Principles of econometrics / (Record no. 240920)
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| fixed length control field | 27616nam a22002777a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260305152053.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260219b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789363865778 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | AIMIT LIBRARY |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 5 |
| Classification number | 330.015195 |
| Item number | HILR |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Hill, R. Carter. |
| 9 (RLIN) | 255747 |
| 245 ## - TITLE STATEMENT | |
| Title | Principles of econometrics / |
| Statement of responsibility, etc. | By R. Carter Hill ...[et.al.]. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 5th ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi : |
| Name of publisher, distributor, etc. | Wiley India Pvt Ltd, |
| Date of publication, distribution, etc. | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxxi,633 p.; |
| Other physical details | PB |
| Dimensions | 25.5 cm. |
| 500 ## - GENERAL NOTE | |
| General note | Principles of Econometrics, Fifth Edition, is an introductory book for undergraduate students in economics and finance, as well as first-year graduate students in a variety of fields that include economics, finance, accounting, marketing, public policy, sociology, law, and political science. Students will gain a working knowledge of basic econometrics so they can apply modeling, estimation, inference, and forecasting techniques when working with real-world economic problems. Readers will also gain an understanding of econometrics that allows them to critically evaluate the results of others’ economic research and modeling, and that will serve as a foundation for further study of the field.<br/><br/>This new edition of the highly-regarded econometrics text includes major revisions that both reorganize the content and present students with plentiful opportunities to practice what they have read in the form of chapter-end exercises. |
| 501 ## - WITH NOTE | |
| With note | Features<br/>Wiley Advantage:<br/><br/>Complete solutions manual for professors is available online in both Microsoft Word and PDF formats<br/>New examples and exercises use real data to make the material more relevant<br/>Chapters are focused on core material and exercises, while more advanced content is presented in the appendices<br/>Between 25 and 30 new exercises have been added to each chapter to help students apply what they’ve learned<br/>Reorganization of chapters follows a natural progression that is conducive to undergraduate and graduate-level instruction |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of Contents<br/>Preface v<br/><br/>List of Examples xxi<br/><br/>1 An Introduction to Econometrics 1<br/><br/>1.1 Why Study Econometrics? 1<br/><br/>1.2 What Is Econometrics About? 2<br/><br/>1.2.1 Some Examples 3<br/><br/>1.3 The Econometric Model 4<br/><br/>1.3.1 Causality and Prediction 5<br/><br/>1.4 How Are Data Generated? 5<br/><br/>1.4.1 Experimental Data 6<br/><br/>1.4.2 Quasi-Experimental Data 6<br/><br/>1.4.3 Nonexperimental Data 7<br/><br/>1.5 Economic Data Types 7<br/><br/>1.5.1 Time-Series Data 7<br/><br/>1.5.2 Cross-Section Data 8<br/><br/>1.5.3 Panel or Longitudinal Data 9<br/><br/>1.6 The Research Process 9<br/><br/>1.7 Writing an Empirical Research Paper 11<br/><br/>1.7.1 Writing a Research Proposal 11<br/><br/>1.7.2 A Format for Writing a Research Report 11<br/><br/>1.8 Sources of Economic Data 13<br/><br/>1.8.1 Links to Economic Data on the Internet 13<br/><br/>1.8.2 Interpreting Economic Data 14<br/><br/>1.8.3 Obtaining the Data 14<br/><br/>Probability Primer 15<br/><br/>P.1 Random Variables 16<br/><br/>P.2 Probability Distributions 17<br/><br/>P.3 Joint, Marginal, and Conditional Probabilities 20<br/><br/>P.3.1 Marginal Distributions 20<br/><br/>P.3.2 Conditional Probability 21<br/><br/>P.3.3 Statistical Independence 21<br/><br/>P.4 A Digression: