MARC details
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11225cam a2200385 a 4500 |
| 001 - CONTROL NUMBER |
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310 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
BD-DhEWU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20140302104722.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
940825s1995 nyua g b 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
0070252149 (alk. paper) |
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| International Standard Book Number |
9780070252141 |
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| International Standard Book Number |
007113963X (alk. paper : International ed.) |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)31075018 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Transcribing agency |
DLC |
| Modifying agency |
DLC |
| -- |
BD-DhEWU |
| Language of cataloging |
eng |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
HB139 |
| Item number |
.G84 1995 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
330.015195 |
| Edition number |
20 |
| Item number |
GUB 1995 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Gujarati, Damodar N. |
| 9 (RLIN) |
2735 |
| 245 10 - TITLE STATEMENT |
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| Title |
Basic econometrics / |
| Statement of responsibility, etc |
Damodar N. Gujarati. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
3rd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
New York : |
| Name of publisher, distributor, etc |
McGraw-Hill, |
| Date of publication, distribution, etc |
c1995. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxiii, 838 p. : |
| Other physical details |
ill. ; |
| Dimensions |
25 cm. |
| 500 ## - GENERAL NOTE |
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| General note |
"International edition"-- Verso of t.p. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references (p. 824-826) and indexes. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
TOC |
| Formatted contents note |
Part 1 Single-Equation Regression Models --<br/>1 The Nature of Regression Analysis 15 --<br/>1.1 Historical Origin of the Term "Regression" 15 --<br/>1.2 The Modern Interpretation of Regression 16 --<br/>Examples 16 --<br/>1.3 Statistical vs. Deterministic Relationships 19 --<br/>1.4 Regression vs. Causation 20 --<br/>1.5 Regression vs. Correlation 21 --<br/>1.6 Terminology and Notation 22 --<br/>1.7 The Nature and Sources of Data for Econometric Analysis 23 --<br/>Types of Data 23 --<br/>The Sources of Data 24 --<br/>The Accuracy of Data 26 --<br/>Exercises 28 --<br/>Appendix 1A 29 --<br/>1A.1 Sources of Economic Data 29 --<br/>1A.2 Sources of Financial Data 31 --<br/>2 Two-Variable Regression Analysis: Some Basic Ideas 32 --<br/>2.1 A Hypothetical Example 32 --<br/>2.2 The Concept of Population Regression Function (PRF) 36 --<br/>2.3 The Meaning of the Term "Linear" 36 --<br/>Linearity in the Variables 37 --<br/>Linearity in the Parameters 37 --<br/>2.4 Stochastic Specification of PRF 38 --<br/>2.5 The Significance of the Stochastic Disturbance Term 39 --<br/>2.6 The Sample Regression Function (SRF) 41 --<br/>Exercises 45 --<br/>3 Two-Variable Regression Model: The Problem of Estimation 52 --<br/>3.1 The Method of Ordinary Least Squares 52 --<br/>3.2 The Classical Linear Regression Model: The Assumptions Underlying the Method of Least Squares 59 --<br/>How Realistic Are These Assumptions? 68 --<br/>3.3 Precision or Standard Errors of Least-Squares Estimates 69 --<br/>3.4 Properties of Least-Squares Estimators: The Gauss-Markov Theorem 72 --<br/>3.5 The Coefficient of Determination r2: A Measure of "Goodness of Fit" 74 --<br/>3.6 A Numerical Example 80 --<br/>3.7 Illustrative Examples 83 --<br/>Coffee Consumption in the United States, 1970-1980 83 --<br/>Keynesian Consumption Function for the United States, 1980-1991 84 --<br/>3.8 Computer Output for the Coffee Demand Function 85 --<br/>3.9 A Note on Monte Carlo Experiments 85 --<br/>Exercises 87 --<br/>Problems 89 --<br/>Appendix 3A 94 --<br/>3A.1 Derivation of Least-Squares Estimates 94 --<br/>3A.2 Linearity and Unbiasedness Properties of Least-Squares Estimators 94 --<br/>3A.3 Variances and Standard Errors of Least-Squares Estimators 95 --<br/>3A.4 Covariance between B1 and B2 96 --<br/>3A.5 The Least-Squares Estimator of o2 96 --<br/>3A.6 Minimum-Variance Property of Least-Squares Estimators 97 --<br/>3A.7 SAS Output of the Coffee Demand Function (3.7.1) 99 --<br/>4 The Normality Assumption: Classical Normal Linear Regression Model (CNLRM) 101 --<br/>4.1 The Probability Distribution of Disturbances ui 101 --<br/>4.2 The Normality Assumption 102 --<br/>4.3 Properties of OLS Estimators under the Normality Assumption 104 --<br/>4.4 The Method of Maximum Likelihood (ML) 107 --<br/>4.5 Probability Distributions Related to the Normal Distribution: The t, Chi-square (X2), and F Distributions 107 --<br/>Appendix 4A 110 --<br/>Maximum Likelihood Estimation of Two-Variable Regression Model 110 --<br/>Maximum Likelihood Estimation of the Consumption-Income Example 113 --<br/>Appendix 4A Exercises 113 --<br/>5 Two-Variable Regression: Interval Estimation and Hypothesis Testing 115 --<br/>5.1 Statistical Prerequisites 115 --<br/>5.2 Interval Estimation: Some Basic Ideas 116 --<br/>5.3 Confidence Intervals for Regression Coefficients B1 and B2 117 --<br/>Confidence Interval for B2 117 --<br/>Confidence Interval for B1 119 --<br/>Confidence Interval for B1 and B2 Simultaneously 120 --<br/>5.4 Confidence Interval for o2 120 --<br/>5.5 Hypothesis Testing: General Comments 121 --<br/>5.6 Hypothesis Testing: The Confidence-Interval Approach 122 --<br/>Two-Sided or Two-Tail Test 122 --<br/>One-Sided or One-Tail Test 124 --<br/>5.7 Hypothesis Testing: The Test-of-Significance Approach 124 --<br/>Testing the Significance of Regression Coefficients: The t-Test 124 --<br/>Testing the Significance of o2: the X2 Test 128 --<br/>5.8 Hypothesis Testing: Some Practical Aspects 129 --<br/>The Meaning of "Accepting" or "Rejecting" a Hypothesis 129 --<br/>The "Zero" Null Hypothesis and the "2-t" Rule of Thumb 129 --<br/>Forming the Null and Alternative Hypotheses 130 --<br/>Choosing a, the Level of Significance 131 --<br/>The Exact Level of Significance: The p Value 132 --<br/>Statistical Significance versus Practical Significance 133 --<br/>The Choice between Confidence-Interval and Test-of-Significance Approaches to Hypothesis Testing 134 --<br/>5.9 Regression Analysis and Analysis of Variance 134 --<br/>5.10 Application of Regression Analysis: The Problem of Prediction 137 --<br/>Mean Prediction 137 --<br/>Individual Prediction 138 --<br/>5.11 Reporting the Results of Regression Analysis 140 --<br/>5.12 Evaluating the Results of Regression Analysis 140 --<br/>Normality Test 141 --<br/>Other Tests of Model Adequacy 144 --<br/>Exercises 145 --<br/>Problems 147 --<br/>Appendix 5A 152 --<br/>5A.1 Derivation of Equation (5.3.2) 152 --<br/>5A.2 Derivation of Equation (5.9.1) 152 --<br/>5A.3 Derivation of Equations (5.10.2) and (5.10.6) 153 --<br/>Variance of Mean Prediction 153 --<br/>Variance of Individual Prediction 153 --<br/>6 Extensions of the Two-Variable Linear Regression Model 155 --<br/>6.1 Regression through the Origin 155 --<br/>r2 for Regression-through-Origin Model An Illustrative Example: The Characteristic Line of Portfolio Theory 159 --<br/>6.2 Scaling and Units of Measurement 161 --<br/>A Numerical Example: The Relationship between GPDI and GNP, United States, 1974-1983 163 --<br/>A Word about Interpretation 164 --<br/>6.3 Functional Forms of Regression Models 165 --<br/>6.4 How to Measure Elasticity: The Log-Linear Model 165 --<br/>An Illustrative Example: The Coffee Demand Function Revisited 167 --<br/>6.5 Semilog Models: Log-Lin and Lin-Log Models 169 --<br/>How to Measure the Growth Rate: The Log-Lin Model 169 --<br/>The Lin-Log Model 172 --<br/>6.6 Reciprocal Models 173 --<br/>An Illustrative Example: The Phillips Curve for the United Kingdom, 1950-1966 176 --<br/>6.7 Summary of Functional Forms 176 --<br/>6.8 A Note on the Nature of the Stochastic Error Term: Additive versus Multiplicative Stochastic Error Term 178 --<br/>Exercises 180 --<br/>Problems 183 --<br/>Appendix 6A 186 --<br/>6A.1 Derivation of Least-Squares Estimators for Regression through the Origin 186 --<br/>6A.2 SAS Output of the Characteristic Line (6.1.12) 189 --<br/>6A.3 SAS Output of the United Kingdom Phillips Curve Regression (6.6.2) 190 --<br/>7 Multiple Regression Analysis: The Problem of Estimation 191 --<br/>7.1 The Three-Variable Model: Notation and Assumptions 192 --<br/>7.2 Interpretation of Multiple Regression Equation 194 --<br/>7.3 The Meaning of Partial Regression Coefficients 195 --<br/>7.4 OLS and ML Estimation of the Partial Regression Coefficients 197 --<br/>OLS Estimators 197 --<br/>Variances and Standard Errors of OLS Estimators 198 --<br/>Properties of OLS Estimators 199 --<br/>Maximum Likelihood Estimators 