and the specific dynamic models SPSS and the dynamic models SPSS and dynamic models with stochastic regressors. instrumental variables EVIEWS and dynamic models with stochastic regressors. instrumental variables SAS and the dynamic models Stable models. Structural change, unit roots and cointegration Structural stability in econometric models Parameters constant in time and prediction of Chow test Chow prediction test Structural Change and Chow test Recursive models: contrasts based on recursive estimation CUSUM and CUSUMQ tests Unstable models: spurious regressions Stationary time series. Detecting stationarity Seasonality detection Unit roots test Dickey-Fuller Unit Roots Tests Phillips-Perron Unit Roots Test Stable models in the long term: the cointegration analysis Phillips-Oularis for the Cointegration Test Error correction models mce Unit roots and cointegration in seasonal series Unit roots and cointegration in series with structural change Stationary and seasonality with EVIEWS Unit roots, cointegration and structural change with EVIEWS Panel data models. Unit roots and cointegration in panel. Dynamic panels Econometric models with panel data Panel data models with constant coefficients Panel data models with fixed effects Panel data models with random -effects Dynamic panel data models Logit and probit panel data models Unit roots and cointegration in panel data models EVIEWS and panel data models SPSS and panel data models Panel data models with SAS EVIEWS and dynamic models with panel data. methodology of ARELLANO and BOND EVIEWS and the contrasts of unit roots with panel data. Cointegration in panel


Econometric Models with Panel Data

Econometric Models with Panel Data

Author: César Pérez López

Publisher: Createspace Independent Publishing Platform

Published: 2015-01-20

Total Pages: 0

ISBN-13: 9781507644997

DOWNLOAD EBOOK

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 (or cross section) that are obtained when all operators are considered in an instant of time. Different patterns of behaviour of all players 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. In the latter case could be considered different patterns for individual to individual behaviour all the time interval of the sample. The book focuses on practical aspects of econometrics of panel data presenting variety of solved exercise with the latest software. STATA, SAS, SPSS and EVIEWS software was used. The remarkable reads as follows: MODELS WITH PANEL DATA PURE PANELS AND EXPANDED PANELS COMPARISON BETWEEN ANNUAL SAMPLES, COMBINATIONS OF CROSS SECTIONS (DATA POOL) AND PANELS ECONOMETRIC MODELS WITH PANEL DATA PANEL DATA MODELS WITH CONSTANT COEFFICIENTS PANEL DATA MODELS WITH FIXED EFFECTS PANEL DATA MODELS WITH RANDOM EFFECTS DYNAMIC PANEL DATA MODELS LOGIT AND PROBIT PANEL DATA MODELS PANEL DATA MODELS WITH EVIEWS EVIEWS AND MODELS WITH PANEL DATA. PANELS OF CONSTANT COEFICIENTS, FIXED EFFECTS AND RANDOM EFFECTS EVIEWS AND DYNAMIC MODELS WITH PANEL DATA. ARELLANO AND BOND METHODOLOGY PANEL DATA MODELS WITH STATA EXAMPLES MODELS WITH PANEL DATA LOGIT, PROBIT AND POISSON MODELS WITH PANEL DATA ESTIMATION OF DYNAMIC PANELS USING THE ARELLANO - BOND METHODOLOGY PANEL DATA MODELS WITH SAS 57 SAS AND MODELS WITH PANELDATA. PROCEDURE TSCSREG SAS AND MODELS WITH PANEL DATA. PROCEDURE PANEL PANEL DATA MODELS WITH SPSS STABILITY IN PANEL DATA MODELS. STRUCTURAL CHANGE, UNIT ROOTS AND COINTEGRATION STRUCTURAL STABILITY IN ECONOMETRIC MODELS UNSTABLE MODELS: SPURIOUS REGRESSIONS SEASONAL TIME SERIES. DETECTION OF SEASONALITY UNIT ROOTS TESTS STABLE MODELS IN THE LONG TERM: THE COINTEGRATION ANALYSIS THE ERROR CORRECTION MODELS UNIT ROOTS AND COINTEGRATION IN SEASONAL SERIES UNIT ROOTS AND COINTEGRATION IN SERIES WITH STRUCTURAL CHANGE UNIT ROOTS AND COINTEGRATION WITH PANEL DATA STATIONARY AND SEASONALITY WITH EVIEWS ROOTS UNIT, COINTEGRATION AND STRUCTURAL CHANGE WITH EVIEWS EVIEWS AND THE CONTRASTS OF UNIT ROOTS WITH PANEL DATA. COINTEGRATION IN PANEL DATA MODELS UNIT ROOTS, COINTEGRATION AND STRUCTURAL CHANGE WITH SAS SAS AND UNIT ROOTS TESTS WITH PANEL DATA MODELS. COINTEGRATION IN PANEL DATA MODELS


Advanced Econometrics. Multiple Equation Models. Exercises with SPSS, Eviews, SAS and Stata

Advanced Econometrics. Multiple Equation Models. Exercises with SPSS, Eviews, SAS and Stata

