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### eBook Details:

- Full title:
**Introductory Econometrics: A Modern Approach, 7th Edition** - Edition:
**7th** - Copyright year:
**2020** - Publisher:
**Cengage Learning** - Author:
**Jeffrey M. Wooldridge** - ISBN:
**9781337558860**,**9781337671330** - Format:
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### Description of Introductory Econometrics: A Modern Approach, 7th Edition:

Gain an understanding of how econometrics can answer today’s questions in business, policy evaluation and forecasting with Wooldridge’s INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 7E. Unlike traditional texts, this book’s practical, yet professional, approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines.

The author has organized the book’s presentation around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 data sets in different formats. Updates introduce the latest developments in the field, including the recent advances in the so-called “causal effects” or “treatment effects,” to provide a complete understanding of the impact and importance of econometrics today.

#### Table of Contents of Introductory Econometrics: A Modern Approach, 7th Edition PDF ebook:

Brief ContentsContentsChapter 1: The Nature of Econometrics and Economic Data1-1 What Is Econometrics?1-2 Steps in Empirical Economic Analysis1-3 The Structure of Economic Data1-3a Cross-Sectional Data1-3b Time Series Data1-3c Pooled Cross Sections1-3d Panel or Longitudinal Data1-3e A Comment on Data Structures1-4 Causality, Ceteris Paribus, and Counterfactual ReasoningSummaryKey TermsProblemsComputer ExercisesPart 1: Regression Analysis with Cross-Sectional DataChapter 2: The Simple Regression Model2-1 Definition of the Simple Regression Model2-2 Deriving the Ordinary Least Squares Estimates2-2a A Note on Terminology2-3 Properties of OLS on Any Sample of Data2-3a Fitted Values and Residuals2-3b Algebraic Properties of OLS Statistics2-3c Goodness-of-Fit2-4 Units of Measurement and Functional Form2-4a The Effects of Changing Units of Measurement on OLS Statistics2-4b Incorporating Nonlinearities in Simple Regression2-4c The Meaning of “Linear” Regression2-5 Expected Values and Variances of the OLS Estimators2-5a Unbiasedness of OLS2-5b Variances of the OLS Estimators2-5c Estimating the Error Variance2-6 Regression through the Origin and Regression on a Constant2-7 Regression on a Binary Explanatory Variable2-7a Counterfactual Outcomes, Causality, and Policy AnalysisSummaryKey TermsProblemsComputer ExercisesChapter 3: Multiple Regression Analysis: Estimation3-1 Motivation for Multiple Regression3-1a The Model with Two Independent Variables3-1b The Model with k Independent Variables3-2 Mechanics and Interpretation of Ordinary Least Squares3-2a Obtaining the OLS Estimates3-2b Interpreting the OLS Regression Equation3-2c On the Meaning of “Holding Other Factors Fixed” in Multiple Regression3-2d Changing More Than One Independent Variable Simultaneously3-2e OLS Fitted Values and Residuals3-2f A “Partialling Out” Interpretation of Multiple Regression3-2g Comparison of Simple and Multiple Regression Estimates3-2h Goodness-of-Fit3-2i Regression through the Origin3-3 The Expected Value of the OLS Estimators3-3a Including Irrelevant Variables in a Regression Model3-3b Omitted Variable Bias: The Simple Case3-3c Omitted Variable Bias: More General Cases3-4 The Variance of the OLS Estimators3-4a The Components of the OLS Variances: Multicollinearity3-4b Variances in Misspecified Models3-4c Estimating s2: Standard Errors of the OLS Estimators3-5 Efficiency of OLS: The Gauss-Markov Theorem3-6 Some Comments on the Language of Multiple Regression Analysis3-7 Several Scenarios for Applying Multiple Regression3-7a Prediction3-7b Efficient Markets3-7c Measuring the Tradeoff between Two Variables3-7d Testing for Ceteris Paribus Group Differences3-7e Potential Outcomes, Treatment Effects, and Policy AnalysisSummaryKey TermsProblemsComputer ExercisesChapter 4: Multiple Regression Analysis: Inference4-1 Sampling Distributions of the OLS Estimators4-2 Testing Hypotheses about a Single Population Parameter: The t Test4-2a Testing against One-Sided Alternatives4-2b Two-Sided Alternatives4-2c Testing Other Hypotheses about bj4-2d Computing p-Values for t Tests4-2e A Reminder on