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Basic Econometrics (Record no. 39434)

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000 -LEADER
fixed length control field	16057nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field	OSt
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9780071333450
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	330.015195 GUJ-B
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Gujrati, Damodar N
245 ## - TITLE STATEMENT
Title	Basic Econometrics
250 ## - EDITION STATEMENT
Edition statement	5th
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc	New Delhi
Name of publisher, distributor, etc	Mcgraw hill
Date of publication, distribution, etc	2009
300 ## - PHYSICAL DESCRIPTION
Extent	886p.
500 ## - GENERAL NOTE
General note	Contents<br/>Preface vii<br/>Acknowledgments xi<br/>Introduction 1<br/>I.1 What is Econometrics? 1<br/>I.2 Why a Separate Discipline? 2<br/>I.3 Methodology of Econometrics 2<br/>I.4 Types of Econometrics 9<br/>I.5 Mathematical and Statistical Prerequisites 10<br/>I.6 The Role of the Computer 10<br/>I.7 Suggestions for Further Reading 10<br/>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 15<br/>1.3 Statistical versus Deterministic Relationships 19<br/>1.4 Regression versus Causation 19<br/>1.5 Regression versus Correlation 20<br/>1.6 Terminology and Notation 20<br/>1.7 The Nature and Sources of Data for Economic Analysis 21<br/>Summary and Conclusions 28<br/>Multiple Choice Questions 29<br/>Exercises 32<br/>Key to Multiple Choice Questions 37<br/>2. Two-Variable Regression Analysis: Some Basic Ideas 38<br/>2.1 A Hypothetical Example 38<br/>2.2 The Concept of Population Regression Function (PRF) 41<br/>2.3 The Meaning of the Term Linear 42<br/>2.4 Stochastic Specifi cation of PRF 43<br/>xvi Contents<br/>2.5 The Signifi cance of the Stochastic Disturbance Term 45<br/>2.6 The Sample Regression Function (SRF) 46<br/>2.7 Illustrative Examples 49<br/>Summary and Conclusions 51<br/>Multiple Choice Questions 51<br/>Exercises 54<br/>Key to Multiple Choice Questions 60<br/>3. Two-Variable Regression Model: The Problem of Estimation 61<br/>3.1 The Method of Ordinary Least Squares 61<br/>3.2 The Classical Linear Regression Model: The Assumptions Underlying the<br/>Method of Least Squares 67<br/>3.3 Precision or Standard Errors of Least-Squares Estimates 74<br/>3.4 Properties of Least-Squares Estimators: The Gauss–Markov Theorem 76<br/>3.5 The Coeffi cient of Determination r2<br/>: A Measure of “Goodness of Fit” 78<br/>3.6 A Numerical Example 83<br/>3.7 Illustrative Examples 86<br/>3.8 A Note on Monte Carlo Experiments 88<br/>Summary and Conclusions 89<br/>Multiple Choice Questions 90<br/>Exercises 93<br/>Key to Multiple Choice Questions 99<br/>Appendix 3A 100<br/>4. Classical Normal Linear Regression Model (CNLRM) 105<br/>4.1 The Probability Distribution of Disturbances ui<br/>105<br/>4.2 The Normality Assumption for ui<br/>106<br/>4.3 Properties of OLS Estimators under the Normality Assumption 107<br/>4.4 The Method of Maximum Likelihood (ML) 109<br/>Summary and Conclusions 110<br/>Appendix 4A 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 115<br/>5.3 Confi dence Intervals for Regression Coeffi cients b1 and b2 117<br/>5.4 Confi dence Interval for s2 119<br/>5.5 Hypothesis Testing: General Comments 120<br/>5.6 Hypothesis Testing: The Confi dence-Interval Approach 121<br/>5.7 Hypothesis Testing: The Test-of-Signifi cance Approach 122<br/>5.8 Hypothesis Testing: Some Practical Aspects 127<br/>5.9 Regression Analysis and Analysis of Variance 131<br/>5.10 Application of Regression Analysis: The Problem of Prediction 133<br/>5.11 Reporting the Results of Regression Analysis 136<br/>5.12 Evaluating the Results of Regression Analysis 137<br/>Summary and Conclusions 140<br/>Multiple Choice Questions 141<br/>Exercises 146<br/>Contents xvii<br/>Key to Multiple