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Format: 1 online resource (899 pages)

Edition: 1st ed.

ISBN: 9780199268016 , 9780191533235

Content: This rigorous textbook provides students with a working understanding and hands-on experience of current econometrics. It covers basic econometric methods and addresses the creative process of model building. Using real-world examples and exercises, it focuses on regression and covers choice data and time series data. Perfect for advanced undergraduate students, new graduate students, and applied researchers

Note: Cover Page -- Title Page -- Copyright Page -- Preface -- Contents -- Detailed Contents -- List of Exhibits -- Abbreviations -- Guide to the Book -- Introduction -- Econometrics -- Purpose of the book -- Characteristic features of the book -- Target audience and required background knowledge -- Brief contents of the book -- Study advice -- Teaching suggestions -- Some possible course structures -- 1 Review of Statistics -- 1.1 Descriptive statistics -- 1.1.1 Data graphs -- 1.1.2 Sample statistics -- 1.2 Random variables -- 1.2.1 Single random variables -- 1.2.2 Joint random variables -- 1.2.3 Probability distributions -- 1.2.4 Normal random samples -- 1.3 Parameter estimation -- 1.3.1 Estimation methods -- 1.3.2 Statistical properties -- 1.3.3 Asymptotic properties -- 1.4 Tests of hypotheses -- 1.4.1 Size and power -- 1.4.2 Tests for mean and variance -- 1.4.3 Interval estimates and the bootstrap -- Summary, further reading, and keywords -- Exercises -- 2 Simple Regression -- 2.1 Least squares -- 2.1.1 Scatter diagrams -- 2.1.2 Least squares -- 2.1.3 Residuals and R2 -- 2.1.4 Illustration: Bank Wages -- 2.2 Accuracy of least squares -- 2.2.1 Data generating processes -- 2.2.2 Examples of regression models -- 2.2.3 Seven assumptions -- 2.2.4 Statistical properties -- 2.2.5 Efficiency -- 2.3 Significance tests -- 2.3.1 The t-test -- 2.3.2 Examples -- 2.3.3 Use under less strict conditions -- 2.4 Prediction -- 2.4.1 Point predictions and prediction intervals -- 2.4.2 Examples -- Summary, further reading, and keywords -- Exercises -- 3 Multiple Regression -- 3.1 Least squares in matrix form -- 3.1.1 Introduction -- 3.1.2 Least squares -- 3.1.3 Geometric interpretation -- 3.1.4 Statistical properties -- 3.1.5 Estimating the disturbance variance -- 3.1.6 Coefficient of determination -- 3.1.7 Illustration: Bank Wages -- 3.2 Adding or deleting variables , 3.2.1 Restricted and unrestricted models -- 3.2.2 Interpretation of regression coefficients -- 3.2.3 Omitting variables -- 3.2.4 Consequences of redundant variables -- 3.2.5 Partial regression -- 3.3 The accuracy of estimates -- 3.3.1 The t-test -- 3.3.2 Illustration: Bank Wages -- 3.3.3 Multicollinearity -- 3.3.4 Illustration: Bank Wages -- 3.4 The F-test -- 3.4.1 The F-test in different forms -- 3.4.2 Illustration: Bank Wages -- 3.4.3 Chow forecast test -- 3.4.4 Illustration: Bank Wages -- Summary, further reading, and keywords -- Exercises -- 4 Non-Linear Methods -- 4.1 Asymptotic analysis -- 4.1.1 Introduction -- 4.1.2 Stochastic regressors -- 4.1.3 Consistency -- 4.1.4 Asymptotic normality -- 4.1.5 Simulation examples -- 4.2 Non-linear regression -- 4.2.1 Motivation -- 4.2.2 Non-linear least squares -- 4.2.3 Non-linear optimization -- 4.2.4 The Lagrange Multiplier test -- 4.2.5 Illustration: Coffee Sales -- 4.3 Maximum likelihood -- 4.3.1 Motivation -- 4.3.2 Maximum likelihood estimation -- 4.3.3 Asymptotic properties -- 4.3.4 The Likelihood Ratio test -- 4.3.5 The Wald test -- 4.3.6 The Lagrange Multiplier test -- 4.3.7 LM-test in the linear model -- 4.3.8 Remarks on tests -- 4.3.9 Two examples -- 4.4 Generalized method of moments -- 4.4.1 Motivation -- 4.4.2 GMM estimation -- 4.4.3 GMM standard errors -- 4.4.4 Quasi-maximum likelihood -- 4.4.5 GMM in simple regression -- 4.4.6 Illustration: Stock Market Returns -- Summary, further reading, and keywords -- Exercises -- 5 Diagnostic Tests and Model Adjustments -- 5.1 Introduction -- 5.2 