TY  - BOOK
AU  - Gujarati,Damodar N.
TI  - Basic econometrics
PY  - 1995///
CY  - New York
PB  - McGraw-Hill
KW  - Econometría
KW  - Economía matemática
KW  - Libros de texto
KW  - Educación superior
KW  - Estadística matemática
N1  - Libro con rayones en partes del libro; 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
N2  - Provides an introduction to econometrics and the developments in the theory and practice of econometrics
ER  -