Summation Notation 22<br/><br/>P.5 Properties of Probability Distributions 23<br/><br/>P.5.1 Expected Value of a Random Variable 24<br/><br/>P.5.2 Conditional Expectation 25<br/><br/>P.5.3 Rules for Expected Values 25<br/><br/>P.5.4 Variance of a Random Variable 26<br/><br/>P.5.5 Expected Values of Several Random Variables 27<br/><br/>P.5.6 Covariance Between Two Random Variables 27<br/><br/>P.6 Conditioning 29<br/><br/>P.6.1 Conditional Expectation 30<br/><br/>P.6.2 Conditional Variance 31<br/><br/>P.6.3 Iterated Expectations 32<br/><br/>P.6.4 Variance Decomposition 33<br/><br/>P.6.5 Covariance Decomposition 34<br/><br/>P.7 The Normal Distribution 34<br/><br/>P.7.1 The Bivariate Normal Distribution 37<br/><br/>P.8 Exercises 39<br/><br/>2 The Simple Linear Regression Model 46<br/><br/>2.1 An Economic Model 47<br/><br/>2.2 An Econometric Model 49<br/><br/>2.2.1 Data Generating Process 51<br/><br/>2.2.2 The Random Error and Strict Exogeneity 52<br/><br/>2.2.3 The Regression Function 53<br/><br/>2.2.4 Random Error Variation 54<br/><br/>2.2.5 Variation in x 56<br/><br/>2.2.6 Error Normality 56<br/><br/>2.2.7 Generalizing the Exogeneity Assumption 56<br/><br/>2.2.8 Error Correlation 57<br/><br/>2.2.9 Summarizing the Assumptions 58<br/><br/>2.3 Estimating the Regression Parameters 59<br/><br/>2.3.1 The Least Squares Principle 61<br/><br/>2.3.2 Other Economic Models 65<br/><br/>2.4 Assessing the Least Squares Estimators 66<br/><br/>2.4.1 The Estimator b2 67<br/><br/>2.4.2 The Expected Values of b1 and b2 68<br/><br/>2.4.3 Sampling Variation 69<br/><br/>2.4.4 The Variances and Covariance of b1 and b2 69<br/><br/>2.5 The Gauss–Markov Theorem 72<br/><br/>2.6 The Probability Distributions of the Least Squares Estimators 73<br/><br/>2.7 Estimating the Variance of the Error Term 74<br/><br/>2.7.1 Estimating the Variances and Covariance of the Least Squares Estimators 74<br/><br/>2.7.2 Interpreting the Standard Errors 76<br/><br/>2.8 Estimating Nonlinear Relationships 77<br/><br/>2.8.1 Quadratic Functions 77<br/><br/>2.8.2 Using a Quadratic Model 77<br/><br/>2.8.3 A Log-Linear Function 79<br/><br/>2.8.4 Using a Log-Linear Model 80<br/><br/>2.8.5 Choosing a Functional Form 82<br/><br/>2.9 Regression with Indicator Variables 82<br/><br/>2.10 The Independent Variable 84<br/><br/>2.10.1 Random and Independent x 84<br/><br/>2.10.2 Random and Strictly Exogenous x 86<br/><br/>2.10.3 Random Sampling 87<br/><br/>2.11 Exercises 89<br/><br/>2.11.1 Problems 89<br/><br/>2.11.2 Computer Exercises 93<br/><br/>Appendix 2A Derivation of the Least Squares Estimates 98<br/><br/>Appendix 2B Deviation from the Mean Form of b2 99<br/><br/>Appendix 2C b2 Is a Linear Estimator 100<br/><br/>Appendix 2D Derivation of Theoretical Expression for b2 100<br/><br/>Appendix 2E Deriving the Conditional Variance of b2 100<br/><br/>Appendix 2F Proof of the Gauss–Markov Theorem 102<br/><br/>Appendix 2G Proofs of Results Introduced in Section 2.10 103<br/><br/>2G.1 The Implications of Strict Exogeneity 103<br/><br/>2G.2 The Random and Independent x Case 103<br/><br/>2G.3 The Random and Strictly Exogenous x Case 105<br/><br/>2G.4 Random Sampling 106<br/><br/>Appendix 2H Monte Carlo Simulation 106<br/><br/>2H.1 The Regression Function 106<br/><br/>2H.2 The Random Error 107<br/><br/>2H.3 Theoretically True Values 107<br/><br/>2H.4 Creating a Sample of Data 108<br/><br/>2H.5 Monte Carlo Objectives 109<br/><br/>2H.6 Monte Carlo Results 109<br/><br/>2H.7 Random-x Monte Carlo Results 110<br/><br/>3 Interval Estimation and Hypothesis Testing 112<br/><br/>3.1 Interval Estimation 113<br/><br/>3.1.1 The t-Distribution 113<br/><br/>3.1.2 Obtaining Interval Estimates 115<br/><br/>3.1.3 The Sampling Context 116<br/><br/>3.2 Hypothesis Tests 118<br/><br/>3.2.1 The Null Hypothesis 118<br/><br/>3.2.2 The Alternative Hypothesis 118<br/><br/>3.2.3 The Test Statistic 119<br/><br/>3.2.4 The Rejection Region 119<br/><br/>3.2.5 A Conclusion 120<br/><br/>3.3 Rejection Regions for Specific Alternatives 120<br/><br/>3.3.1 One-Tail Tests with Alternative ‘‘Greater Than’’ (>) 120<br/><br/>3.3.2 One-Tail Tests with Alternative ‘‘Less Than’’ (<) 121<br/><br/>3.3.3 Two-Tail Tests with Alternative ‘‘Not Equal To’’ (≠) 122<br/><br/>3.4 Examples of Hypothesis Tests 123<br/><br/>3.5 The p-Value 126<br/><br/>3.6 Linear Combinations of Parameters 129<br/><br/>3.6.1 Testing a Linear Combination of Parameters 131<br/><br/>3.7 Exercises 133<br/><br/>3.7.1 Problems 133<br/><br/>3.7.2 Computer Exercises 139<br/><br/>Appendix 3A Derivation of the t-Distribution 144<br/><br/>Appendix 3B Distribution of the t-Statistic under H1 145<br/><br/>Appendix 3C Monte Carlo Simulation 147<br/><br/>3C.1 Sampling Properties of Interval Estimators 148<br/><br/>3C.2 Sampling Properties of Hypothesis Tests 149<br/><br/>3C.3 Choosing the Number of Monte Carlo Samples 149<br/><br/>3C.4 Random-x Monte Carlo Results 150<br/><br/>4 Prediction, Goodness-of-Fit, and Modeling Issues 152<br/><br/>4.1 Least Squares Prediction 153<br/><br/>4.2 Measuring Goodness-of-Fit 156<br/><br/>4.2.1 Correlation Analysis 158<br/><br/>4.2.2 Correlation Analysis and R2 158<br/><br/>4.3 Modeling Issues 160<br/><br/>4.3.1 The Effects of Scaling the Data 160<br/><br/>4.3.2 Choosing a Functional Form 161<br/><br/>4.3.3 A Linear-Log Food Expenditure Model 163<br/><br/>4.3.4 Using Diagnostic Residual Plots 165<br/><br/>4.3.5 Are the Regression Errors Normally Distributed? 167<br/><br/>4.3.6 Identifying Influential Observations 169<br/><br/>4.4 Polynomial Models 171<br/><br/>4.4.1 Quadratic and Cubic Equations 171<br/><br/>4.5 Log-Linear Models 173<br/><br/>4.5.1 Prediction in the Log-Linear Model 175<br/><br/>4.5.2 A Generalized R2 Measure 176<br/><br/>4.5.3 Prediction Intervals in the Log-Linear Model 177<br/><br/>4.6 Log-Log Models 177<br/><br/>4.7 Exercises 179<br/><br/>4.7.1 Problems 179<br/><br/>4.7.2 Computer Exercises 185<br/><br/>Appendix 4A Development of a Prediction Interval 192<br/><br/>Appendix 4B The Sum of Squares Decomposition 193<br/><br/>Appendix 4C Mean Squared Error: Estimation and Prediction 193<br/><br/>5 The Multiple Regression Model 196<br/><br/>5.1 Introduction 197<br/><br/>5.1.1 The Economic Model 197<br/><br/>5.1.2 The Econometric Model 198<br/><br/>5.1.3 The General Model 202<br/><br/>5.1.4 Assumptions of the Multiple Regression Model 203<br/><br/>5.2 Estimating the Parameters of the Multiple Regression Model 205<br/><br/>5.2.1 Least Squares Estimation Procedure 205<br/><br/>5.2.2 Estimating the Error Variance σ2 207<br/><br/>5.2.3 Measuring Goodness-of-Fit 208<br/><br/>5.2.4 Frisch–Waugh–Lovell (FWL) Theorem 209<br/><br/>5.3 Finite Sample Properties of the Least Squares Estimator 211<br/><br/>5.3.1 The Variances and Covariances of the Least Squares Estimators 212<br/><br/>5.3.2 The Distribution of the Least Squares Estimators 214<br/><br/>5.4 Interval Estimation 216<br/><br/>5.4.1 Interval Estimation for a Single Coefficient 216<br/><br/>5.4.2 Interval Estimation for a Linear Combination of Coefficients 217<br/><br/>5.5 Hypothesis Testing 218<br/><br/>5.5.1 Testing the Significance of a Single Coefficient 219<br/><br/>5.5.2 One-Tail Hypothesis Testing for a Single Coefficient 220<br/><br/>5.5.3 Hypothesis Testing for a Linear Combination of Coefficients 221<br/><br/>5.6 Nonlinear Relationships 222<br/><br/>5.7 Large Sample Properties of the Least Squares Estimator 227<br/><br/>5.7.1 Consistency 227<br/><br/>5.7.2 Asymptotic Normality 229<br/><br/>5.7.3 Relaxing Assumptions 230<br/><br/>5.7.4 Inference for a Nonlinear Function of Coefficients 232<br/><br/>5.8 Exercises 234<br/><br/>5.8.1 Problems 234<br/><br/>5.8.2 Computer Exercises 240<br/><br/>Appendix 5A Derivation of Least Squares Estimators 247<br/><br/>Appendix 5B The Delta Method 248<br/><br/>5B.1 Nonlinear Function of a Single Parameter 248<br/><br/>5B.2 Nonlinear Function of Two Parameters 249<br/><br/>Appendix 5C Monte Carlo Simulation 250<br/><br/>5C.1 Least Squares Estimation with Chi-Square Errors 250<br/><br/>5C.2 Monte Carlo Simulation of the Delta Method 252<br/><br/>Appendix 5D Bootstrapping 254<br/><br/>5D.1 