Functional form and explanatory variables -- 5.2.1 The number of explanatory variables -- 5.2.2 Non-linear functional forms -- 5.2.3 Non-parametric estimation -- 5.2.4 Data transformations -- 5.2.5 Summary -- 5.3 Varying parameters -- 5.3.1 The use of dummy variables -- 5.3.2 Recursive least squares , 5.3.3 Tests for varying parameters -- 5.3.4 Summary -- 5.4 Heteroskedasticity -- 5.4.1 Introduction -- 5.4.2 Properties of OLS and White standard errors -- 5.4.3 Weighted least squares -- 5.4.4 Estimation by maximum likelihood and feasible WLS -- 5.4.5 Tests for homoskedasticity -- 5.4.6 Summary -- 5.5 Serial correlation -- 5.5.1 Introduction -- 5.5.2 Properties of OLS -- 5.5.3 Tests for serial correlation -- 5.5.4 Model adjustments -- 5.5.5 Summary -- 5.6 Disturbance distribution -- 5.6.1 Introduction -- 5.6.2 Regression diagnostics -- 5.6.3 Test for normality -- 5.6.4 Robust estimation -- 5.6.5 Summary -- 5.7 Endogenous regressors and instrumental variables -- 5.7.1 Instrumental variables and two-stage least squares -- 5.7.2 Statistical properties of IV estimators -- 5.7.3 Tests for exogeneity and validity of instruments -- 5.7.4 Summary -- 5.8 Illustration: Salaries of top managers -- Summary, further reading, and keywords -- Exercises -- 6 Qualitative and Limited Dependent Variables -- 6.1 Binary response -- 6.1.1 Model formulation -- 6.1.2 Probit and logit models -- 6.1.3 Estimation and evaluation -- 6.1.4 Diagnostics -- 6.1.5 Model for grouped data -- 6.1.6 Summary -- 6.2 Multinomial data -- 6.2.1 Unordered response -- 6.2.2 Multinomial and conditional logit -- 6.2.3 Ordered response -- 6.2.4 Summary -- 6.3 Limited dependent variables -- 6.3.1 Truncated samples -- 6.3.2 Censored data -- 6.3.3 Models for selection and treatment effects -- 6.3.4 Duration models -- 6.3.5 Summary -- Summary, further reading, and keywords -- Exercises -- 7 Time Series and Dynamic Models -- 7.1 Models for stationary time series -- 7.1.1 Introduction -- 7.1.2 Stationary processes -- 7.1.3 Autoregressive models -- 7.1.4 ARMA models -- 7.1.5 Autocorrelations and partial autocorrelations -- 7.1.6 Forecasting -- 7.1.7 Summary -- 7.2 Model estimation and selection , 7.2.1 The modelling process -- 7.2.2 Parameter estimation -- 7.2.3 Model selection -- 7.2.4 Diagnostic tests -- 7.2.5 Summary -- 7.3 Trends and seasonals -- 7.3.1 Trend models -- 7.3.2 Trend estimation and forecasting -- 7.3.3 Unit root tests -- 7.3.4 Seasonality -- 7.3.5 Summary -- 7.4 Non-linearities and time-varying volatility -- 7.4.1 Outliers -- 7.4.2 Time-varying parameters -- 7.4.3 GARCH models for clustered volatility -- 7.4.4 Estimation and diagnostic tests of GARCH models -- 7.4.5 Summary -- 7.5 Regression models with lags -- 7.5.1 Autoregressive models with distributed lags -- 7.5.2 Estimation, testing, and forecasting -- 7.5.3 Regression of variables with trends -- 7.5.4 Summary -- 7.6 Vector autoregressive models -- 7.6.1 Stationary vector autoregressions -- 7.6.2 Estimation and diagnostic tests of stationary VAR models -- 7.6.3 Trends and cointegration -- 7.6.4 Summary -- 7.7 Other multiple equation models -- 7.7.1 Introduction -- 7.7.2 Seemingly unrelated regression model -- 7.7.3 Panel data -- 7.7.4 Simultaneous equation model -- 7.7.5 Summary -- Summary, further reading, and keywords -- Exercises -- Appendix A. Matrix Methods -- A.1 Summations -- A.2 Vectors and matrices -- A.3 Matrix addition and multiplication -- A.4 Transpose, trace, and inverse -- A.5 Determinant, rank, and eigenvalues -- A.6 Positive (semi)definite matrices and projections -- A.7 Optimization of a function of several variables -- A.8 Concentration and the Lagrange method -- Exercise -- Appendix B. Data Sets -- List of Data Sets -- Index

Additional Edition: Print version Heij, Christiaan Econometric Methods with Applications in Business and Economics Oxford : Oxford University Press, Incorporated,c2004 ISBN 9780199268016

Language: English

Keywords: Electronic books

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