Introductory Econometrics/ (Registro n. 2230)
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| 007 - Campo Fixo - Descrição Física | |
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| Campo fixo de controle local | 190617s2003 bl gr 000 0 por u |
| 020 ## - ISBN | |
| ISBN | 324113641 |
| 040 ## - Fonte da Catalogação | |
| Fonte de catalogação | BR-BrCADE |
| 090 ## - Número de Chamada | |
| Localização na estante | 330.0151 W913i |
| Cutter | W913i |
| 100 10 - Autor | |
| Autor | WOOLDRIDGE, Jeffrey M. |
| 245 10 - Titulo Principal | |
| Título principal | Introductory Econometrics/ |
| 260 ## - Editora | |
| Cidade | Estados Unidos: |
| Editora | Thomson, |
| Data | 2003. |
| 300 ## - Descrição Física | |
| Número de páginas | 863 p. |
| 505 ## - Conteúdo | |
| Conteúdo | Contents<br/>Chapter 1 The Nature of Econometrics and Economic Data <br/>1 What Is Econometrics?<br/>1.2 Steps in Empirical Economic Analysis<br/>1.3 The Structure of Economic Data <br/>Cross-Sectionai Data <br/>Time Series Data <br/>Pooied Cross Sections <br/>Panei or Longitudinal Data <br/>A Comnent on Data Structures <br/>1.4 Causality and the Notion of Ceteris Paribus in Econometric <br/>Analysis <br/>Summary<br/>Key Terms<br/><br/>PART 1 <br/>REGRESSION ANALYSIS WITR CROSS-SECTIONAL DATA <br/> <br/>Chapter 2 The Simple Regression Model <br/>2.1 Definition of the Simple Regression Model <br/>2.2 Deriving the Ordinary Least Squares Estimates<br/>A Note on Terminoiogy <br/>2.3 Mechanics of OLS<br/>Fitted Vaiues and Residuais <br/>Algebraic Properties of OLS Statistics <br/>Goodness-of-Fit <br/>2.4 Units of Measurement and Functional Form <br/>The Effects of c/uanging Units ofMeasurement on OLS Statistics <br/>Incorporating Nonlinearities in Simpie Regression <br/>The Meaning of Linear" Regression <br/>2.5 Expected Values and Variances of the OLS Estimators<br/>Unbiasedness of OLS <br/>Variances of the OLS Estimators <br/>Estimating the Error Variance <br/>2.6 Regression Through the Origin <br/>Summary <br/>Key Terms <br/>Problems <br/>Computer Exercises <br/>Appendix 2A <br/><br/>Chapter 3 Multiple Regression Analysis: Estimation <br/>3.1 Motivation for Multiple Regression <br/>The Model with Two Independent Variables <br/>The Model with k Independent Variables <br/>3.2 Mechanics and Interpretation of Ordinary Least Squares<br/>Obtaining the OLS Estimates <br/>Interpreting the OLS Regression Equation <br/>On the Meaning of "Holding Other Factors Fixed" in Multiple <br/>Regression <br/>Changing More than One Independent Variable Simultaneously <br/>OLS Fitted Values and Residuais <br/>A "Partialling Our" Interpretation of Multiple Regression <br/>Comparison of Simple and Multiple Regression Estimates <br/>Goodness-of-Fir <br/>Regression Through the Origin <br/>3.3 The Expected Value of the OLS Estimators <br/>Including Irrelevant Variables in a Regression Model <br/>Omitted Variable Bias: The Simple Case <br/>Omitted Variable Bias: More General Cases <br/>3.4 The Variance of the OLS Estimators <br/>The Components of the OLS Variances: Multicollinearily <br/>Variances in Misspeczfied Modeis <br/>Estimating 0.2: Standard Errors of the OLS Estimators <br/>3.5 Efficiency of OLS: The Gauss-Markov Theorem<br/>Summary <br/>Key Terms <br/>Problems <br/>Computer Exercises <br/>Appendix 3 A <br/><br/>Chapter 4 Multiple Regression Analysis: Inference <br/>4.1 Sampling Distributions of the OLS Estimators <br/>4.2 Testing Hypotheses About a Single Population Parameter: <br/>The t Test<br/>Testing Against One-Sided Alternatives <br/>Two-SidedAlternatives <br/>Testing Other Hypotheses About B <br/>Computing p-Values for t Tests <br/>A Reminder on the Language of Classical Hypothesis Testing<br/>Econornic, or Practical, versus Statistical Significance <br/>4.3 Confidence Intervals <br/>4.4 Testing