Resampling 255<br/><br/>5D.2 Bootstrap Bias Estimate 256<br/><br/>5D.3 Bootstrap Standard Error 256<br/><br/>5D.4 Bootstrap Percentile Interval Estimate 257<br/><br/>5D.5 Asymptotic Refinement 258<br/><br/>6 Further Inference in the Multiple Regression Model 260<br/><br/>6.1 Testing Joint Hypotheses: The F-test 261<br/><br/>6.1.1 Testing the Significance of the Model 264<br/><br/>6.1.2 The Relationship Between t- and F-Tests 265<br/><br/>6.1.3 More General F-Tests 267<br/><br/>6.1.4 Using Computer Software 268<br/><br/>6.1.5 Large Sample Tests 269<br/><br/>6.2 The Use of Nonsample Information 271<br/><br/>6.3 Model Specification 273<br/><br/>6.3.1 Causality versus Prediction 273<br/><br/>6.3.2 Omitted Variables 275<br/><br/>6.3.3 Irrelevant Variables 277<br/><br/>6.3.4 Control Variables 278<br/><br/>6.3.5 Choosing a Model 280<br/><br/>6.3.6 RESET 281<br/><br/>6.4 Prediction 282<br/><br/>6.4.1 Predictive Model Selection Criteria 285<br/><br/>6.5 Poor Data, Collinearity, and Insignificance 288<br/><br/>6.5.1 The Consequences of Collinearity 289<br/><br/>6.5.2 Identifying and Mitigating Collinearity 290<br/><br/>6.5.3 Investigating Influential Observations 293<br/><br/>6.6 Nonlinear Least Squares 294<br/><br/>6.7 Exercises 297<br/><br/>6.7.1 Problems 297<br/><br/>6.7.2 Computer Exercises 303<br/><br/>Appendix 6A The Statistical Power of F-Tests 311<br/><br/>Appendix 6B Further Results from the FWL Theorem 315<br/><br/>7 Using Indicator Variables 317<br/><br/>7.1 Indicator Variables 318<br/><br/>7.1.1 Intercept Indicator Variables 318<br/><br/>7.1.2 Slope-Indicator Variables 320<br/><br/>7.2 Applying Indicator Variables 323<br/><br/>7.2.1 Interactions Between Qualitative Factors 323<br/><br/>7.2.2 Qualitative Factors with Several Categories 324<br/><br/>7.2.3 Testing the Equivalence of Two Regressions 326<br/><br/>7.2.4 Controlling for Time 328<br/><br/>7.3 Log-Linear Models 329<br/><br/>7.3.1 A Rough Calculation 330<br/><br/>7.3.2 An Exact Calculation 330<br/><br/>7.4 The Linear Probability Model 331<br/><br/>7.5 Treatment Effects 332<br/><br/>7.5.1 The Difference Estimator 334<br/><br/>7.5.2 Analysis of the Difference Estimator 334<br/><br/>7.5.3 The Differences-in-Differences Estimator 338<br/><br/>7.6 Treatment Effects and Causal Modeling 342<br/><br/>7.6.1 The Nature of Causal Effects 342<br/><br/>7.6.2 Treatment Effect Models 343<br/><br/>7.6.3 Decomposing the Treatment Effect 344<br/><br/>7.6.4 Introducing Control Variables 345<br/><br/>7.6.5 The Overlap Assumption 347<br/><br/>7.6.6 Regression Discontinuity Designs 347<br/><br/>7.7 Exercises 351<br/><br/>7.7.1 Problems 351<br/><br/>7.7.2 Computer Exercises 358<br/><br/>Appendix 7A Details of Log-Linear Model Interpretation 366<br/><br/>Appendix 7B Derivation of the Differences-in-Differences Estimator 366<br/><br/>Appendix 7C The Overlap Assumption: Details 367<br/><br/>8 Heteroskedasticity 368<br/><br/>8.1 The Nature of Heteroskedasticity 369<br/><br/>8.2 Heteroskedasticity in the Multiple Regression Model 370<br/><br/>8.2.1 The Heteroskedastic Regression Model 371<br/><br/>8.2.2 Heteroskedasticity Consequences for the OLS Estimator 373<br/><br/>8.3 Heteroskedasticity Robust Variance Estimator 374<br/><br/>8.4 Generalized Least Squares: Known Form of Variance 375<br/><br/>8.4.1 Transforming the Model: Proportional Heteroskedasticity 375<br/><br/>8.4.2 Weighted Least Squares: Proportional Heteroskedasticity 377<br/><br/>8.5 Generalized Least Squares: Unknown Form of Variance 379<br/><br/>8.5.1 Estimating the Multiplicative Model 381<br/><br/>8.6 Detecting Heteroskedasticity 383<br/><br/>8.6.1 Residual Plots 384<br/><br/>8.6.2 The Goldfeld–Quandt Test 384<br/><br/>8.6.3 A General Test for Conditional Heteroskedasticity 385<br/><br/>8.6.4 The White Test 387<br/><br/>8.6.5 Model Specification and Heteroskedasticity 388<br/><br/>8.7 Heteroskedasticity in the Linear Probability Model 390<br/><br/>8.8 Exercises 391<br/><br/>8.8.1 Problems 391<br/><br/>8.8.2 Computer Exercises 401<br/><br/>Appendix 8A Properties of the Least