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245		0	_a _aIntroductory econometrics : _d _ba practical approach / _n _cHamid Seddighi. _h6 _p
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249			_i _i _a
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264			_3 _3 _aLondon : _d _bRoutledge/Taylor & Francis Group, _c©201246
300			_e _e _c25 cm _axvi, 383 pages : _billustrations
310			_a _a _b
321			_a _a _b
336			_b _atext _2rdacontent
337			_3 _30 _b _aunmediated _2rdamedia
338			_3 _30 _b _avolume _2rdacarrier
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500			_a _aIncludes bibliographical references and index. _d _b _c56
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505			_a _aMachine generated contents note: Unit 1 Single-equation regression models 1.Economic theory and economic modelling in practice 1.1.Economic theory and modelling 1.2.Economic modelling in practice 1.3.Mathematical specification of the economic models 1.4.The need for the empirical evaluation of economic models: the econometrics approach 1.5.A summary of key issues Review questions 2.Formulating single-equation regression models 2.1.Towards developing an econometric model 2.2.The standard assumptions of the data generation process (DGP) 2.3.The two-variable classical normal linear regression (CNLR) model and its assumptions 2.4.Towards the formulation of a general single-equation regression model 2.5.The multiple linear regression model and its assumptions: the classical normal linear regression model (CNLRM) 2.6.A summary of the key assumptions of the multiple linear regression model 2.7.A summary of key issues Contents note continued: 3.Estimation of single-equation regression models: basic ideas, concepts and methods 3.1.Estimation of a single-equation regression model: basic ideas and concepts 3.2.Estimators and their sampling distributions 3.3.Estimation methods: the OLS and the ML estimators 3.4.Monte Carlo studies 3.5.Maximum likelihood (ML) estimators 3.6.A summary of key issues Key terms: Chapters 1 -3 4.Evaluation of the regression results: hypothesis testing and tests of significance 4.1.Econometric modelling in practice: a model of demand for competitive imports 4.2.Definition of variables, time series data, and the OLS estimation method 4.3.Criteria for the evaluation of the regression results 4.4.Tests of significance 4.5.Testing for linear restrictions on the parameters of the regression models 4.6.A summary of key issues 5.Autocorrelation, heteroscedasticity and diagnostic testing Contents note continued: 5.1.Econometric criteria of evaluation and diagnostic testing procedures 5.2.Autocorrelation: causes, consequences and detection - the Durbin-Watson test 5.3.Heteroscedasticity: causes, consequences and detection 5.4.A test of normality assumption: the Bera-Jarque (1987) (BJ) test 5.5.A summary of key issues 6.The phenomenon of the spurious regression, data generation process (DGP), and additional diagnostic tests 6.1.The phenomenon of the spurious regression 6.2.Making the regression model dynamic via a partial adjustment process: estimation and evaluation 6.3.The Chow test for parameter stability 6.4.Recursive estimation 6.5.Testing for measurement errors and errors in variables: the Hausman specification test 6.6.A summary of key issues Key terms: Chapters 4 -6 7.Dynamic econometric modelling: the distributed lag models Contents note continued: 7.1.A review of the classical normal linear multiple regression model 7.2.Cases from economic theory 7.3.The definitions 7.4.Estimation of distributed lag models 7.5.Restricted estimation of finite distributed lag models 7.6.Restricted estimation of infinite distributed lag models 7.7.Infinite distributed lag models and economic theory 7.8.Estimation methods of infinite distributed lag models 7.9.Diagnostic tests for models with lagged dependent variables 7.10.Illustrative examples of infinite distributed lag models 7.11.A summary of key issues Key terms: Chapter 7 General notes and bibliography: Unit 1 Unit 2 Simultaneous equation regression models 8.Simultaneous equation models and econometric analysis 8.1.Definitions and notation 8.2.Simultaneous equation bias 8.3.Coping with simultaneous equation bias 8.4.Identification 8.5.Conditions for identification Contents note continued: 8.6.Methods of estimation 8.7.Seemingly unrelated equations 8.8.Diagnostic tests for simultaneous equation models 8.9.A comparison of methods of estimation 8.10.A summary of key issues Key terms: Unit 2 General notes and bibliography: Unit 2 Unit 3 Qualitative variables in econometric models - panel data regression models 9.Dummy variable regression models 9.1.Dummy variables and regression analysis 9.2.No base group: regression with no intercept term 9.3.More than one qualitative variable influencing household consumption expenditure 9.4.Seasonal adjustments of data using seasonal dummies 9.5.Pooling cross-section data with time series data 9.6.Using dummy variables to test for parameter stability 9.7.A summary of key issues 10.Qualitative response regression models 10.1.The linear probability model (LPM) 10.2.The logit regression model Contents note continued: 10.3.The probit/​normit model 10.4.The logit/​probit regression models in practice 10.5.Multi-response qualitative dependent variable regression models 10.6.Ordered logit/​probit regression models 10.7.A summary of key issues 11.Panel data regression models 11.1.Examples of panel data regression 11.2.The nature of panel data regression and analysis 11.3.The fixed-effects model 11.4.The random-effects model 11.5.Panel data regression analysis in practice 11.6.A summary of key issues Key terms: Unit 3 General notes and bibliography: Unit 3 Unit 4 Time series econometrics 12.Stationary and non-stationary time series 12.1.The definitions 12.2.Models with deterministic and stochastic trends 12.3.Integrated time series 12.4.Testing for stationarity: the autocorrelation function 12.5.Using autocorrelation coefficients to test for stationarity Contents note continued: 12.6.A summary of key issues 13.Testing for stationarity: the unit root tests 13.1.The unit root methodology 13.2.The Dickey-Fuller (DF) test 13.3.The augmented Dickey-Fuller (ADF) test 13.4.Testing joint hypotheses with the Dickey-Fuller tests 13.5.Testing conditional hypotheses with the Dickey-Fuller tests 13.6.The multiple unit roots, the seasonal unit roots and the panel data unit root tests 13.7.Problems of the unit root tests and recommendations 13.8.A summary of key issues 14.Cointegration analysis: two-variable case 14.1.Spurious regression and cointegration analysis 14.2.Cointegration: definition and concept 14.3.Testing for cointegration 14.4.Cointegration: the estimation of error correction models (ECM) 14.5.A summary of key issues 15.Cointegration analysis: the multivariate case Contents note continued: 15.1.Cointegration of more than two variables: key ideas and concepts 15.2.Cointegration tests for the multivariate case 15.3.Cointegration: estimation of error correction models (ECM) 15.4.Vector autoregressions and cointegration 15.5.VAR and cointegration 15.6.The Granger causality test 15.7.A summary of key issues Key terms: Unit 4 General notes and bibliography: Unit 4 Unit 5 Aspects of financial time series econometrics 16.Modelling volatility and correlations in financial time series 16.1.Defining volatility and its features 16.2.Parametric estimators of volatility 16.3.Non-parametric estimators of volatility 16.4.Multivariate volatility and correlation models 16.5.A summary of key issues General notes and bibliography: Unit 5. _b _t _g _r
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