Econometrics in Practice:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
Mercury Learning & Information
[2021]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xi, 374 Seiten Diagramme |
| ISBN: | 9781683926603 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Preface Chapter 1: Chapter 2: xiii Probability and the Statistical Foundations of Econometrics 1.1 Joint, Conditional, and Marginal Probabilities 1.2 The Probability Distribution Function 1.3 The Normal Distribution 1.4 The Probability Density Functionand the Moments of the Distribution 1.5 Other Useful Distributions 1.6 Classical and Bayesian Statistics 1.7 Summary Exercises References 14 15 20 20 21 22 Statistical Inference 2.1 Sampling 2.2 Hypothesis Testing 2.3 Confidence Intervals 2.4 P Values 2.5 Higher-Order Moments 2.6 NonParametric Tests Exercises References 25 28 31 36 39 41 44 47 48 1 4 8 12
viii · Contents Chapter 3: Chapter 4: Chapter 5: The Bivariate Regression Model 3.1 Derivation of the OLS Estimator 3.2 Interpreting the Regression Line - Marginal Effects and Elasticities 3.3 The Reverse Regression 3.4 Assumptions of the Classical Linear Regression Model 3.5 Distribution of the OLS Estimator 3.6 Statistical Inference with the OLS Estimator 3.7 Proof of the- Gauss-Markov Theorem 3.8 The Method of Maximum Likelihood 3.9 Prediction with the OLS Estimator 3.10 Summary Exercises References 49 50 The Multivariate Regression Model 4.1 Derivation of the Distribution of the OLS Estimator 4.2 Principles of Testing 4.3 Hypothesis Testing in the Multivariate Regression Model 4.4 Goodness of Fit 4.5 Misspecification 4.6 Interpreting a Multivariable Regression Equation 4.7 Partial Correlation Exercises References 79 83 84 86 90 96 98 100 103 106 Serial Correlation 5.1 Causes of Serial Correlation 5.2 Consequences of Serial Correlation 5.3 Detection of Serial Correlation 5.4 Dealing with Serial Correlation 5.5 Serial Correlation as a Simplifying Assumption Exercises References 107 108 110 115 123 128 130 132 53 56 58 60 63 68 70 72 74 75 78
Contents Chapter 6: Chapter 7: Chapter 8: Heteroscedasticity, Functional Form, and Structural Breaks 6.1 Causes of Heteroscedasticity 6.2 Consequences of Heteroscedasticity 6.3 Detection of Heteroscedasticity 6.4 Dealing with Heteroscedasticity 6.5 Testing the Functional Form 6.6 Changing the Functional Form 6.7 Testing Linear vs Log-Linear Functional Forms 6.8 Autoregressive Conditional Heteroscedasticity 6.9 Structural Breaks Exercises References · ix 135 136 137 141 144 146 148 150 154 157 162 165 Binary Dependent Variables 7.1 Logit Estimation 7.2 Goodness of Fit in Limited Dependent Variable Models 7.3 An Aside on Maximum Likelihood 7.4 Some Alternative Limited Dependent Variable Models 7.5 Another Example: The Market for Oranges Exercises References 167 170 Stochastic Regressors 8.1 Exogenous Regressors 8.2 Implications for Ordinary Least Squares Estimation 8.3 Asymptotic Distribution Theory 8.4 The Errors in Variables Model 8.5 The Instrumental Variables Estimator 8.6 Simultaneous Equations 8.7 Estimation of Simultaneous Equations Models 8.8 Estimation of Over Identified Equations Exercises References 187 187 189 192 194 196 199 202 206 209 210 174 176 178 182 183 185
x · Contents Chapter 9: Dynamic Models 213 9.1 Models with Expectations 216 9.2 Costs of Adjustment 218 9.3 Assessing the Dynamics 221 9.4 Modeling Dynamic Relationships 224 9.5 Statistical Problems with Lagged Dependent Variables 231 Exercises 232 References 234 Chapter 10: Time Series Analysis and ARIMAModeling 10.1 Identification of ARIMA Processes 10.2 ARIMA Modeling 10.3 Forecasting with an ARIMA Model 10.4 Impulse Responses 10.5 Moving Average Processes Exercises References 235 239 246 251 255 259 263 264 Chapter 11: Unit Roots and Seasonality 11.1 Testing for Unit Roots 11.2 Forecasting with Unit Root Processes 11.3 Seasonality 11.4 Structural Breaks and Unit Roots Exercises References 265 267 272 273 277 280 281 Chapter 12: Cointegration 12.1 Testing for Cointegration 12.2 Cointegration with Multiple Variables 12.3 Cointegration and Error Correction 12.4 The Johansen Test for Cointegration Exercises References 283 286 291 293 295 300 302
Contents Chapter 13: Vector Autoregressions 13.1 13.2 Some General Results for VARs Impulse Responses 13.3 Variance Decompositions 13.4 13.5 Structural VARs Vector Error Correction Models (VECMs) Exercises References Appendix: Answers to Odd Numbered Exercises Index · xi 305 309 311 313 316 317 319 321 323 367