Squares Estimator 407<br/><br/>Appendix 8B Lagrange Multiplier Tests for Heteroskedasticity 408<br/><br/>Appendix 8C Properties of the Least Squares Residuals 410<br/><br/>8C.1 Details of Multiplicative Heteroskedasticity Model 411<br/><br/>Appendix 8D Alternative Robust Sandwich Estimators 411<br/><br/>Appendix 8E Monte Carlo Evidence: OLS, GLS, and FGLS 414<br/><br/>9 Regression with Time-Series Data: Stationary Variables 417<br/><br/>9.1 Introduction 418<br/><br/>9.1.1 Modeling Dynamic Relationships 420<br/><br/>9.1.2 Autocorrelations 424<br/><br/>9.2 Stationarity and Weak Dependence 427<br/><br/>9.3 Forecasting 430<br/><br/>9.3.1 Forecast Intervals and Standard Errors 433<br/><br/>9.3.2 Assumptions for Forecasting 435<br/><br/>9.3.3 Selecting Lag Lengths 436<br/><br/>9.3.4 Testing for Granger Causality 437<br/><br/>9.4 Testing for Serially Correlated Errors 438<br/><br/>9.4.1 Checking the Correlogram of the Least Squares Residuals 439<br/><br/>9.4.2 Lagrange Multiplier Test 440<br/><br/>9.4.3 Durbin–Watson Test 443<br/><br/>9.5 Time-Series Regressions for Policy Analysis 443<br/><br/>9.5.1 Finite Distributed Lags 445<br/><br/>9.5.2 HAC Standard Errors 448<br/><br/>9.5.3 Estimation with AR(1) Errors 452<br/><br/>9.5.4 Infinite Distributed Lags 456<br/><br/>9.6 Exercises 463<br/><br/>9.6.1 Problems 463<br/><br/>9.6.2 Computer Exercises 468<br/><br/>Appendix 9A The Durbin–Watson Test 476<br/><br/>9A.1 The Durbin–Watson Bounds Test 478<br/><br/>Appendix 9B Properties of an AR(1) Error 479<br/><br/>10 Endogenous Regressors and Moment-Based Estimation 481<br/><br/>10.1 Least Squares Estimation with Endogenous Regressors 482<br/><br/>10.1.1 Large Sample Properties of the OLS Estimator 483<br/><br/>10.1.2 Why Least Squares Estimation Fails 484<br/><br/>10.1.3 Proving the Inconsistency of OLS 486<br/><br/>10.2 Cases inWhich x and e are Contemporaneously Correlated 487<br/><br/>10.2.1 Measurement Error 487<br/><br/>10.2.2 Simultaneous Equations Bias 488<br/><br/>10.2.3 Lagged-Dependent Variable Models with Serial Correlation 489<br/><br/>10.2.4 Omitted Variables 489<br/><br/>10.3 Estimators Based on the Method of Moments 490<br/><br/>10.3.1 Method of Moments Estimation of a Population Mean and Variance 490<br/><br/>10.3.2 Method of Moments Estimation in the Simple Regression Model 491<br/><br/>10.3.3 Instrumental Variables Estimation in the Simple Regression Model 492<br/><br/>10.3.4 The Importance of Using Strong Instruments 493<br/><br/>10.3.5 Proving the Consistency of the IV Estimator 494<br/><br/>10.3.6 IV Estimation Using Two-Stage Least Squares (2SLS) 495<br/><br/>10.3.7 Using Surplus Moment Conditions 496<br/><br/>10.3.8 Instrumental Variables Estimation in the Multiple Regression Model 498<br/><br/>10.3.9 Assessing Instrument Strength Using the First-Stage Model 500<br/><br/>10.3.10 Instrumental Variables Estimation in a General Model 502<br/><br/>10.3.11 Additional Issues When Using IV Estimation 504<br/><br/>10.4 Specification Tests 505<br/><br/>10.4.1 The Hausman Test for Endogeneity 505<br/><br/>10.4.2 The Logic of the Hausman Test 507<br/><br/>10.4.3 Testing Instrument Validity 508<br/><br/>10.5 Exercises 510<br/><br/>10.5.1 Problems 510<br/><br/>10.5.2 Computer Exercises 516<br/><br/>Appendix 10A Testing for Weak Instruments 520<br/><br/>10A.1 A Test for Weak Identification 521<br/><br/>10A.2 Testing for Weak Identification: Conclusions 525<br/><br/>Appendix 10B Monte Carlo Simulation 525<br/><br/>10B.1 Illustrations Using Simulated Data 526<br/><br/>10B.2 The Sampling Properties of IV/2SLS 528<br/><br/>11 Simultaneous Equations Models 531<br/><br/>11.1 A Supply and Demand Model 532<br/><br/>11.2 The Reduced-Form Equations 534<br/><br/>11.3 The Failure of Least Squares Estimation 535<br/><br/>11.3.1 Proving the Failure of OLS 535<br/><br/>11.4 The Identification Problem 536<br/><br/>11.5 Two-Stage Least Squares Estimation 538<br/><br/>11.5.1 The General Two-Stage Least Squares Estimation Procedure 539<br/><br/>11.5.2 The Properties of the Two-Stage Least Squares Estimator 