Choice Questions 154<br/>Appendix 5A 155<br/>6. Extensions of the Two-Variable Linear Regression Model 159<br/>6.1 Regression through the Origin 159<br/>6.2 Scaling and Units of Measurement 166<br/>6.3 Regression on Standardized Variables 170<br/>6.4 Functional Forms of Regression Models 171<br/>6.5 How to Measure Elasticity: The Log-Linear Model 172<br/>6.6 Semilog Models: Log–Lin and Lin–Log Models 175<br/>6.7 Reciprocal Models 179<br/>6.8 Choice of Functional Form 184<br/>6.9 A Note on the Nature of the Stochastic Error Term: Additive versus<br/>Multiplicative Stochastic Error Term 186<br/>Summary and Conclusions 187<br/>Multiple Choice Questions 188<br/>Exercises 190<br/>Key to Multiple Choice Questions 196<br/>Appendix 6A 197<br/>7. Multiple Regression Analysis: The Problem of Estimation 203<br/>7.1 The Three-Variable Model: Notation and Assumptions 203<br/>7.2 Interpretation of Multiple Regression Equation 205<br/>7.3 The Meaning of Partial Regression Coeffi cients 205<br/>7.4 OLS and ML Estimation of the Partial Regression Coeffi cients 207<br/>7.5 The Multiple Coeffi cient of Determination R2<br/>and the<br/>Multiple Coeffi cient of Correlation R 210<br/>7.6 An Illustrative Example 212<br/>7.7 Simple Regression in the Context of Multiple Regression:<br/>Introduction to Specifi cation Bias 214<br/>7.8 R2<br/>and the Adjusted R2 215<br/>7.9 The Cobb–Douglas Production Function: More on Functional Form 220<br/>7.10 Polynomial Regression Models 223<br/>7.11 Partial Correlation Coeffi cients 226<br/>Summary and Conclusions 228<br/>Multiple Choice Questions 228<br/>Exercises 231<br/>Key to Multiple Choice Questions 243<br/>Appendix 7A 243<br/>8. Multiple Regression Analysis: The Problem of Inference 249<br/>8.1 The Normality Assumption Once Again 249<br/>8.2 Hypothesis Testing in Multiple Regression: General Comments 250<br/>8.3 Hypothesis Testing about Individual Regression Coeffi cients 251<br/>8.4 Testing the Overall Signifi cance of the Sample Regression 253<br/>8.5 Testing the Equality of Two Regression Coeffi cients 262<br/>8.6 Restricted Least Squares: Testing Linear Equality Restrictions 264<br/>8.7 Testing for Structural or Parameter Stability of Regression Models: The Chow Test 270<br/>xviii Contents<br/>8.8 Prediction with Multiple Regression 275<br/>8.9 The Troika of Hypothesis Tests: The Likelihood Ratio (LR), Wald (W), and<br/>Lagrange Multiplier (LM) Tests 275<br/>8.10 Testing the Functional Form of Regression: Choosing between<br/>Linear and Log–Linear Regression Models 276<br/>Summary and Conclusions 278<br/>Multiple Choice Questions 278<br/>Exercises 281<br/>Key to Multiple Choice Questions 292<br/>Appendix 8A 292<br/>9. Dummy Variable Regression Models 295<br/>9.1 The Nature of Dummy Variables 295<br/>9.2 ANOVA Models 296<br/>9.3 ANOVA Models with Two Qualitative Variables 300<br/>9.4 Regression with a Mixture of Quantitative and Qualitative Regressors:<br/>The ANCOVA Models 302<br/>9.5 The Dummy Variable Alternative to the Chow Test 303<br/>9.6 Interaction Effects Using Dummy Variables 306<br/>9.7 The Use of Dummy Variables in Seasonal Analysis 307<br/>9.8 Piecewise Linear Regression 311<br/>9.9 Panel Data Regression Models 314<br/>9.10 Some Technical Aspects of the Dummy Variable Technique 314<br/>9.11 Topics for Further Study 316<br/>9.12 A Concluding Example 316<br/>Summary and Conclusions 320<br/>Multiple Choice Questions 320<br/>Exercises 324<br/>Key to Multiple Choice Questions 332<br/>Appendix 9A 332<br/>PART 2 Relaxing the Assumptions of the Classical Model<br/>10. Multicollinearity: What Happens If the Regressors are Correlated? 