Introduction to Applied Econometerics
(Sprache: Englisch)
You'll find the "econ" back in econometrics with INTRODUCTION TO APPLIED ECONOMETRICS and its accompanying CD.. You'll have the opportunity to replicate classic empirical findings using original data sets and will develop an understanding of the relevance...
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Klappentext zu „Introduction to Applied Econometerics “
You'll find the "econ" back in econometrics with INTRODUCTION TO APPLIED ECONOMETRICS and its accompanying CD.. You'll have the opportunity to replicate classic empirical findings using original data sets and will develop an understanding of the relevance of economic theory to empirical analysis. The author integrates classic empirical examples and applications and builds toward a self-contained four-chapter introduction to time series analysis. The CD includes data sets formatted for STATA, Eviews, Excel, Minitab, SAS and ASCII, as well as an appendix presenting multiple regression in matrix form and another on treating portfolio theory and the capital asset pricing model.
Inhaltsverzeichnis zu „Introduction to Applied Econometerics “
1. ECONOMIC DATA AND ECONOMIC MODELS.Economic Data. Economic Models. Descriptive Statistics Versus Statistical Inference.
2. STATISTICAL INFERENCE.
Populations, Samples and Parameters. Statistics and Sampling Distributions. Properties of Estimators. Derivation of Estimators. Hypothesis Testing. Further Topics in Hypothesis Testing. Inference is Conditional on the Model. Econometrics and Statistics. Statistical Methodology and the Philosophy of Science.
3. RELATIONSHIPS BETWEEN VARIABLES.
Covariance and Correlation. Regression. Deviation Form Notation. Conclusions.
4. SIMPLE REGRESSION.
Model Specification. Least Squares Estimation. Sampling Properties of the Least Squares Estimators. The Sampling Distributions of a and B. Hypothesis Testing. Decomposition of Sample Variation. Presentation of Regression Results. Scaling and Units of Measure. Sampling, Numerical, and Invariance Properties. Application: Output and Production Costs.
5. SUPPLEMENTARY TOPICS IN REGRESSION.
Forecasting. Regression Through the Origin. When Regression Goes Wrong.
6. MATTERS OF FUNCTIONAL FORM.
Loglinear Models. Log-Lin Models. Lin-Log Models. Reciprocal Models. Application: Engel Curves. Conclusions.
7. APPLICATIONS TO PRODUCTION FUNCTIONS.
General Features of Production Functions. The Cobb-Douglas Production Function. Technical Change. Testing Marginal Productivity Conditions. Conclusions.
8.MULTIPLE REGRESSION.
Model Specification. Least Squares Estimation. Properties of Least Squares Estimators. Hypothesis Testing. Decomposition of a Sample Variation. Application: Electricity Demand. Multicollinearity. Application: the Quadratic Cost Function. Model Misspecification. Pre-Test Estimation.
9.APPLICATION TO ECONOMIC GROWTH.
Introduction. The Textbook Solow-Swan Model. Human Capital in the Solow-Swan Model. Summary: Mankiw, Romer, and Weil in a Nutshell. Conclusions.
10. DUMMY VARIABLES AND RESTRICTED COEFFICIENTS.
Dummy Variables. Restricted Coefficients.
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Identification.
11. APPLICATIONS TO COST FUNCTIONS.
The Cost Function. Deriving the Cost Function. Using the Cost Function. Returns to Scale in Electricity Generation. The Translog Cost Function. Consumer Demand. Further Reading.
12. MODEL DISCOVERY.
Data Mining. Specification Testing. Non-nested Testing. Model Choice. Should the Equation Be Part of a System? Conclusions.
13. NONLINEAR REGRESSION.
Introduction. Nonlinear Least Squares. Computer Numerics. Reparameterization. Identification. Sampling Properties of NLS Estimators. Estimating Sigma 2. Hypothesis Testing. Conclusions.