the Language of Classical Hypothesis Testing4-2f Economic, or Practical, versus Statistical Significance4-3 Confidence Intervals4-4 Testing Hypotheses about a Single Linear Combination of the Parameters4-5 Testing Multiple Linear Restrictions: The F Test4-5a Testing Exclusion Restrictions4-5b Relationship between F and t Statistics4-5c The R-Squared Form of the F Statistic4-5d Computing p-values for F Tests4-5e The F Statistic for Overall Significance of a Regression4-5f Testing General Linear Restrictions4-6 Reporting Regression Results4-7 Revisiting Causal Effects and Policy AnalysisSummaryKey TermsProblemsComputer ExercisesChapter 5: Multiple Regression Analysis: OLS Asymptotics5-1 Consistency5-1a Deriving the Inconsistency in OLS5-2 Asymptotic Normality and Large Sample Inference5-2a Other Large Sample Tests: The Lagrange Multiplier Statistic5-3 Asymptotic Efficiency of OLSSummaryKey TermsProblemsComputer ExercisesChapter 6: Multiple Regression Analysis: Further Issues6-1 Effects of Data Scaling on OLS Statistics6-1a Beta Coefficients6-2 More on Functional Form6-2a More on Using Logarithmic Functional Forms6-2b Models with Quadratics6-2c Models with Interaction Terms6-2d Computing Average Partial Effects6-3 More on Goodness-of-Fit and Selection of Regressors6-3a Adjusted R-Squared6-3b Using Adjusted R-Squared to Choose between Nonnested Models6-3c Controlling for Too Many Factors in Regression Analysis6-3d Adding Regressors to Reduce the Error Variance6-4 Prediction and Residual Analysis6.4 a Confidence Intervals for Predictions6-4b Residual Analysis6-4c Predicting y When log(y) Is the Dependent Variable6-4d Predicting y When the Dependent Variable Is log(y)SummaryKey TermsProblemsComputer ExercisesChapter 7: Multiple Regression Analysis with Qualitative Information7-1 Describing Qualitative Information7-2 A Single Dummy Independent Variable7-2a Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y)7-3 Using Dummy Variables for Multiple Categories7-3a Incorporating Ordinal Information by Using Dummy Variables7-4 Interactions Involving Dummy Variables7-4a Interactions among Dummy Variables7-4b Allowing for Different Slopes7-4c Testing for Differences in Regression Functions across Groups7-5 A Binary Dependent Variable: The Linear Probability Model7-6 More on Policy Analysis and Program Evaluation7-6a Program Evaluation and Unrestricted Regression Adjustment7-7 Interpreting Regression Results with Discrete Dependent VariablesSummaryKey TermsProblemsComputer ExercisesChapter 8: Heteroskedasticity8-1 Consequences of Heteroskedasticity for OLS8-2 Heteroskedasticity-Robust Inference after OLS Estimation8-2a Computing Heteroskedasticity-Robust LM Tests8-3 Testing for Heteroskedasticity8-3a The White Test for Heteroskedasticity8-4 Weighted Least Squares Estimation8-4a The Heteroskedasticity Is Known up to a Multiplicative Constant8-4b The Heteroskedasticity Function Must Be Estimated: Feasible GLS8-4c What If the Assumed Heteroskedasticity Function Is Wrong?8-4d Prediction and Prediction Intervals with Heteroskedasticity8-5 The Linear Probability Model RevisitedSummaryKey TermsProblemsComputer ExercisesChapter 9: More on Specification and Data Issues9-1 Functional Form Misspecification9-1a RESET as a General Test for Functional Form Misspecification9-1b Tests against Nonnested Alternatives9-2 Using Proxy Variables for Unobserved Explanatory Variables9-2a Using Lagged Dependent Variables as Proxy Variables9-2b A Different Slant on Multiple Regression9-2c Potential Outcomes and Proxy Variables9-3 Models with Random Slopes9-4 Properties of OLS under Measurement Error9-4a Measurement Error in the Dependent Variable9-4b Measurement Error in an Explanatory Variable9-5 Missing Data, Nonrandom Samples, and Outlying Observations9-5a Missing Data9-5b Nonrandom Samples9-5c Outliers and Influential Observations9-6 Least Absolute Deviations EstimationSummaryKey TermsProblemsComputer ExercisesPart 2: Regression Analysis with Time Series DataChapter 10: Basic Regression Analysis with Time Series Data10-1 The Nature of Time Series Data10-2 Examples of Time Series Regression Models10-2a Static Models10-2b Finite Distributed Lag Models10-2c A Convention about the Time Index10-3 Finite Sample Properties of OLS under Classical Assumptions10-3a Unbiasedness of OLS10-3b The Variances of the OLS Estimators