Advanced Econometrics Multiple Equation Models Exercises With Spss Eviews Sas And Stata
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Advanced Econometrics. Multiple Equation Models. Exercises with SPSS, Eviews, SAS and Stata
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
ADVANCED ECONOMETRICS: SIMULTANEOUS EQUATION MODELS, MULTIVARIATE TIME SERIES MODELS AND NONLINEAR MODELS. EXERCICES WITH EVIEWS, SAS AND STATA
Multi-equation econometric models are characterized by the presence of several equations to simultaneously estimate. 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 and SPSS
Advanced Econometrics. Dynamic Models. Exercises with SPSS, SAS, Stata and Eviews
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