Hypotheses About a Single Linear Combination of the Parameters<br/>4.5 Testing Multiple Linear Restrictions: The FTest <br/>Testing Exclusion Restrictions <br/>Relationship Between F and t Sratistics <br/>The R-Squared Form of the F Statistic <br/>Computing p-Valuesjbr F Tests <br/>The F Statistic for Overali Significance of a Regression <br/>Testing General Linear Restrictions <br/>4.6 Reporting Regression Results <br/>Summary <br/>KeyTerms <br/>Problems <br/>Computer Exercises <br/><br/>Chapter 5 Multiple Regression Analysis: OLS Asymptotics <br/>5.1 Consistency <br/>Deriving the Jnconsistencv ia OLS <br/>5.2 Asymptotic Normality and Large Sample Inference <br/>Other Large Sample Tests: The Lagrange Multiplier<br/>Statistic <br/>5.3 Asymptotic Efficiency of OLS <br/>Summary <br/>KeyTerms <br/>Problems <br/>Computer Exercises <br/>Appendix 5A <br/><br/>Chapter 6 Multiple Regression Analysis: Further Issues <br/>6.1 Effects of Data Scaling on OLS Statistics <br/>Beta Coefficients <br/>6.2 More on Functional Form <br/>More on Using Logarithmic Functional Forrns <br/>Modeis with Quadratics <br/>Models with Interaction Terms<br/>6.3 More on Goodness-of-Fit and Selection of Regressors <br/>Adjusted R-Squared <br/>Using Adjusted R-Squared to Choose Between IVonnested <br/>Modeis <br/>Controlling for Too Manv Factors in Regression Analvsis <br/>Adding Regressors to Reduce the Error Variance <br/>6.4 Prediction and Residual Analysis <br/>Confidence Inrervais for Predictions <br/>Residual Analvsis <br/>Predicting y when log(y) Is the Dependem Variabie <br/>Summary <br/>KeyTerms <br/>Problems <br/>Computer Exercises <br/><br/>Chapter 7 Multiple Regression Analysis with Qualitative Information:<br/>Binary (or Dummy) Variables <br/>7.1 Describing Qualitative Enformation <br/>7.2 A Single Dummy Independent Variable <br/>Interpreting Coefficients on Dummy Explanatory Variables <br/>when the Dependent Variable Is log(y) <br/>7.3 Using Dummy Variables for Multiple Categories<br/>lncorporaring Ordinal Information hy Using Dummy Variables <br/>7.4 Interactions Involving Dummy Variables <br/>Interacrions Among Dummy Variables <br/>Aliowing for Dfferent Siopes<br/>Testing for Differences in Regression Functions Across Groups <br/>7.5 A Binary Dependent Variable: The Linear Probability Model<br/>7.6 More on Policy Analysis and Program Evaluation<br/>Summary <br/>Key Terms <br/>Problems <br/>Computer Exercises <br/><br/>Chapter 8 Heteroskedasticity <br/>8.1 Consequences of Heteroskedasticity for OLS <br/>8.2 Heteroskedasticity-Robust Inference After OLS Estimation <br/>Computing Heteroskedasticity-Robust LM Tests <br/>8.3 Testing for Heteroskedasticity<br/>The White Test for Heteroskedasticity<br/>8.4 Weighted Least Squares Estimation <br/>The Heteroskedasticitv Is Known up to a Multiplicative Constant <br/>The J-Jeteroskedasticity Function Must Be Estirnated: Feasible GLS <br/>8.5 The Linear Probability Model Revisited <br/>Summary <br/>Key Terras <br/>Problems<br/>Computer Exercises <br/><br/>Chapter 9 More on Specification and Data Problems <br/>9.1 Functional Form Misspecification <br/>RESET as a General Test for Functional Form <br/>Misspecification <br/>Tests Against Nonnested Alrernatives <br/>9.2 Using Proxy Variables for Unobserved Explanatory Variables<br/>Using Lagged Dependent Variables as Pro.rv Variables <br/>9.3 Properties of OLS Under Measurement Error <br/>Measurement Error in the Dependent Variable <br/>Measurement Error in an Explanatorv Variable <br/>9.4 Missing Data, Nonrandom Samples. and Outlying Observations <br/>Missing Data <br/>Nonrandom Samples <br/>Outliers and Influential Observations <br/>Summary <br/>KeyTerms <br/>Problems <br/>Computer Exercises <br/><br/>PART 2<br/>REGRESSION ANALYSIS WITH TIME SERIES DATA <br/><br/>Chapter 10 Basic Regression Analysis