540<br/><br/>11.6 Exercises 545<br/><br/>11.6.1 Problems 545<br/><br/>11.6.2 Computer Exercises 551<br/><br/>Appendix 11A 2SLS Alternatives 557<br/><br/>11A.1 The k-Class of Estimators 557<br/><br/>11A.2 The LIML Estimator 558<br/><br/>11A.3 Monte Carlo Simulation Results 562<br/><br/>12 Regression with Time-Series Data: Nonstationary Variables 563<br/><br/>12.1 Stationary and Nonstationary Variables 564<br/><br/>12.1.1 Trend Stationary Variables 567<br/><br/>12.1.2 The First-Order Autoregressive Model 570<br/><br/>12.1.3 Random Walk Models 572<br/><br/>12.2 Consequences of Stochastic Trends 574<br/><br/>12.3 Unit Root Tests for Stationarity 576<br/><br/>12.3.1 Unit Roots 576<br/><br/>12.3.2 Dickey–Fuller Tests 577<br/><br/>12.3.3 Dickey–Fuller Test with Intercept and No Trend 577<br/><br/>12.3.4 Dickey–Fuller Test with Intercept and Trend 579<br/><br/>12.3.5 Dickey–Fuller Test with No Intercept and No Trend 580<br/><br/>12.3.6 Order of Integration 581<br/><br/>12.3.7 Other Unit Root Tests 582<br/><br/>12.4 Cointegration 582<br/><br/>12.4.1 The Error Correction Model 584<br/><br/>12.5 Regression When There Is No Cointegration 585<br/><br/>12.6 Summary 587<br/><br/>12.7 Exercises 588<br/><br/>12.7.1 Problems 588<br/><br/>12.7.2 Computer Exercises 592<br/><br/>13 Vector Error Correction and Vector Autoregressive Models 597<br/><br/>13.1 VEC and VAR Models 598<br/><br/>13.2 Estimating a Vector Error Correction Model 600<br/><br/>13.3 Estimating a VAR Model 601<br/><br/>13.4 Impulse Responses and Variance Decompositions 603<br/><br/>13.4.1 Impulse Response Functions 603<br/><br/>13.4.2 Forecast Error Variance Decompositions 605<br/><br/>13.5 Exercises 607<br/><br/>13.5.1 Problems 607<br/><br/>13.5.2 Computer Exercises 608<br/><br/>Appendix 13A The Identification Problem 612<br/><br/>14 Time-Varying Volatility and ARCH Models 614<br/><br/>14.1 The ARCH Model 615<br/><br/>14.2 Time-Varying Volatility 616<br/><br/>14.3 Testing, Estimating, and Forecasting 620<br/><br/>14.4 Extensions 622<br/><br/>14.4.1 The GARCH Model—Generalized ARCH 622<br/><br/>14.4.2 Allowing for an Asymmetric Effect 623<br/><br/>14.4.3 GARCH-in-Mean and Time-Varying Risk Premium 624<br/><br/>14.4.4 Other Developments 625<br/><br/>14.5 Exercises 626<br/><br/>14.5.1 Problems 626<br/><br/>14.5.2 Computer Exercises 627<br/><br/>15 Panel Data Models 634<br/><br/>15.1 The Panel Data Regression Function 636<br/><br/>15.1.1 Further Discussion of Unobserved Heterogeneity 638<br/><br/>15.1.2 The Panel Data Regression Exogeneity Assumption 639<br/><br/>15.1.3 Using OLS to Estimate the Panel Data Regression 639<br/><br/>15.2 The Fixed Effects Estimator 640<br/><br/>15.2.1 The Difference Estimator: T = 2 640<br/><br/>15.2.2 The Within Estimator: T = 2 642<br/><br/>15.2.3 The Within Estimator: T > 2 643<br/><br/>15.2.4 The Least Squares Dummy Variable Model 644<br/><br/>15.3 Panel Data Regression Error Assumptions 646<br/><br/>15.3.1 OLS Estimation with Cluster-Robust Standard Errors 648<br/><br/>15.3.2 Fixed Effects Estimation with Cluster-Robust Standard Errors 650<br/><br/>15.4 The Random Effects Estimator 651<br/><br/>15.4.1 Testing for Random Effects 653<br/><br/>15.4.2 A Hausman Test for Endogeneity in the Random Effects Model 654<br/><br/>15.4.3 A Regression-Based Hausman Test 656<br/><br/>15.4.4 The Hausman–Taylor Estimator 658<br/><br/>15.4.5 Summarizing Panel Data Assumptions 660<br/><br/>15.4.6 Summarizing and Extending Panel Data Model Estimation 661<br/><br/>15.5 Exercises 663<br/><br/>15.5.1 Problems 663<br/><br/>15.5.2 Computer Exercises 670<br/><br/>Appendix 15A Cluster-Robust Standard Errors: Some Details 677<br/><br/>Appendix 15B Estimation of Error Components 679<br/><br/>16 Qualitative and Limited Dependent Variable Models 681<br/><br/>16.1 Introducing Models with Binary Dependent Variables 682<br/><br/>16.1.1 The Linear Probability Model 683<br/><br/>16.2 Modeling Binary Choices 685<br/><br/>16.2.1 The Probit Model for Binary Choice 686<br/><br/>16.2.2 Interpreting the Probit Model 687<br/><br/>16.2.3 Maximum Likelihood