Author: César Pérez López

Publisher: CreateSpace

Published: 2013-10

Total Pages: 168

ISBN-13: 9781493621606

DOWNLOAD EBOOK

Multi-equation econometric models are characterized by the presence of several equations to simultaneously estimate. It is thus a generalization of the models in the field of systems of equations. Multi-equational simultaneous equations in linear models, incorporating the identification of models and techniques of estimation theory are covered in this book (MCI, MC2E, MC3E, RANR, SUR, etc.). Then the models are dealt with multivariate time series (VAR VARX, VARMA, BVAR, VEC) dealing the Cointegration theory from the multi-equational standpoint. Also delves into the non-linear multi-equational models and models of regression partitioned and segmented. The development of practical exercises is carried out from a perspective multi-software, using the latest software on the market suitable for these non-trivial econometric tasks: SAS, EVIEWS, STATA y SPSS. The book develops the following themes: Multiple equation models. Simultaneous equations Multi-equation linear models. Structural form and simultaneous linear equation models Multi equation model in reduced form Structural simultaneous equations model identification. MCI estimate Estimate simultaneous linear equations model Indirect Least Squares Instrumental variables Two Stage Least Square Recursive models Maximum Likelihood with limited information Maximum Likelihood Full Information Class k estimators and Tree Stage Least Square RANR or SUR method The heteroscedasticity robust methods: WHITE and HAC Simultaneous linear equations with time series models Simultaneous linear equations with eviews Simultaneous linear equations models with SAS: SYSLIN and MODEL procedures Simultaneous linear equations models with STATA Multivariate time series models: VAR, VARX, VARMA and BVAR. Cointegration Vector Autoregressive (VAR) models Identification in VAR models Estimate a VAR model VARMA models Cointegration in VAR models. Johansen test VAR models with EVIEWS. Johansen test Estimation VAR models in EVIEWS through menus Cointegration in VAR models with EVIEWS through menus Error Correction Model in VAR models with EVIEWS VAR models with SAS. Causality test and cointegration. Johansen test Johansen test in VAR models with SAS Error Correction Vector Model (VEC) in VAR models with SAS VAR models with exogenous variables (VARX) in SAS STATA and the VEC and VAR models. Causality test and cointegration. Johansen test Non-linear models. Partitioned and segmented regression Non- linear models Simple non-linear models Non-linear least squares. Newton and Marquardt algorithms Partitioned regression Segmented regression Non-linear estimation and segmented regression with SPSS Non-linear estimation with SAS. NLIN procedure Non-linear simultaneous equations models with SAS: procedure MODEL Non- linear models with EVIEWS Non- linear models with STATA


DYNAMIC ECONOMETRIC STRUCTURAL STABILITY, COINTEGRATION AND PANEL DATA

DYNAMIC ECONOMETRIC STRUCTURAL STABILITY, COINTEGRATION AND PANEL DATA

Author: Cesar Perez Lopez

Publisher: CESAR PEREZ

Published:

Total Pages: 223

ISBN-13: 1716278619

DOWNLOAD EBOOK

Usually explanatory variables in an econometric model are supposed related at one time with the endogenous variable, so usually the temporary sub-indices of all variables are equal. However, economic theory and other sciences lead us to dynamic relationship between the variables, since the impacts between variables can become manifest in later periods or extended to many periods. In this way appear dynamic models with variables out in time. Dynamic models usually seen three different situations according to the variables affected by delays. It may be that the delays involved only to exogenous variables, only the endogenous variable or simultaneously to endogenous and exogenous variables. This book covers a wide typology of dynamic models including models with distributed delays, models with stochastic regressors, models with structural change and dynamic panel data models. Widely is the theory of unit roots, the Cointegration and error correction models. And all this from a perspective multi-software, using the latest software on the market suitable for these non-trivial econometric tasks (SAS, EVIEWS, SPSS and STATA).


Econometric Models with Panel Data Across Stata

Econometric Models with Panel Data Across Stata

Author: Econometric Books

Publisher: Createspace Independent Publishing Platform

Published: 2015-10-23

Total Pages: 0

ISBN-13: 9781518745645

DOWNLOAD EBOOK

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 content is de next: PANEL DATA MODELS 1.1 Introduction TO PANEL data: Data structures 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 2.2 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 3.3 PANELS WITH AUTOCORRELATION. XTREGAR 3.4 HETEROSKEDASTICITY AN AUTOCORRELATION IN PANEL DATA MODELS. XTGLS 3.5 PANEL-CORRECTED STANDARD ERRORS. XTPCSE 3.6 INSTRUMENTAL VARIABLES AND TWO-STAGE LEAST SQUARES IN PANEL DATA. XTIVREG 3.7 panel-data models with random coefficients. XTRC 3.8 panel-data models with multilevel mixed effects. XTMIXED 3.9 ERROR-COMPONENTS MODEL across Hausman-Taylor estimator. XTHTAYLOR 3.10 Stochastic frontier models for panel data. 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 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 6.3 COUNT PANEL DATA MODELS


Solutions Manual for Econometrics

Solutions Manual for Econometrics

Author: Badi H. Baltagi

Publisher: Springer Nature

Published: 2022-12-07

Total Pages: 435

ISBN-13: 3030801586

DOWNLOAD EBOOK

This Fourth Edition updates the "Solutions Manual for Econometrics" to match the Sixth Edition of the Econometrics textbook. It adds problems and solutions using latest software versions of Stata and EViews. Special features include empirical examples replicated using EViews, Stata as well as SAS. The book offers rigorous proofs and treatment of difficult econometrics concepts in a simple and clear way, and provides the reader with both applied and theoretical econometrics problems along with their solutions. These should prove useful to students and instructors using this book.