Developing Econometrics -- Contents -- Foreword -- Preface -- Acknowledgements -- 1 Introduction -- 1.1 Nature and Scope of Econometrics -- 1.1.1 What is Econometrics and Why Study Econometrics? -- 1.1.2 Econometrics and Scientific Credibility of Business and Economic Decisions -- 1.2 Types of Economic Problems, Types of Data, and Types of Models -- 1.2.1 Experimental Data from a Marketing Experiment -- 1.2.2 Cross-Section Data: National Sample Survey Data on Consumer Expenditure -- 1.2.3 Non-Experimental Data Taken from Secondary Sources: The Case of Pharmaceutical Industry in India -- 1.2.4 Loan Default Risk of a Customer and the Problem Facing Decision on a Loan Application -- 1.2.5 Panel Data: Performance of Banks in India by the Type of Ownership after Economic Reforms -- 1.2.6 Single Time Series Data: The Bombay Stock Exchange (BSE) Index -- 1.2.7 Multiple Time Series Data: Stock Prices in BRIC Countries -- 1.3 Pattern Recognition and Exploratory Data Analysis -- 1.3.1 Some Basic Issues in Econometric Modeling -- 1.3.2 Exploratory Data Analysis Using Correlations and Scatter Diagrams: The Relative Importance of Managerial Function and Labor -- 1.3.3 Cleaning and Reprocessing Data to Discover Patterns: BSE Index Data -- 1.4 Econometric Modeling: The Roadmap of This Book -- 1.4.1 The Econometric Modeling Strategy -- 1.4.2 Plan of the Book -- Electronic References for Chapter 1 -- References -- 2 Independent Variables in Linear Regression Models -- 2.1 Brief Review of Linear Regression -- 2.1.1 Brief Review of Univariate Linear Regression -- 2.1.2 Brief Review of Multivariate Linear Regression -- 2.2 Selection of Independent Variable and Stepwise Regression -- 2.2.1 Principles of Selection of Independent Variables -- 2.2.2 Stepwise Regression -- 2.3 Multivariate Data Transformation and Polynomial Regression.

2.3.1 Linear Regression after Multivariate Data Transformation -- 2.3.2 Polynomial Regression on an Independent Variable -- 2.3.3 Multivariable Polynomial Regression -- 2.4 Column Multicollinearity in Design Matrix and Ridge Regression -- 2.4.1 Effect of Column Multicollinearity of Design Matrix -- 2.4.2 Ridge Regression -- 2.4.3 Ridge Trace Analysis and Ridge Parameter Selection -- 2.4.4 Generalized Ridge Regression -- 2.5 Recombination of Independent Variable and Principal Components Regression -- 2.5.1 Concept of Principal Components Regression -- 2.5.2 Determination of Principal Component -- Electronic References for Chapter 2 -- References -- 3 Alternative Structures of Residual Error in Linear Regression Models -- 3.1 Heteroscedasticity: Consequences and Tests for Its Existence -- 3.1.1 Consequences of Heteroscedasticity -- 3.1.2 Tests for Heteroscedasticity -- 3.2 Generalized Linear Model with Covariance Being a Diagonal Matrix -- 3.2.1 Diagonal Covariance Matrix and Weighted Least Squares -- 3.2.2 Model with Two Unknown Variances -- 3.2.3 Multiplicative Heteroscedastic Model -- 3.3 Autocorrelation in a Linear Model -- 3.3.1 Linear Model with First-Order Residual Autoregression -- 3.3.2 Autoregressive Conditional Heteroscedasticity (ARCH) Model -- 3.4 Generalized Linear Model with Positive Definite Covariance Matrix -- 3.4.1 Model Definition, Parameter Estimation and Hypothesis Tests -- 3.4.2 Some Equivalent Conditions -- 3.5 Random Effects and Variance Component Model -- 3.5.1 Random Effect Regression Model -- 3.5.2 The Variance Component Model -- 3.5.3 Analysis of Variance Method to Solve Variance Component Model -- 3.5.4 Minimum Norm Quadratic Unbiased Estimation (MINQUE) to Solve Variance Component -- 3.5.5 Maximum Likelihood Method to Solve Variance Component Model -- Electronic References for Chapter 3 -- References.

4 Discrete Variables and Nonlinear Regression Model -- 4.1 Regression Model When Independent Variables are Categorical -- 4.1.1 Problem About Wage and Gender Differences -- 4.1.2 Structural Changes in the Savings Function (Use of Categorical Variables in Combination with Continuous Variables) -- 4.1.3 Cross Section Analysis -- 4.1.4 Seasonal Analysis Model -- 4.2 Models with Categorical or Discrete Dependent Variables -- 4.2.1 Linear Model with Binary Dependent Variable -- 4.2.2 Logit Regression Model -- 4.2.3 Probit Regression Model -- 4.2.4 Tobit Regression Model -- 4.3 Nonlinear Regression Model and Its Algorithm -- 4.3.1 The Least Squares Estimate for Nonlinear Regression Model -- 4.3.2 Maximum Likelihood Estimation of Nonlinear Regression Model -- 4.3.3 Equivalence of Maximum Likelihood Estimation and Least Squares Estimation -- 4.4 Nonlinear Regression Models in Practice -- 4.4.1 Growth Curve Models -- 4.4.2 Box-Cox Transformation Model -- 4.4.3 Survival Data and Failure Rate Model -- 4.4.4 Total Factor Productivity (TFP) -- Electronic References for Chapter 4 -- References -- 5 Nonparametric and Semiparametric Regression Models -- 5.1 Nonparametric Regression and Weight Function Method -- 5.1.1 The Concept of Nonparametric Regression -- 5.1.2 Weight Function Method -- 5.2 Semiparametric Regression Model -- 5.2.1 Linear Semiparametric Regression Model -- 5.2.2 Single-Index Semiparametric Regression Model -- 5.3 Stochastic Frontier Regression Model -- 5.3.1 Stochastic Frontier Linear Regression Model and Asymptotically Efficient Estimator of Its Parameters -- 5.3.2 Semiparametric Stochastic Frontier Model -- Electronic References for Chapter 5 -- References -- 6 Simultaneous Equations Models and Distributed Lag Models -- 6.1 Simultaneous Equations Models and Inconsistency of OLS Estimators.