Estimation of the Probit Model 690<br/><br/>16.2.4 The Logit Model for Binary Choices 693<br/><br/>16.2.5 Wald Hypothesis Tests 695<br/><br/>16.2.6 Likelihood Ratio Hypothesis Tests 696<br/><br/>16.2.7 Robust Inference in Probit and Logit Models 698<br/><br/>16.2.8 Binary Choice Models with a Continuous Endogenous Variable 698<br/><br/>16.2.9 Binary Choice Models with a Binary Endogenous Variable 699<br/><br/>16.2.10 Binary Endogenous Explanatory Variables 700<br/><br/>16.2.11 Binary Choice Models and Panel Data 701<br/><br/>16.3 Multinomial Logit 702<br/><br/>16.3.1 Multinomial Logit Choice Probabilities 703<br/><br/>16.3.2 Maximum Likelihood Estimation 703<br/><br/>16.3.3 Multinomial Logit Postestimation Analysis 704<br/><br/>16.4 Conditional Logit 707<br/><br/>16.4.1 Conditional Logit Choice Probabilities 707<br/><br/>16.4.2 Conditional Logit Postestimation Analysis 708<br/><br/>16.5 Ordered Choice Models 709<br/><br/>16.5.1 Ordinal Probit Choice Probabilities 710<br/><br/>16.5.2 Ordered Probit Estimation and Interpretation 711<br/><br/>16.6 Models for Count Data 713<br/><br/>16.6.1 Maximum Likelihood Estimation of the Poisson Regression Model 713<br/><br/>16.6.2 Interpreting the Poisson Regression Model 714<br/><br/>16.7 Limited Dependent Variables 717<br/><br/>16.7.1 Maximum Likelihood Estimation of the Simple Linear Regression Model 717<br/><br/>16.7.2 Truncated Regression 718<br/><br/>16.7.3 Censored Samples and Regression 718<br/><br/>16.7.4 Tobit Model Interpretation 720<br/><br/>16.7.5 Sample Selection 723<br/><br/>16.8 Exercises 725<br/><br/>16.8.1 Problems 725<br/><br/>16.8.2 Computer Exercises 733<br/><br/>Appendix 16A Probit Marginal Effects: Details 739<br/><br/>16A.1 Standard Error of Marginal Effect at a Given Point 739<br/><br/>16A.2 Standard Error of Average Marginal Effect 740<br/><br/>Appendix 16B Random Utility Models 741<br/><br/>16B.1 Binary Choice Model 741<br/><br/>16B.2 Probit or Logit? 742<br/><br/>Appendix 16C Using Latent Variables 743<br/><br/>16C.1 Tobit (Tobit Type I) 743<br/><br/>16C.2 Heckit (Tobit Type II) 744<br/><br/>Appendix 16D A Tobit Monte Carlo Experiment 745<br/><br/>A Mathematical Tools 748<br/><br/>A.1 Some Basics 749<br/><br/>A.1.1 Numbers 749<br/><br/>A.1.2 Exponents 749<br/><br/>A.1.3 Scientific Notation 749<br/><br/>A.1.4 Logarithms and the Number e 750<br/><br/>A.1.5 Decimals and Percentages 751<br/><br/>A.1.6 Logarithms and Percentages 751<br/><br/>A.2 Linear Relationships 752<br/><br/>A.2.1 Slopes and Derivatives 753<br/><br/>A.2.2 Elasticity 753<br/><br/>A.3 Nonlinear Relationships 753<br/><br/>A.3.1 Rules for Derivatives 754<br/><br/>A.3.2 Elasticity of a Nonlinear Relationship 757<br/><br/>A.3.3 Second Derivatives 757<br/><br/>A.3.4 Maxima and Minima 758<br/><br/>A.3.5 Partial Derivatives 759<br/><br/>A.3.6 Maxima and Minima of Bivariate Functions 760<br/><br/>A.4 Integrals 762<br/><br/>A.4.1 Computing the Area Under a Curve 762<br/><br/>A.5 Exercises 764<br/><br/>B Probability Concepts 768<br/><br/>B.1 Discrete Random Variables 769<br/><br/>B.1.1 Expected Value of a Discrete Random Variable 769<br/><br/>B.1.2 Variance of a Discrete Random Variable 770<br/><br/>B.1.3 Joint, Marginal, and Conditional Distributions 771<br/><br/>B.1.4 Expectations Involving Several Random Variables 772<br/><br/>B.1.5 Covariance and Correlation 773<br/><br/>B.1.6 Conditional Expectations 774<br/><br/>B.1.7 Iterated Expectations 774<br/><br/>B.1.8 Variance Decomposition 774<br/><br/>B.1.9 Covariance Decomposition 777<br/><br/>B.2 Working with Continuous Random Variables 778<br/><br/>B.2.1 Probability Calculations 779<br/><br/>B.2.2 Properties of Continuous Random Variables 780<br/><br/>B.2.3 Joint, Marginal, and Conditional Probability Distributions 781<br/><br/>B.2.4 Using Iterated Expectations with Continuous Random Variables 785<br/><br/>B.2.5 Distributions of Functions of Random Variables 787<br/><br/>B.2.6 Truncated Random Variables 789<br/><br/>B.3 Some Important Probability Distributions 789<br/><br/>B.3.1 The Bernoulli Distribution 