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Basic Econometrics (Record no. 4148)

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000 -LEADER
fixed length control field	16062nam a2200205Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field	OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20190918020003.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	140801s2009 xx 000 0 und d
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	Gujarati, Damodra N
245 ## - TITLE STATEMENT
Title	Basic Econometrics
250 ## - EDITION STATEMENT
Edition statement	5th
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc	Mcgraw -Hill
Date of publication, distribution, etc	2009
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
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Damodra N Gujarati
856 ## - ELECTRONIC LOCATION AND ACCESS
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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Source of classification or shelving scheme	Dewey Decimal Classification
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Koha issues (borrowed), all copies	20

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Dewey Decimal Classification	Not For Loan	Reference	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00		330.015195 GUJ-B	19699	23/08/2014	https://epgp.inflibnet.ac.in/	03/01/2014	Reference Book
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	18	330.015195 GUJ-B	19700	08/11/2024	https://epgp.inflibnet.ac.in/	03/01/2014	Books	22/11/2024	08/11/2024
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	12	330.015195 GUJ-B	19701	09/10/2024	https://epgp.inflibnet.ac.in/	03/01/2014	Books		26/09/2024
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	17	330.015195 GUJ-B	19702	28/01/2019	https://epgp.inflibnet.ac.in/	03/01/2014	Books		25/09/2018
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	11	330.015195 GUJ-B	19703	23/10/2024	https://epgp.inflibnet.ac.in/	03/01/2014	Books		04/10/2024
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	14	330.015195 GUJ-B	19704	03/12/2018	https://epgp.inflibnet.ac.in/	03/01/2014	Books		31/07/2018
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	4	330.015195 GUJ-B	19705	24/06/2016	https://epgp.inflibnet.ac.in/	03/01/2014	Books	08/07/2016	24/06/2016
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	15	330.015195 GUJ-B	19706	23/10/2024	https://epgp.inflibnet.ac.in/	03/01/2014	Books		09/10/2024
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	10	330.015195 GUJ-B	19707	04/11/2019	https://epgp.inflibnet.ac.in/	03/01/2014	Books		17/09/2019
Dewey Decimal Classification		Text Book	Amity Central Library	Amity Central Library	ASE	03/01/2014	715.00	18	330.015195 GUJ-B	19708	16/04/2018	https://epgp.inflibnet.ac.in/	03/01/2014	Books		23/02/2018	3