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Part 1 Single-Equation Regression Models -- 1 The Nature of Regression Analysis 15 -- 1.1 Historical Origin of the Term "Regression" 15 -- 1.2 The Modern Interpretation of Regression 16 -- Examples 16 -- 1.3 Statistical vs. Deterministic Relationships 19 -- 1.4 Regression vs. Causation 20 -- 1.5 Regression vs. Correlation 21 -- 1.6 Terminology and Notation 22 -- 1.7 The Nature and Sources of Data for Econometric Analysis 23 -- Types of Data 23 -- The Sources of Data 24 -- The Accuracy of Data 26 -- Exercises 28 -- Appendix 1A 29 -- 1A.1 Sources of Economic Data 29 -- 1A.2 Sources of Financial Data 31 -- 2 Two-Variable Regression Analysis: Some Basic Ideas 32 -- 2.1 A Hypothetical Example 32 -- 2.2 The Concept of Population Regression Function (PRF) 36 -- 2.3 The Meaning of the Term "Linear" 36 -- Linearity in the Variables 37 -- Linearity in the Parameters 37 -- 2.4 Stochastic Specification of PRF 38 -- 2.5 The Significance of the Stochastic Disturbance Term 39 -- 2.6 The Sample Regression Function (SRF) 41 -- Exercises 45 -- 3 Two-Variable Regression Model: The Problem of Estimation 52 -- 3.1 The Method of Ordinary Least Squares 52 -- 3.2 The Classical Linear Regression Model: The Assumptions Underlying the Method of Least Squares 59 -- How Realistic Are These Assumptions? 68 -- 3.3 Precision or Standard Errors of Least-Squares Estimates 69 -- 3.4 Properties of Least-Squares Estimators: The Gauss-Markov Theorem 72 -- 3.5 The Coefficient of Determination r2: A Measure of "Goodness of Fit" 74 -- 3.6 A Numerical Example 80 -- 3.7 Illustrative Examples 83 -- Coffee Consumption in the United States, 1970-1980 83 -- Keynesian Consumption Function for the United States, 1980-1991 84 -- 3.8 Computer Output for the Coffee Demand Function 85 -- 3.9 A Note on Monte Carlo Experiments 85 -- Exercises 87 -- Problems 89 -- Appendix 3A 94 -- 3A.1 Derivation of Least-Squares Estimates 94 -- 3A.2 Linearity and Unbiasedness Properties of Least-Squares Estimators 94 -- 3A.3 Variances and Standard Errors of Least-Squares Estimators 95 -- 3A.4 Covariance between B1 and B2 96 -- 3A.5 The Least-Squares Estimator of o2 96 -- 3A.6 Minimum-Variance Property of Least-Squares Estimators 97 -- 3A.7 SAS Output of the Coffee Demand Function (3.7.1) 99 -- 4 The Normality Assumption: Classical Normal Linear Regression Model (CNLRM) 101 -- 4.1 The Probability Distribution of Disturbances ui 101 -- 4.2 The Normality Assumption 102 -- 4.3 Properties of OLS Estimators under the Normality Assumption 104 -- 4.4 The Method of Maximum Likelihood (ML) 107 -- 4.5 Probability Distributions Related to the Normal Distribution: The t, Chi-square (X2), and F Distributions 107 -- Appendix 4A 110 -- Maximum Likelihood Estimation of Two-Variable Regression Model 110 -- Maximum Likelihood Estimation of the Consumption-Income Example 113 -- Appendix 4A Exercises 113 -- 5 Two-Variable Regression: Interval Estimation and Hypothesis Testing 115 -- 5.1 Statistical Prerequisites 115 -- 5.2 Interval Estimation: Some Basic Ideas 116 -- 5.3 Confidence Intervals for Regression Coefficients B1 and B2 117 -- Confidence Interval for B2 117 -- Confidence Interval for B1 119 -- Confidence Interval for B1 and B2 Simultaneously 120 -- 5.4 Confidence Interval for o2 120 -- 5.5 Hypothesis Testing: General Comments 121 -- 5.6 Hypothesis Testing: The Confidence-Interval Approach 122 -- Two-Sided or Two-Tail Test 122 -- One-Sided or One-Tail Test 124 -- 5.7 Hypothesis Testing: The Test-of-Significance Approach 124 -- Testing the Significance of Regression Coefficients: The t-Test 124 -- Testing the Significance of o2: the X2 Test 128 -- 5.8 Hypothesis Testing: Some Practical Aspects 129 -- The Meaning of "Accepting" or "Rejecting" a Hypothesis 129 -- The "Zero" Null Hypothesis and the "2-t" Rule of Thumb 129 -- Forming the Null and Alternative Hypotheses 130 -- Choosing a, the Level of Significance 131 -- The Exact Level of Significance: The p Value 132 -- Statistical Significance versus Practical Significance 133 -- The Choice between Confidence-Interval and Test-of-Significance Approaches to Hypothesis Testing 134 -- 5.9 Regression Analysis and Analysis of Variance 134 -- 5.10 Application of Regression Analysis: The Problem of Prediction 137 -- Mean Prediction 137 -- Individual Prediction 138 -- 5.11 Reporting the Results of Regression Analysis 140 -- 5.12 Evaluating the Results of Regression Analysis 140 -- Normality Test 141 -- Other Tests of Model Adequacy 144 -- Exercises 145 -- Problems 147 -- Appendix 5A 152 -- 5A.1 Derivation of Equation (5.3.2) 152 -- 5A.2 Derivation of Equation (5.9.1) 152 -- 5A.3 Derivation of Equations (5.10.2) and (5.10.6) 153 -- Variance of Mean Prediction 153 -- Variance of Individual Prediction 153 -- 6 Extensions of the