339<br/>10.1 The Nature of Multicollinearity 340<br/>10.2 Estimation in the Presence of Perfect Multicollinearity 342<br/>10.3 Estimation in the Presence of “High” but “Imperfect” Multicollinearity 344<br/>10.4 Multicollinearity: Much Ado about Nothing? Theoretical Consequences of<br/>Multicollinearity 344<br/>10.5 Practical Consequences of Multicollinearity 346<br/>10.6 An Illustrative Example 351<br/>10.7 Detection of Multicollinearity 356<br/>10.8 Remedial Measures 360<br/>10.9 Is Multicollinearity Necessarily Bad? Maybe Not, If the Objective<br/>Is Prediction Only 365<br/>10.10 An Extended Example: The Longley Data 365<br/>Summary and Conclusions 368<br/>Contents xix<br/>Multiple Choice Questions 369<br/>Exercises 372<br/>Key to Multiple Choice Questions 385<br/>11. Heteroscedasticity: What Happens if the Error Variance is Nonconstant? 386<br/>11.1 The Nature of Heteroscedasticity 386<br/>11.2 OLS Estimation in the Presence of Heteroscedasticity 391<br/>11.3 The Method of Generalized Least Squares (GLS) 392<br/>11.4 Consequences of Using OLS in the Presence of Heteroscedasticity 395<br/>11.5 Detection of Heteroscedasticity 397<br/>11.6 Remedial Measures 410<br/>11.7 Concluding Examples 416<br/>11.8 A Caution about Overreacting to Heteroscedasticity 420<br/>Summary and Conclusions 421<br/>Multiple Choice Questions 421<br/>Exercises 424<br/>Key to Multiple Choice Questions 432<br/>Appendix 11A 432<br/>12. Autocorrelation: What Happens if the Error Terms are Correlated? 436<br/>12.1 The Nature of the Problem 437<br/>12.2 OLS Estimation in the Presence of Autocorrelation 443<br/>12.3 The BLUE Estimator in the Presence of Autocorrelation 445<br/>12.4 Consequences of Using OLS in the Presence of Autocorrelation 446<br/>12.5 Relationship between Wages and Productivity in the Business Sector of the<br/>United States, 1960–2005 451<br/>12.6 Detecting Autocorrelation 453<br/>12.7 What to do when you fi nd Autocorrelation: Remedial Measures 463<br/>12.8 Model Mis-Specifi cation versus Pure Autocorrelation 463<br/>12.9 Correcting for (Pure) Autocorrelation: The Method of Generalized<br/>Least Squares (GLS) 464<br/>12.10 The Newey–West Method of Correcting the OLS Standard Errors 470<br/>12.11 OLS versus FGLS and HAC 470<br/>12.12 Additional Aspects of Autocorrelation 471<br/>12.13 A Concluding Example 472<br/>Summary and Conclusions 474<br/>Multiple Choice Questions 475<br/>Exercises 478<br/>Key to Multiple Choice Questions 490<br/>Appendix 12A 491<br/>13. Econometric Modeling: Model Specifi cation and Diagnostic Testing 492<br/>13.1 Model Selection Criteria 493<br/>13.2 Types of Specifi cation Errors 493<br/>13.3 Consequences of Model Specifi cation Errors 495<br/>13.4 Tests of Specifi cation Errors 499<br/>13.5 Errors of Measurement 506<br/>xx Contents<br/>13.6 Incorrect Specifi cation of the Stochastic Error Term 510<br/>13.7 Nested versus Non-Nested Models 510<br/>13.8 Tests of Non-Nested Hypotheses 511<br/>13.9 Model Selection Criteria 516<br/>13.10 Additional Topics in Econometric Modeling 520<br/>13.11 Concluding Examples 524<br/>13.12 Non-Normal Errors and Stochastic Regressors 533<br/>13.13 A Word to the Practitioner 535<br/>Summary and Conclusions 536<br/>Multiple Choice Questions 537<br/>Exercises 540<br/>Key to Multiple Choice Questions 546<br/>Appendix 13A 546<br/>PART 3 Topics in Econometrics<br/>14. Nonlinear Regression Models 553<br/>14.1 Intrinsically Linear and Intrinsically Nonlinear Regression Models 553<br/>14.2 Estimation of Linear and Nonlinear Regression Models 555<br/>14.3 Estimating Nonlinear Regression Models: The Trial-and-Error Method 555<br/>14.4 Approaches to Estimating Nonlinear Regression Models 557<br/>14.5 Illustrative Examples 558<br/>Summary and Conclusions 562<br/>Multiple Choice Questions 563<br/>Exercises 565<br/>Key to Multiple Choice Questions 566<br/>Appendix 14A 567<br/>15. Qualitative Response Regression Models 570<br/>15.1 The Nature of Qualitative Response Models 570<br/>15.2 The Linear Probability Model (LPM) 572<br/>15.3 Applications of LPM 578<br/>15.4 Alternatives to LPM 581<br/>15.5 The Logit Model 582<br/>15.6 Estimation of the Logit Model 584<br/>15.7 The Grouped Logit (Glogit) Model: A Numerical Example 587<br/>15.8 The Logit Model for Ungrouped or Individual Data 590<br/>15.9 The Probit Model 594<br/>15.10 Logit and Probit Models 599<br/>15.11 The Tobit Model 602<br/>15.12 Modeling Count Data: The Poisson Regression Model 604<br/>15.13 Further Topics in Qualitative Response Regression Models 607<br/>Summary and Conclusions 609<br/>Multiple Choice Questions 610<br/>Exercises 613<br/>Key to Multiple Choice Questions 620<br/>Appendix 15A 620<br/>Contents xxi<br/>16. Panel Data Regression Models 622<br/>16.1 Why Panel Data? 623<br/>16.2 Panel Data: An Illustrative Example 624<br/>16.3 Pooled OLS Regression or Constant Coeffi cients Model 625<br/>16.4 The Fixed Effect Least-Squares Dummy Variable (LSDV) Model 627<br/>16.5 The Fixed-Effect Within-Group (WG) Estimator 630<br/>16.6 The Random Effects Model (REM) 633<br/>16.7 Properties of Various Estimators 637<br/>16.8 Fixed Effects versus Random Effects Model: Some Guidelines 637<br/>16.9 Panel Data Regressions: Some Concluding Comments 638<br/>16.10 Some Illustrative Examples 639<br/>Summary and Conclusions 644<br/>Multiple Choice Questions 645<br/>Exercises 648<br/>Key to Multiple Choice Questions 651<br/>17. Dynamic Econometric Models: Autoregressive and Distributed-Lag Models 652<br/>17.1 The Role of “Time,” or “Lag,” in Economics 653<br/>17.2 The Reasons for Lags 657<br/>17.3 Estimation of Distributed-Lag Models 658<br/>17.4 The Koyck Approach to Distributed-Lag Models 659<br/>17.5 Rationalization of the Koyck Model: The Adaptive Expectations Model 664<br/>17.6 Another Rationalization of the Koyck Model: The Stock<br/>Adjustment, or Partial Adjustment, Model 666<br/>17.7 Combination of Adaptive Expectations and Partial Adjustment Models 668<br/>17.8 Estimation of Autoregressive Models 669<br/>17.9 The Method of Instrumental Variables (IV) 670<br/>17.10 Detecting Autocorrelation in Autoregressive Models: Durbin h Test 671<br/>17.11 A Numerical Example: The Demand for Money in Canada,<br/>1979–I to 1988–IV 673<br/>17.12 Illustrative Examples 676<br/>17.13 The Almon Approach to Distributed-Lag Models:<br/>The Almon or Polynomial Distributed Lag (PDL) 679<br/>17.14 Causality in Economics: The Granger Causality Test 686<br/>Summary and Conclusions 692<br/>Multiple Choice Questions 693<br/>Exercises 696<br/>Key to Multiple Choice Questions 705<br/>Appendix 17A 705<br/>PART 4 Simultaneous-Equation Models and Time Series Econometrics<br/>18. Simultaneous-Equation Models 709<br/>18.1 The Nature of Simultaneous-Equation Models 709<br/>18.2 Examples of Simultaneous-Equation Models 710<br/>18.3 The Simultaneous-Equation Bias: Inconsistency of OLS Estimators 715<br/>18.4 The Simultaneous-Equation Bias: A Numerical Example 718<br/>Summary and Conclusions 720<br/>xxii Contents<br/>Multiple Choice Questions 720<br/>Exercises 721<br/>Key to Multiple Choice Questions 725<br/>19. The Identifi cation Problem 726<br/>19.1 Notations and Defi nitions 726<br/>19.2 The Identifi cation Problem 729<br/>19.3 Rules for Identifi cation 736<br/>19.4 A Test of Simultaneity 740<br/>19.5 Tests for Exogeneity 743<br/>Summary and Conclusions 743<br/>Multiple Choice Questions 744<br/>Exercises 746<br/>Key to Multiple Choice Questions 750<br/>20. Simultaneous-Equation Methods 751<br/>20.1 Approaches to Estimation 751<br/>20.2 Recursive Models and Ordinary Least Squares 753<br/>20.3 Estimation of a Just Identifi ed Equation: The Method of Indirect Least Squares (ILS) 755<br/>20.4 Estimation of an Overidentifi ed Equation: The Method of<br/>Two-Stage Least Squares (2SLS) 758<br/>20.5 2SLS: A Numerical Example 761<br/>20.6 Illustrative Examples 764<br/>Summary and Conclusions 770<br/>Multiple Choice Questions 771<br/>Exercises 773<br/>Key to Multiple Choice Questions 777<br/>Appendix 20A 777<br/>21. Time Series Econometrics: Some Basic Concepts 780<br/>21.1 A Look at Selected U.S. Economic