790<br/><br/>B.3.2 The Binomial Distribution 790<br/><br/>B.3.3 The Poisson Distribution 791<br/><br/>B.3.4 The Uniform Distribution 792<br/><br/>B.3.5 The Normal Distribution 793<br/><br/>B.3.6 The Chi-Square Distribution 794<br/><br/>B.3.7 The t-Distribution 796<br/><br/>B.3.8 The F-Distribution 797<br/><br/>B.3.9 The Log-Normal Distribution 799<br/><br/>B.4 Random Numbers 800<br/><br/>B.4.1 Uniform Random Numbers 805<br/><br/>B.5 Exercises 806<br/><br/>C Review of Statistical Inference 812<br/><br/>C.1 A Sample of Data 813<br/><br/>C.2 An Econometric Model 814<br/><br/>C.3 Estimating the Mean of a Population 815<br/><br/>C.3.1 The Expected Value of Y 816<br/><br/>C.3.2 The Variance of Y 817<br/><br/>C.3.3 The Sampling Distribution of Y 817<br/><br/>C.3.4 The Central Limit Theorem 818<br/><br/>C.3.5 Best Linear Unbiased Estimation 820<br/><br/>C.4 Estimating the Population Variance and Other Moments 820<br/><br/>C.4.1 Estimating the Population Variance 821<br/><br/>C.4.2 Estimating Higher Moments 821<br/><br/>C.5 Interval Estimation 822<br/><br/>C.5.1 Interval Estimation: σ2 Known 822<br/><br/>C.5.2 Interval Estimation: σ2 Unknown 825<br/><br/>C.6 Hypothesis Tests About a Population Mean 826<br/><br/>C.6.1 Components of Hypothesis Tests 826<br/><br/>C.6.2 One-Tail Tests with Alternative ‘‘Greater Than’’ (>) 828<br/><br/>C.6.3 One-Tail Tests with Alternative ‘‘Less Than’’ (<) 829<br/><br/>C.6.4 Two-Tail Tests with Alternative ‘‘Not Equal To’’ (≠) 829<br/><br/>C.6.5 The p-Value 831<br/><br/>C.6.6 A Comment on Stating Null and Alternative Hypotheses 832<br/><br/>C.6.7 Type I and Type II Errors 833<br/><br/>C.6.8 A Relationship Between Hypothesis Testing and Confidence Intervals 833<br/><br/>C.7 Some Other Useful Tests 834<br/><br/>C.7.1 Testing the Population Variance 834<br/><br/>C.7.2 Testing the Equality of Two Population Means 834<br/><br/>C.7.3 Testing the Ratio of Two Population Variances 835<br/><br/>C.7.4 Testing the Normality of a Population 836<br/><br/>C.8 Introduction to Maximum Likelihood Estimation 837<br/><br/>C.8.1 Inference with Maximum Likelihood Estimators 840<br/><br/>C.8.2 The Variance of the Maximum Likelihood Estimator 841<br/><br/>C.8.3 The Distribution of the Sample Proportion 842<br/><br/>C.8.4 Asymptotic Test Procedures 843<br/><br/>C.9 Algebraic Supplements 848<br/><br/>C.9.1 Derivation of Least Squares Estimator 848<br/><br/>C.9.2 Best Linear Unbiased Estimation 849<br/><br/>C.10 Kernel Density Estimator 851<br/><br/>C.11 Exercises 854<br/><br/>C.11.1 Problems 854<br/><br/>C.11.2 Computer Exercises 857<br/><br/>D Statistical Tables 862<br/><br/>TableD.1 Cumulative Probabilities for the Standard Normal Distribution ��(z) = P(Z ≤ z) 862<br/><br/>TableD.2 Percentiles of the t-distribution 863<br/><br/>TableD.3 Percentiles of the Chi-square Distribution 864<br/><br/>TableD.4 95th Percentile for the F-distribution 865<br/><br/>TableD.5 99th Percentile for the F-distribution 866<br/><br/>TableD.6 Standard Normal pdf Values ��(z) 867<br/><br/>Index 869 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Simple linear regression model |
| 9 (RLIN) | 255748 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Multiple regression model |
| 9 (RLIN) | 255749 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Using indicator variables |
| 9 (RLIN) | 255750 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Griffiths, William E. |
| 9 (RLIN) | 255751 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Lim, Guay C. |
| 9 (RLIN) | 255752 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sharma, Chandan. |
| 9 (RLIN) | 255753 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Call number prefix | 330.015195 HILR |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | MBA | St Aloysius Institute of Management & Information Technology | St Aloysius Institute of Management & Information Technology | Economics | 02/12/2026 | SK Publishers & Distributors | 1079.00 | Bill no:SKP4043;Bill dt:2026/2/2 | 330.015195 HILR | MBA15279 | 05/23/2026 | 809.25 | 02/19/2026 | Book |