201 --<br/>7.5 The Multiple Coefficient of Determination R2 and the Multiple Coefficient of Correlation R 201 --<br/>7.6 Example 7.1: The Expectations-Augmented Phillips Curve for the United States, 1970-1982 203 --<br/>7.7 Simple Regression in the Context of Multiple Regression: Introduction to Specification Bias 204 --<br/>7.8 R2 and the Adjusted R2 207 --<br/>Comparing Two R2 Values 209 --<br/>Example 7.2: Coffee Demand Function Revisited 210 --<br/>The "Game" of Maximizing R2 211 --<br/>7.9 Partial Correlation Coefficients 211 --<br/>Explanation of Simple and Partial Correlation Coefficients 211 --<br/>Interpretation of Simple and Partial Correlation Coefficients 213 --<br/>7.10 Example 7.3: The Cobb-Douglas Production Function: More on Functional Form 214 --<br/>7.11 Polynomial Regression Models 217 --<br/>Example 7.4: Estimating the Total Cost Function 218 --<br/>Empirical Results 220 --<br/>Exercises 221 --<br/>Problems 224 --<br/>Appendix 7A 231 --<br/>7A.1 Derivation of OLS Estimators Given in Equations (7.4.3) and (7.4.5) 231 --<br/>7A.2 Equality between a1 of (7.3.5) and B2 of (7.4.7) 232 --<br/>7A.3 Derivation of Equation (7.4.19) 232 --<br/>7A.4 Maximum Likelihood Estimation of the Multiple Regression Model 233 --<br/>7A.5 The Proof that E(b12) = B2 + B3b32 (Equation 7.7.4) 234 --<br/>7A.6 SAS Output of the Expectations-Augmented Phillips Curve (7.6.2) 236 --<br/>7A.7 SAS Output of the Cobb-Douglas Production Function (7.10.4) 237 --<br/>8 Multiple Regression Analysis: The Problem of Inference 238 --<br/>8.1 The Normality Assumption Once Again 238 --<br/>8.2 Example 8.1: U.S. Personal Consumption and Personal Disposal Income Relation, 1956-1970 239 --<br/>8.3 Hypothesis Testing in Multiple Regression: General Comments 242 --<br/>8.4 Hypothesis Testing about Individual Partial Regression Coefficients 242 --<br/>8.5 Testing the Overall Significance of the Sample Regression 244 --<br/>The Analysis of Variance Approach to Testing the Overall Significance of an Observed Multiple Regression: The F Test 245 --<br/>An Important Relationship between R2 and F 248 --<br/>The "Incremental," or "Marginal," Contribution of an Explanatory Variable 250 --<br/>8.6 Testing the Equality of Two Regression Coefficients 254 --<br/>Example 8.2: The Cubic Cost Function Revisited 255 --<br/>8.7 Restricted Least Squares: Testing Linear Equality Restrictions 256 --<br/>The t Test Approach 256 --<br/>The F Test Approach: Restricted Least Squares 257 --<br/>Example 8.3: The Cobb-Douglas Production Function for Taiwanese Agricultural Sector, 1958-1972 259 --<br/>General F Testing 260 --<br/>8.8 Comparing Two Regressions: Testing for Structural Stability of Regression Models 262 --<br/>8.9 Testing the Functional Form of Regression: Choosing between Linear and Log-Linear Regression Models 265 --<br/>Example 8.5: The Demand for Roses 266 --<br/>8.10 Prediction with Multiple Regression 267 --<br/>8.11 The Troika of Hypothesis Tests: The Likelihood Ratio (LR), Wald (W), and Lagrange Multiplier (LM) Tests 268 --<br/>The Road Ahead 269 --<br/>Exercises 270 --<br/>Problems 273 --<br/>Appendix 8A 280 --<br/>Likelihood Ratio (LR) Test 280 --<br/>9 The Matrix Approach to Linear Regression Model 282 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
An introduction to econometrics which discusses techniques and topics suitable for a first-year undergraduate course. The text assumes a statistics course as a prerequisite and contains an appendix on fundamental statistics. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Program name |
Economics |
| 590 ## - LOCAL NOTE (RLIN) |
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| Local note |
Sagar Shahanawaz |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Econometrics. |
| 9 (RLIN) |
76 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
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| Materials specified |
Publisher description |
| Uniform Resource Identifier |
http://www.loc.gov/catdir/enhancements/fy0602/94035295-d.html |
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| Materials specified |
Table of contents only |
| Uniform Resource Identifier |
http://www.loc.gov/catdir/enhancements/fy0602/94035295-t.html |
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| Materials specified |
WorldCat details |
| Uniform Resource Identifier |
http://www.worldcat.org/title/basic-econometrics/oclc/31075018&referer=brief_results |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Text |