and the Gauss-Markov Theorem10-3c Inference under the Classical Linear Model Assumptions10-4 Functional Form, Dummy Variables, and Index Numbers10-5 Trends and Seasonality10-5a Characterizing Trending Time Series10-5b Using Trending Variables in Regression Analysis10-5c A Detrending Interpretation of Regressions with a Time Trend10-5d Computing R-Squared When the Dependent Variable Is Trending10-5e SeasonalitySummaryKey TermsProblemsComputer ExercisesChapter 11: Further Issues in Using OLS with Time Series Data11-1 Stationary and Weakly Dependent Time Series11-1a Stationary and Nonstationary Time Series11-1b Weakly Dependent Time Series11-2 Asymptotic Properties of OLS11-3 Using Highly Persistent Time Series in Regression Analysis11-3a Highly Persistent Time Series11-3b Transformations on Highly Persistent Time Series11-3c Deciding Whether a Time Series Is I(1)11-4 Dynamically Complete Models and the Absence of Serial Correlation11-5 The Homoskedasticity Assumption for Time Series ModelsSummaryKey TermsProblemsComputer ExercisesChapter 12: Serial Correlation and Heteroskedasticity in Time Series Regressions12-1 Properties of OLS with Serially Correlated Errors12-1a Unbiasedness and Consistency12-1b Efficiency and Inference12-1c Goodness-of-Fit12-1d Serial Correlation in the Presence of Lagged Dependent Variables12-2 Serial CorrelationRobust Inference after OLS12-3 Testing for Serial Correlation12-3a A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors12-3b The Durbin-Watson Test under Classical Assumptions12-3c Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors12-3d Testing for Higher-Order Serial Correlation12-4 Correcting for Serial Correlation with Strictly Exogenous Regressors12-4a Obtaining the Best Linear Unbiased Estimator in the AR(1) Model12-4b Feasible GLS Estimation with AR(1) Errors12-4c Comparing OLS and FGLS12-4d Correcting for Higher-Order Serial Correlation12-4e What if the Serial Correlation Model Is Wrong?12-5 Differencing and Serial Correlation12-6 Heteroskedasticity in Time Series Regressions12-6a Heteroskedasticity-Robust Statistics12-6b Testing for Heteroskedasticity12-6c Autoregressive Conditional Heteroskedasticity12-6d Heteroskedasticity and Serial Correlation in Regression ModelsSummaryKey TermsProblemsComputer ExercisesPart 3: Advanced TopicsChapter 13: Pooling Cross Sections across Time: Simple Panel Data Methods13-1 Pooling Independent Cross Sections across Time13-1a The Chow Test for Structural Change across Time13-2 Policy Analysis with Pooled Cross Sections13-2a Adding an Additional Control Group13-2b A General Framework for Policy Analysis with Pooled Cross Sections13-3 Two-Period Panel Data Analysis13-3a Organizing Panel Data13-4 Policy Analysis with Two-Period Panel Data13-5 Differencing with More Than Two Time Periods13-5a Potential Pitfalls in First Differencing Panel DataSummaryKey TermsProblemsComputer ExercisesChapter 14: Advanced Panel Data Methods14-1 Fixed Effects Estimation14-1a The Dummy Variable Regression14-1b Fixed Effects or First Differencing?14-1c Fixed Effects with Unbalanced Panels14-2 Random Effects Models14-2a Random Effects or Pooled OLS?14-2b Random Effects or Fixed Effects?14-3 The Correlated Random Effects Approach14-3a Unbalanced Panels14-4 General Policy Analysis with Panel Data14-4a Advanced Considerations with Policy Analysis14-5 Applying Panel Data Methods to Other Data StructuresSummaryKey TermsProblemsComputer ExercisesChapter 15: Instrumental Variables Estimation and Two-Stage Least Squares15-1 Motivation: Omitted Variables in a Simple Regression Model15-1a Statistical Inference with the IV Estimator15-1b Properties of IV with a Poor Instrumental Variable15-1c Computing R-Squared after IV Estimation15-2 IV Estimation of the Multiple Regression Model15-3 Two-Stage Least Squares15-3a A Single Endogenous Explanatory Variable15-3b Multicollinearity and 2SLS15-3c Detecting Weak Instruments15-3d Multiple Endogenous Explanatory Variables15-3e Testing Multiple Hypotheses after 2SLS Estimation15-4 IV Solutions to Errors-in-Variables Problems15-5 Testing for Endogeneity and Testing Overidentifying Restrictions15-5a Testing for Endogeneity15-5b Testing Overidentification Restrictions15-6 2SLS with Heteroskedasticity15-7 Applying 2SLS to Time Series Equations15-8 Applying 2SLS to Pooled Cross Sections and Panel