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| adam_txt |
Contents Preface Chapter 1: Chapter 2: xiii Probability and the Statistical Foundations of Econometrics 1.1 Joint, Conditional, and Marginal Probabilities 1.2 The Probability Distribution Function 1.3 The Normal Distribution 1.4 The Probability Density Functionand the Moments of the Distribution 1.5 Other Useful Distributions 1.6 Classical and Bayesian Statistics 1.7 Summary Exercises References 14 15 20 20 21 22 Statistical Inference 2.1 Sampling 2.2 Hypothesis Testing 2.3 Confidence Intervals 2.4 P Values 2.5 Higher-Order Moments 2.6 NonParametric Tests Exercises References 25 28 31 36 39 41 44 47 48 1 4 8 12
viii · Contents Chapter 3: Chapter 4: Chapter 5: The Bivariate Regression Model 3.1 Derivation of the OLS Estimator 3.2 Interpreting the Regression Line - Marginal Effects and Elasticities 3.3 The Reverse Regression 3.4 Assumptions of the Classical Linear Regression Model 3.5 Distribution of the OLS Estimator 3.6 Statistical Inference with the OLS Estimator 3.7 Proof of the- Gauss-Markov Theorem 3.8 The Method of Maximum Likelihood 3.9 Prediction with the OLS Estimator 3.10 Summary Exercises References 49 50 The Multivariate Regression Model 4.1 Derivation of the Distribution of the OLS Estimator 4.2 Principles of Testing 4.3 Hypothesis Testing in the Multivariate Regression Model 4.4 Goodness of Fit 4.5 Misspecification 4.6 Interpreting a Multivariable Regression Equation 4.7 Partial Correlation Exercises References 79 83 84 86 90 96 98 100 103 106 Serial Correlation 5.1 Causes of Serial Correlation 5.2 Consequences of Serial Correlation 5.3 Detection of Serial Correlation 5.4 Dealing with Serial Correlation 5.5 Serial Correlation as a Simplifying Assumption Exercises References 107 108 110 115 123 128 130 132 53 56 58 60 63 68 70 72 74 75 78
Contents Chapter 6: Chapter 7: Chapter 8: Heteroscedasticity, Functional Form, and Structural Breaks 6.1 Causes of Heteroscedasticity 6.2 Consequences of Heteroscedasticity 6.3 Detection of Heteroscedasticity 6.4 Dealing with Heteroscedasticity 6.5 Testing the Functional Form 6.6 Changing the Functional Form 6.7 Testing Linear vs Log-Linear Functional Forms 6.8 Autoregressive Conditional Heteroscedasticity 6.9 Structural Breaks Exercises References · ix 135 136 137 141 144 146 148 150 154 157 162 165 Binary Dependent Variables 7.1 Logit Estimation 7.2 Goodness of Fit in Limited Dependent Variable Models 7.3 An Aside on Maximum Likelihood 7.4 Some Alternative Limited Dependent Variable Models 7.5 Another Example: The Market for Oranges Exercises References 167 170 Stochastic Regressors 8.1 Exogenous Regressors 8.2 Implications for Ordinary Least Squares Estimation 8.3 Asymptotic Distribution Theory 8.4 The Errors in Variables Model 8.5 The Instrumental Variables Estimator 8.6 Simultaneous Equations 8.7 Estimation of Simultaneous Equations Models 8.8 Estimation of Over Identified Equations Exercises References 187 187 189 192 194 196 199 202 206 209 210 174 176 178 182 183 185
x · Contents Chapter 9: Dynamic Models 213 9.1 Models with Expectations 216 9.2 Costs of Adjustment 218 9.3 Assessing the Dynamics 221 9.4 Modeling Dynamic Relationships 224 9.5 Statistical Problems with Lagged Dependent Variables 231 Exercises 232 References 234 Chapter 10: Time Series Analysis and ARIMAModeling 10.1 Identification of ARIMA Processes 10.2 ARIMA Modeling 10.3 Forecasting with an ARIMA Model 10.4 Impulse Responses 10.5 Moving Average Processes Exercises References 235 239 246 251 255 259 263 264 Chapter 11: Unit Roots and Seasonality 11.1 Testing for Unit Roots 11.2 Forecasting with Unit Root Processes 11.3 Seasonality 11.4 Structural Breaks and Unit Roots Exercises References 265 267 272 273 277 280 281 Chapter 12: Cointegration 12.1 Testing for Cointegration 12.2 Cointegration with Multiple Variables 12.3 Cointegration and Error Correction 12.4 The Johansen Test for Cointegration Exercises References 283 286 291 293 295 300 302
Contents Chapter 13: Vector Autoregressions 13.1 13.2 Some General Results for VARs Impulse Responses 13.3 Variance Decompositions 13.4 13.5 Structural VARs Vector Error Correction Models (VECMs) Exercises References Appendix: Answers to Odd Numbered Exercises Index · xi 305 309 311 313 316 317 319 321 323 367 |
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