6.1.1 Demand-and-Supply Model, Keynesian Model and Wage-Price Model (Phillips Curve) -- 6.1.2 Macroeconomic IS Model, LM Model and Klein's Econometric Model -- 6.1.3 Inconsistency of OLS Estimation -- 6.2 Statistical Inference for Simultaneous Equations Models -- 6.2.1 Indirect Least Squares and Generalized Least Squares -- 6.2.2 Two Stage Least Squares -- 6.3 The Concepts of Lag Regression Models -- 6.3.1 Consumption Lag -- 6.3.2 Inflation Lag -- 6.3.3 Deposit Re-Creation -- 6.4 Finite Distributed Lag Models -- 6.4.1 Estimation of Distributed Lag Models When the Lag Length is Known and Finite -- 6.4.2 The Determination of Distributed Lag Length -- 6.5 Infinite Distributed Lag Models -- 6.5.1 Adaptive Expectations Model and Partial Adjustment Model -- 6.5.2 Koyck Transformation and Estimation of Geometric Lag Models -- Electronic References for Chapter 6 -- References -- 7 Stationary Time Series Models -- 7.1 Auto-Regression Model AR( p) -- 7.1.1 AR( p) Model and Stationarity -- 7.1.2 Auto-Covariance Function and Autocorrelation Function of AR( p) Model -- 7.1.3 Spectral Density of AR( p) Model and Partial Correlation Coefficient -- 7.1.4 Estimation of Parameters for AR(p) Model with Known Order p -- 7.1.5 Order Identification for AR( p) Process -- 7.2 Moving Average Model MA(q) -- 7.2.1 MA(q) Model and Its Properties -- 7.2.2 Parameter Estimation of MA(q) Model When the Order q is Known -- 7.2.3 Spectral Density Estimation for MA(q) Process -- 7.2.4 Order Identification for MA(q) Process -- 7.3 Auto-Regressive Moving-Average Process ARMA( p, q) -- 7.3.1 ARMA(p, q) Model and Its Properties -- 7.3.2 Parameter Estimations for ARMA(p, q) Model -- 7.3.3 Test for ARMA(p, q) Model -- 7.3.4 Order Identification for ARMA( p, q) Model -- 7.3.5 Univariate Time Series Modeling: The Basic Issues and Approaches -- Electronic References for Chapter 7 -- References.

8 Multivariate and Nonstationary Time Series Models -- 8.1 Multivariate Stationary Time Series Model -- 8.1.1 General Description of Multivariable Stationary Time Series Model -- 8.1.2 Estimation of Mean and Autocovariance Function of Multivariate Stationary Time Series -- 8.1.3 Vector Autoregression Model of Order p: VAR( p) -- 8.1.4 Wold Decomposition and Impulse-Response -- 8.1.5 Variance Decomposition with VAR( p) -- 8.1.6 Granger Causality with VAR(p) Specification -- 8.2 Nonstationary Time Series -- 8.2.1 Stochastic Trends and Unit Root Processes -- 8.2.2 Test for Unit Root Hypothesis -- 8.3 Cointegration and Error Correction -- 8.3.1 The Concept and Representation of Cointegration -- 8.3.2 Simultaneous (Structural) Equation System (SES) and Vector Auto Regression (VAR) -- 8.3.3 Cointegration and Error Correction Representation -- 8.3.4 Estimation of Parameters of Cointegration Process -- 8.3.5 Test of Hypotheses on the Number of Cointegrating Equations -- 8.4 Autoregression Conditional Heteroscedasticity in Time Series -- 8.4.1 ARCH Model -- 8.4.2 Generalized ARCH Model-GARCH Model -- 8.4.3 Other Generalized Forms of ARCH Model -- 8.5 Mixed Models of Multivariate Regression with Time Series -- 8.5.1 Mixed Model of Multivariate Regression with Time Series -- 8.5.2 Mixed Model of Multivariate Regression and Cointegration with Time Series -- Electronic References for Chapter 8 -- References -- 9 Multivariate Statistical Analysis and Data Analysis -- 9.1 Model of Analysis of Variance -- 9.1.1 Single Factor Analysis of Variance Model -- 9.1.2 Two Factor Analysis of Variance with Non-Repeated Experiment -- 9.1.3 Two Factor Analysis of Variance with Repeated Experiment -- 9.2 Other Multivariate Statistical Analysis Models -- 9.2.1 Discriminate Analysis Model -- 9.2.2 Factor Analysis Model.

9.2.3 Principal Component Analysis and Multidimensional Scaling Method.