Time Series 781<br/>21.2 Key Concepts 782<br/>21.3 Stochastic Processes 783<br/>21.4 Unit Root Stochastic Process 787<br/>21.5 Trend Stationary (TS) and Difference Stationary (DS)<br/>Stochastic Processes 788<br/>21.6 Integrated Stochastic Processes 789<br/>21.7 The Phenomenon of Spurious Regression 790<br/>21.8 Tests of Stationarity 791<br/>21.9 The Unit Root Test 797<br/>21.10 Transforming Nonstationary Time Series 802<br/>21.11 Cointegration: Regression of a Unit Root Time Series on<br/>Another Unit Root Time Series 805<br/>21.12 Some Economic Applications 808<br/>Summary and Conclusions 811<br/>Multiple Choice Questions 812<br/>Exercises 815<br/>Key to Multiple Choice Questions 819<br/>Contents xxiii<br/>22. Time Series Econometrics: Forecasting 820<br/>22.1 Approaches to Economic Forecasting 820<br/>22.2 AR, MA, and ARIMA Modeling of Time Series Data 822<br/>22.3 The Box–Jenkins (BJ) Methodology 824<br/>22.4 Identifi cation 825<br/>22.5 Estimation of the ARIMA Model 829<br/>22.6 Diagnostic Checking 829<br/>22.7 Forecasting 830<br/>22.8 Further Aspects of the BJ Methodology 831<br/>22.9 Vector Autoregression (VAR) 831<br/>22.10 Measuring Volatility in Financial Time Series: The ARCH and GARCH Models 838<br/>22.11 Concluding Examples 843<br/>Summary and Conclusions 845<br/>Multiple Choice Questions 846<br/>Exercises 848<br/>Key to Multiple Choice Questions 850<br/>Appendix D Statistical Tables 851<br/>Selected Bibliography 868<br/>Index 873
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Economics
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Uniform Resource Identifier	<a href="https://books.google.co.in/books?id=WcCjAgAAQBAJ&printsec=frontcover&dq=Basic+Econometrics&hl=en&sa=X&ved=0ahUKEwiypaPPidXkAhWHMo8KHY6tCiEQ6AEIKDAA#v=onepage&q=Basic%20Econometrics&f=false">https://books.google.co.in/books?id=WcCjAgAAQBAJ&printsec=frontcover&dq=Basic+Econometrics&hl=en&sa=X&ved=0ahUKEwiypaPPidXkAhWHMo8KHY6tCiEQ6AEIKDAA#v=onepage&q=Basic%20Econometrics&f=false</a>
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Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	5	330.015195 GUJ-B	26584	05/09/2024	22/08/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books
Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	9	330.015195 GUJ-B	26585	05/09/2024	22/08/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books
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Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	9	330.015195 GUJ-B	26587	26/09/2024	13/09/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books
Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	9	330.015195 GUJ-B	26588	12/09/2024	29/08/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books
Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	10	330.015195 GUJ-B	26589	30/09/2024	13/09/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books
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Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	7	330.015195 GUJ-B	26591	03/06/2024	17/05/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books
Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	10	330.015195 GUJ-B	26592	05/04/2024	05/04/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books	19/04/2024
Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ABS	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	6	330.015195 GUJ-B	26593	28/03/2022	28/03/2022	https://epgp.inflibnet.ac.in/	15/03/2018	Books	11/04/2022
Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ASE	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	7	330.015195 GUJ-B	26594	18/09/2023	31/08/2023	https://epgp.inflibnet.ac.in/	15/03/2018	Books
Dewey Decimal Classification	Text Book	Amity Central Library	Amity Central Library	ASE	15/03/2018	SBA	995.00	SBA / 12119 28/02/2018	9	330.015195 GUJ-B	26595	24/05/2024	10/04/2024	https://epgp.inflibnet.ac.in/	15/03/2018	Books