Two-Variable Linear Regression Model 155 -- 6.1 Regression through the Origin 155 -- r2 for Regression-through-Origin Model An Illustrative Example: The Characteristic Line of Portfolio Theory 159 -- 6.2 Scaling and Units of Measurement 161 -- A Numerical Example: The Relationship between GPDI and GNP, United States, 1974-1983 163 -- A Word about Interpretation 164 -- 6.3 Functional Forms of Regression Models 165 -- 6.4 How to Measure Elasticity: The Log-Linear Model 165 -- An Illustrative Example: The Coffee Demand Function Revisited 167 -- 6.5 Semilog Models: Log-Lin and Lin-Log Models 169 -- How to Measure the Growth Rate: The Log-Lin Model 169 -- The Lin-Log Model 172 -- 6.6 Reciprocal Models 173 -- An Illustrative Example: The Phillips Curve for the United Kingdom, 1950-1966 176 -- 6.7 Summary of Functional Forms 176 -- 6.8 A Note on the Nature of the Stochastic Error Term: Additive versus Multiplicative Stochastic Error Term 178 -- Exercises 180 -- Problems 183 -- Appendix 6A 186 -- 6A.1 Derivation of Least-Squares Estimators for Regression through the Origin 186 -- 6A.2 SAS Output of the Characteristic Line (6.1.12) 189 -- 6A.3 SAS Output of the United Kingdom Phillips Curve Regression (6.6.2) 190 -- 7 Multiple Regression Analysis: The Problem of Estimation 191 -- 7.1 The Three-Variable Model: Notation and Assumptions 192 -- 7.2 Interpretation of Multiple Regression Equation 194 -- 7.3 The Meaning of Partial Regression Coefficients 195 -- 7.4 OLS and ML Estimation of the Partial Regression Coefficients 197 -- OLS Estimators 197 -- Variances and Standard Errors of OLS Estimators 198 -- Properties of OLS Estimators 199 -- Maximum Likelihood Estimators 201 -- 7.5 The Multiple Coefficient of Determination R2 and the Multiple Coefficient of Correlation R 201 -- 7.6 Example 7.1: The Expectations-Augmented Phillips Curve for the United States, 1970-1982 203 -- 7.7 Simple Regression in the Context of Multiple Regression: Introduction to Specification Bias 204 -- 7.8 R2 and the Adjusted R2 207 -- Comparing Two R2 Values 209 -- Example 7.2: Coffee Demand Function Revisited 210 -- The "Game" of Maximizing R2 211 -- 7.9 Partial Correlation Coefficients 211 -- Explanation of Simple and Partial Correlation Coefficients 211 -- Interpretation of Simple and Partial Correlation Coefficients 213 -- 7.10 Example 7.3: The Cobb-Douglas Production Function: More on Functional Form 214 -- 7.11 Polynomial Regression Models 217 -- Example 7.4: Estimating the Total Cost Function 218 -- Empirical Results 220 -- Exercises 221 -- Problems 224 -- Appendix 7A 231 -- 7A.1 Derivation of OLS Estimators Given in Equations (7.4.3) and (7.4.5) 231 -- 7A.2 Equality between a1 of (7.3.5) and B2 of (7.4.7) 232 -- 7A.3 Derivation of Equation (7.4.19) 232 -- 7A.4 Maximum Likelihood Estimation of the Multiple Regression Model 233 -- 7A.5 The Proof that E(b12) = B2 + B3b32 (Equation 7.7.4) 234 -- 7A.6 SAS Output of the Expectations-Augmented Phillips Curve (7.6.2) 236 -- 7A.7 SAS Output of the Cobb-Douglas Production Function (7.10.4) 237 -- 8 Multiple Regression Analysis: The Problem of Inference 238 -- 8.1 The Normality Assumption Once Again 238 -- 8.2 Example 8.1: U.S. Personal Consumption and Personal Disposal Income Relation, 1956-1970 239 -- 8.3 Hypothesis Testing in Multiple Regression: General Comments 242 -- 8.4 Hypothesis Testing about Individual Partial Regression Coefficients 242 -- 8.5 Testing the Overall Significance of the Sample Regression 244 -- The Analysis of Variance Approach to Testing the Overall Significance of an Observed Multiple Regression: The F Test 245 -- An Important Relationship between R2 and F 248 -- The "Incremental," or "Marginal," Contribution of an Explanatory Variable 250 -- 8.6 Testing the Equality of Two Regression Coefficients 254 -- Example 8.2: The Cubic Cost Function Revisited 255 -- 8.7 Restricted Least Squares: Testing Linear Equality Restrictions 256 -- The t Test Approach 256 -- The F Test Approach: Restricted Least Squares 257 -- Example 8.3: The Cobb-Douglas Production Function for Taiwanese Agricultural Sector, 1958-1972 259 -- General F Testing 260 -- 8.8 Comparing Two Regressions: Testing for Structural Stability of Regression Models 262 -- 8.9 Testing the Functional Form of Regression: Choosing between Linear and Log-Linear Regression Models 265 -- Example 8.5: The Demand for Roses 266 -- 8.10 Prediction with Multiple Regression 267 -- 8.11 The Troika of Hypothesis Tests: The Likelihood Ratio (LR), Wald (W), and Lagrange Multiplier (LM) Tests 268 -- The Road Ahead 269 -- Exercises 270 -- Problems 273 -- Appendix 8A 280 -- Likelihood Ratio (LR) Test 280 -- 9 The Matrix Approach to Linear Regression Model 282.

Provides an introduction to econometrics and the developments in the theory and practice of econometrics.

Texto en inglés