14. HETEROSKEDASTICITY.
Consequences for Ordinary Least Squares. Heteroskedasticity-Robust Tests. Weighted Least Squares. Testing for Heteroskedasticity.
15. TIME SERIES: SOME BASIC CONCEPTS.
Introduction. White Noise. Measuring Temporal Dependence. Stationarity and Nonstationarity. Trend Stationary Processes. A Random Walk. A Random Walk with Drift. Key Properties of Random Walks. Conclusions.
16. FLUCTUATIONS.
Introduction. Moving Average Processes. Autoregressive Processes. The Stationarity Condition. Key Properties of Moving Average and Autoregressive Processes. Autoregressive-Moving Average Processes.
17. TRENDS.
The Constant Growth Model Revisited. Trend and Difference Stationary Processes. Testing for Stochastic Trends. Higher Orders of Integration.
18. COINTEGRATION.
Long Run Relationships Between Variables. Relationships Between Variables. The Arithmetic of Integrated Processes. Cointegration. The Engle-Granger Test for Cointegration. Testing Restrictions on the Cointegrating Vector. Error Correction Models. The ECM of VAR. Cointegrating Rank. Conclusions and Further Reading.
APPENDIX A: LAWS OF SUMMATION AND DEVIATION FORM.
Laws of Summation. Laws of Deviation Form.
APPENDIX B: DISTRIBUTION THEORY.
Random Variables and Probability Distribution. Mathematical Expectation. Expected Value of a Function. Variance. Variance of a Function. Standardized Random Variables. Bivariate Dis
11. APPLICATIONS TO COST FUNCTIONS.
The Cost Function. Deriving the Cost Function. Using the Cost Function. Returns to Scale in Electricity Generation. The Translog Cost Function. Consumer Demand. Further Reading.
12. MODEL DISCOVERY.
Data Mining. Specification Testing. Non-nested Testing. Model Choice. Should the Equation Be Part of a System? Conclusions.
13. NONLINEAR REGRESSION.
Introduction. Nonlinear Least Squares. Computer Numerics. Reparameterization. Identification. Sampling Properties of NLS Estimators. Estimating Sigma 2. Hypothesis Testing. Conclusions.
14. HETEROSKEDASTICITY.
Consequences for Ordinary Least Squares. Heteroskedasticity-Robust Tests. Weighted Least Squares. Testing for Heteroskedasticity.
15. TIME SERIES: SOME BASIC CONCEPTS.
Introduction. White Noise. Measuring Temporal Dependence. Stationarity and Nonstationarity. Trend Stationary Processes. A Random Walk. A Random Walk with Drift. Key Properties of Random Walks. Conclusions.
16. FLUCTUATIONS.
Introduction. Moving Average Processes. Autoregressive Processes. The Stationarity Condition. Key Properties of Moving Average and Autoregressive Processes. Autoregressive-Moving Average Processes.
17. TRENDS.
The Constant Growth Model Revisited. Trend and Difference Stationary Processes. Testing for Stochastic Trends. Higher Orders of Integration.
18. COINTEGRATION.
Long Run Relationships Between Variables. Relationships Between Variables. The Arithmetic of Integrated Processes. Cointegration. The Engle-Granger Test for Cointegration. Testing Restrictions on the Cointegrating Vector. Error Correction Models. The ECM of VAR. Cointegrating Rank. Conclusions and Further Reading.
APPENDIX A: LAWS OF SUMMATION AND DEVIATION FORM.
Laws of Summation. Laws of Deviation Form.
APPENDIX B: DISTRIBUTION THEORY.
Random Variables and Probability Distribution. Mathematical Expectation. Expected Value of a Function. Variance. Variance of a Function. Standardized Random Variables. Bivariate Dis
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Bibliographische Angaben
- Autor: Kenneth G. Stewart
- 2004, 913 Seiten, Maße: 24,2 cm, Gebunden, Englisch
- Verlag: Cengage Learning EMEA
- ISBN-10: 0534369162
- ISBN-13: 9780534369163
Sprache:
Englisch
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