with Time Series Data <br/>10.1 The Nature of Time Series Data <br/>10.2 Examples of Time Series Regression Models <br/>Static Modeis <br/>Emite Disrributed Lag Modeis <br/>A C'onvention abour lhe Time Index <br/>10.3 Finite Sample Properties of OLS Under Classical Assumptions<br/>Unbiasedness of OLS <br/>The Variances of lhe OLS Estimators and lhe Gauss-Markov Theorem <br/>inference under lhe Classical Linear Model Assumptions <br/>10.4 Functional Form, Dummy Variables, and Index Numbers<br/>10.5 Trends and Seasonality <br/>Characterizing Trending Time Series <br/>Using Trending Vuriables in Regression Analysis <br/>A Detrending Interpretation ofRegressions with a Time <br/>Trend <br/>Computing R-Squared when The Dependem' Variable Is <br/>Trending <br/>Seasonalirv <br/>Summary <br/>KeyTerms <br/>Problems <br/>Computer Exercises <br/><br/>Chapter 11 Further Issues in Using OLS with Time Series Data <br/>11.1 Stationary and Weakly Dependent Time Series <br/>Stationarv and Nonstationarv Time Series <br/>Weaklv Dependent Time Series <br/>11.2 Asymptotic Properties of OLS <br/>11.3 Using Highly Persistent Time Series in Regression Analysis <br/>Highlv Persistent Time Series <br/>Transforinations on Highlv Persistent Time Series <br/>Deciding Whether a Time Series Is 1(1) <br/>11.4 Dynamically Complete Modeis and the Absence of Serial Correlation <br/>11.5 The Homoskedasticity Assumption for Time Series Modeis <br/>Sunimary <br/>Key Terms <br/>Problems <br/>Computer Exercises <br/><br/>Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions <br/>12.1 Properties of OLS with Serially Correlated Errors<br/>Unbiasedness and Consistency <br/>Efficiency and Inference <br/>Goodness-ofFit <br/>Serial Correlation in the Presence of Lagged Dependenr Variables <br/>12.2 Testmg for Serial Correlation <br/>A t Testfor AR(1) Serial Correlation with Strictly Exogenous <br/>Regressors <br/>The Durbin-Watson Tesi under Classical Assurnptions <br/>Testing for AR(]) Serial Correlation without Strictly Exogenous <br/>Regressors<br/>Testing for Higher Order Serial Correlation <br/>12.3 Correcting for Serial Correlation with Strictly Exogenous Regressors <br/>Obraining the Besr Linear Unbiased Estimaror in the AR(1) <br/>Model <br/>Feasible GLS Estimation with AR(1) Errors <br/>Comparing OLS and FGLS <br/>Correcring for Higher Order Serial Correlation <br/>12.4 Differencing and Serial Correlation<br/>12.5 Serial Correlation-Robust Inference After OLS <br/>12.6 Heteroskedasticity in Time Series Regressions <br/>Heteroskedasticity-Robust Statistics <br/>Testing for Heteroskedasticitv <br/>Auto regressive Conditional Heteroskedasticity <br/>Heteroskedasticity and Serial Correlation in Regression <br/>Modeis <br/>Sunimary <br/>Key Terms <br/>Problems <br/>Computer Exercises <br/><br/>PART 3 <br/>ADVANCED TOPICS <br/><br/>Chapter 13 Pooling Cross Sections Across Time. Simple Panel Data Methods <br/>13.1 Pooling Independent Cross Sections Across Time <br/>The Chow Test for Strucrurai Change Across Time <br/>13.2 Policy Analysis with Pooled Cross Sections <br/>13.3 Two-Period Panei Data Analysis Organizing Panei Data <br/>13.4 Policy Analysis with Two-Period Panei Data <br/>13.5 Differencing with More than Two Time Periods<br/>Summary<br/>Key Terms <br/>Problems <br/>Computer Exercises <br/>Appendix 13A <br/>Chapter 14 Advanced Panei Data Methods<br/>14.1 Fixed Effects Estimation <br/>The Dummy Variable Regression<br/>Fixed Effects or First Dfferencing? <br/>Fixed Effects with Unbalanced Paneis <br/>14.2 Random Effects Modeis<br/>Random Effects or Fixed Effects?<br/>14.3 Applying Panei Data Methods to Other Data Structures<br/>Summary <br/>Key Terms <br/>Problems <br/>Computer Exercises <br/>Appendix 14A <br/>Chapter 15 Instrumental Variabies Estimation and Two Stage Least Squares<br/>15.1 Motivation: Omitted Variables in a Simple Regression Model <br/>Statistical Inference with the IV Estimator <br/>Properties oJIV with a Poor Instrumental Variabie <br/>Coinpuring R-Squared after IV Esriination <br/>15.2 IV Estimation of the Multiple Regression Model<br/>15.3 Two Stage Least Squares <br/>A Single Endogenous Explanatoiy Variable <br/>Multicoliinearitv and 2SLS <br/>Multiple Endogenous Explanatory Variables <br/>Testing Multiple Hypotheses after 2SLS Estimation <br/>15.4 IV Solutions to Errors-in-Variables Problems<br/>15.5 Testing for Endogeneity and Testing Overidentifying Restrictions<br/>Testing for Endogeneity <br/>Testing Overidentification Restrictions <br/>15.6 2SLS with Heteroskedasticity <br/>15.7 Applying 2SLS to Time Series Equations<br/>15.8 Applying 2SLS to Pooled Cross Sections and Panei Data<br/>Summary<br/>KeyTerms<br/>Problems<br/>Computer Exercises<br/>Appendix iSA <br/><br/>Chapter 16 Simultaneous Equations Modeis <br/>16.1 The Nature of Simuitaneous Equations Modeis <br/>16.2 Simultaneity Bias in OLS <br/>16.3 Identifying and Estimatirig a Structural Equation<br/>Idenhification in a Two-Equarion Ss'stem<br/>Estimation bv 2SLS<br/>16.4 Systems with More than Two Equations<br/>Identification in Systems with Three or More Equations<br/>Estimation<br/>16.5 Simultaneous Equations Models with Time Series <br/>16.6 Simultaneous Equations Modeis with Panei Data<br/>Summary <br/>Key Terms<br/>Problems<br/>Computer Exercises <br/><br/>Chapter 17 Limited Dependent Vanable Modeis and Sample Selection Corrections <br/>17.1 Logit and Probit Modeis for Binary Response<br/>Specifying Logit and Probit Modeis <br/>Maximum Likelihood Estimation of Logit and Probit <br/>Modeis <br/>Testing Multiple Hypotheses<br/>inrerprering fixe Logit and Probit Estimares <br/>17.2 The Tobit Model for Comer Solution Responses<br/>interprering the Tobit Estimules <br/>Specification Issues in Tobit Modeis <br/>17.3 The Poisson Regression Model <br/>17.4 Censored and Truncated Regression Modeis <br/>Censored Regression Modeis <br/>Truncated Regression Models <br/>17.5 Sample Selection Corrections <br/>When is OLS on the Selected Sample Consistent? <br/>Incidental Truncation <br/>Summary <br/>Key Terms <br/>Problems <br/>Computer Exercises<br/>Appendix 17A <br/><br/>Chapter 18 Advanced Time Series Topics <br/>18.1 intinite Distributed Lag Models<br/>The Geometric (or Koyck) Distributed Lag <br/>Rational Distributed Lag Modeis <br/>18.2 Testing for Unit Roots<br/>18.3 Spurious Regression <br/>18.4 Cointegration and Error Correction Modeis<br/>Cointegration <br/>Error Correction Modeis <br/>18.5 Forecasting <br/>Types of Regression Modeis Used for Forecasting <br/>One-Step-Ahead Forecasting <br/>C'omparing One-Step-Ahead Forecasis <br/>Multiple Step-Ahead-Forecasts <br/>Forecasting Trending, Seasonal, and Inregrated Processes <br/>Summary <br/>Key Terms <br/>Problems <br/>Computer Exercises <br/><br/>Chapter 19 Carrying out an Empirical Project <br/>19.1 Posing a Question <br/>19.2 Literature Review <br/>19.3 Data Coliection <br/>Deciding on the Appropriate Data Se<br/>Eniering and Storing Your Data<br/>Jnspecting. Cleaning, and Suinmarizing Your Data <br/>19.4 Econometric Analysis <br/>19.5 Writing an Empirical Paper<br/>Introduction <br/>Conceptual (ar Theoretica!) Framework<br/>Econo,netric Modeis and Esti,nation Methods <br/>The Data <br/>Results <br/>Conclusions <br/>Stvle Hints <br/>Summary <br/>Key Terms <br/>Sample Empirical Projects <br/>List of Journals <br/>Data Sources <br/><br/>APPENDICES<br/>Appendix A Basic Mathematical Tools <br/>A. 1 The Surnmation Operator and Descriptive Statistics <br/>A.2 Properties of Linear Functions<br/>A.3 Proportions and Percentages <br/>A.4 Some Special Functions and Their Properties <br/>Quadratic Functions <br/>The Natural Logarithm <br/>The Exponential Function <br/>A.5 Differential Calculus<br/>Suminary<br/>Key Terms <br/>Problems <br/>Appendix B Fundamentais of Probability <br/>B.1 Random Variables and Their Probability Distributions <br/>Discrete Random Variables <br/>Conrinuous Random Variables <br/>B.2 Joint Distributions. Conditional Distributions, and lndependence <br/>Joini Distributions and independence <br/>C'onditional Distributions <br/>B.3 Features of Probability Distributions <br/>A Measure of Central Tendencv: The Expected Value <br/>Properties ofExpected Values <br/>Another Measure of Central Tendencv: The Median <br/>Measures of Variahilirv: Variance and Standard Deviarion <br/>Variance<br/>Standard Deviation <br/>Standardizing a Random Variable <br/>B.4 Features of Joint and Conditional Distributions <br/>Measures ofAssociation. Covariance and Correlation <br/>Cova riance<br/>Correlation Coefficient <br/>Variance of Sums of Random Variables <br/>Conditional Expectation <br/>Properties of Conditional Expectation <br/>Conditional Variance <br/>B.5 The Normal and Related Distributions <br/>The Normal Distribution <br/>The Standard Normal Distribution <br/>Additional Properties of the Normal Distribution <br/>The Chi-Square Distribution <br/>The t Distribution <br/>The F Distribution<br/>Summary <br/>Key Terms <br/>Problems<br/>Appendix C Fundamentais of Mathematicai Statistics <br/>CI Populations, Parameters, and Random Sampling <br/>Sampling <br/>C.2 Finite Sample Properties of Estimators <br/>Estimators and Estimates <br/>Unbiasedness <br/>The Sampling Variance of Estimators <br/>Efficiency <br/>C.3 Asymptotic or Large Sample Properties of Estimators <br/>Consistency <br/>Asymptotic Normaiitv <br/>C.4 General Approaches to Parameter Estimation <br/>Method ofMoments <br/>Maximum Likelihood <br/>Leasi Squares <br/>C.5 Interval Estimation and Confidence Intervals <br/>The Nature of Interval Estimation <br/>Confidence intervals ftr the Mean from a Normallv Distributed <br/>Population <br/>A Simple Rule of Thumb fora 95% Confidence Interval <br/>Asyrnptotic Confidence íntervals for Nonnormal <br/>Populations <br/>C.6 Hypothesis Testing <br/>Fundamentais of Hypothesis Testing <br/>Testing Hvpotheses About me Mean iii a Normal <br/>Population <br/>Asymptotic Tesis for Nonnormai Populations <br/>Computing and Lising p- Values <br/>The Relarionship Between Confidence Intervais and Hypothesis Tes/ing <br/>Practical Versus Statistical Significance <br/>C.7 Remarks on Notation <br/>Summary <br/>Key Terms <br/>Problems <br/>Appendix D Summary of Matrix Algebra <br/>D.1 Basic Definitions <br/>D.2 Matrix Operations <br/>Matrix Addition <br/>Scalar Multiplication <br/>Matrix Multiplication <br/>Transpose <br/>Partitioned Matrix Multiplication <br/>Trace <br/>Inverse <br/>D.3 Linear Independence. Rank of a Matrix <br/>D.4 Quadratic Forms and Positive Definite Matrices <br/>D.5 ldempotent Matrices <br/>D.6 Differentiation of Linear and Quadratic Forms <br/>D.7Moments and Distributions of Random Vectors <br/>Expected Value <br/>Variance-Covariance Matrix <br/>Multivariate Normal Distribution <br/>Chi-Square Distribution <br/>t Distribution <br/>F Distribution <br/>Summary <br/>Key Terms<br/>Problems<br/>Appendix E The Linear Regression Model in Matrix Form <br/>E.1 The Model and Ordinary Least Squares Estimation <br/>E.2 Finite Sample Properties of OLS<br/>E.3 Statistical Inference <br/>E.4 Some Asymptotic Analysis <br/>Wald Statistics for Testing Multiple Hvporheses <br/>Summary <br/>Key Terms <br/>Problems<br/>Appendix F Answers to Chapter Questions <br/>Appendix G Statistical Tables <br/><br/><br/> |
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| Biblioteca Agamenon Magalhães | Biblioteca Agamenon Magalhães | 2019-06-27 | 30082 | 3300151 W913i | 2019-0147 | 1 | 2019-06-27 | Livros |