DataSummaryKey TermsProblemsComputer ExercisesChapter 16: Simultaneous Equations Models16-1 The Nature of Simultaneous Equations Models16-2 Simultaneity Bias in OLS16-3 Identifying and Estimating a Structural Equation16-3a Identification in a Two-Equation System16-3b Estimation by 2SLS16-4 Systems with More Than Two Equations16-4a Identification in Systems with Three or More Equations16-4b Estimation16-5 Simultaneous Equations Models with Time Series16-6 Simultaneous Equations Models with Panel DataSummaryKey TermsProblemsComputer ExercisesChapter 17: Limited Dependent Variable Models and Sample Selection Corrections17-1 Logit and Probit Models for Binary Response17-1a Specifying Logit and Probit Models17-1b Maximum Likelihood Estimation of Logit and Probit Models17-1c Testing Multiple Hypotheses17-1d Interpreting the Logit and Probit Estimates17-2 The Tobit Model for Corner Solution Responses17-2a Interpreting the Tobit Estimates17-2b Specification Issues in Tobit Models17-3 The Poisson Regression Model17-4 Censored and Truncated Regression Models17-4a Censored Regression Models17-4b Truncated Regression Models17-5 Sample Selection Corrections17-5a When Is OLS on the Selected Sample Consistent?17-5b Incidental TruncationSummaryKey TermsProblemsComputer ExercisesChapter 18: Advanced Time Series Topics18-1 Infinite Distributed Lag Models18-1a The Geometric (or Koyck) Distributed Lag Model18-1b Rational Distributed Lag Models18-2 Testing for Unit Roots18-3 Spurious Regression18-4 Cointegration and Error Correction Models18-4a Cointegration18-4b Error Correction Models18-5 Forecasting18-5a Types of Regression Models Used for Forecasting18-5b One-Step-Ahead Forecasting18-5c Comparing One-Step-Ahead Forecasts18-5d Multiple-Step-Ahead Forecasts18-5e Forecasting Trending, Seasonal, and Integrated ProcessesSummaryKey TermsProblemsComputer ExercisesChapter 19: Carrying Out an Empirical Project19-1 Posing a Question19-2 Literature Review19-3 Data Collection19-3a Deciding on the Appropriate Data Set19-3b Entering and Storing Your Data19-3c Inspecting, Cleaning, and Summarizing Your Data19-4 Econometric Analysis19-5 Writing an Empirical Paper19-5a Introduction19-5b Conceptual (or Theoretical) Framework19-5c Econometric Models and Estimation Methods19-5d The Data19-5e Results19.5f Conclusions19-5g Style HintsSummaryKey TermsSample Empirical ProjectsList of JournalsData SourcesMath Refresher A Basic Mathematical ToolsA-1 The Summation Operator and Descriptive StatisticsA-2 Properties of Linear FunctionsA-3 Proportions and PercentagesA-4 Some Special Functions and Their PropertiesA-4a Quadratic FunctionsA-4b The Natural LogarithmA-4c The Exponential FunctionA-5 Differential CalculusSummaryKey TermsProblemsMath Refresher B Fundamentals of ProbabilityB-1 Random Variables and Their Probability DistributionsB-1a Discrete Random VariablesB-1b Continuous Random VariablesB-2 Joint Distributions, Conditional Distributions, and IndependenceB-2a Joint Distributions and IndependenceB-2b Conditional DistributionsB-3 Features of Probability DistributionsB-3a A Measure of Central Tendency: The Expected ValueB-3b Properties of Expected ValuesB-3c Another Measure of Central Tendency: The MedianB-3d Measures of Variability: Variance and Standard DeviationB-3e VarianceB-3f Standard DeviationB-3g Standardizing a Random VariableB-3h Skewness and KurtosisB-4 Features of Joint and Conditional DistributionsB-4a Measures of Association: Covariance and CorrelationB-4b CovarianceB-4c Correlation CoefficientB-4d Variance of Sums of Random VariablesB-4e Conditional ExpectationB-4f Properties of Conditional ExpectationB-4g Conditional VarianceB-5 The Normal and Related DistributionsB-5a The Normal DistributionB-5b The Standard Normal DistributionB-5c Additional Properties of the Normal DistributionB-5d The Chi-Square DistributionB-5e The t DistributionB-5f The F DistributionSummaryKey TermsProblemsMath Refresher C Fundamentals of Mathematical StatisticsC-1 Populations, Parameters, and Random SamplingC-1a SamplingC-2 Finite Sample Properties of EstimatorsC-2a Estimators and EstimatesC-2b UnbiasednessC-2c The Sampling Variance of EstimatorsC-2d EfficiencyC-3 Asymptotic or Large Sample Properties of EstimatorsC-3a ConsistencyC-3b Asymptotic NormalityC-4 General Approaches to Parameter EstimationC-4a Method of MomentsC-4b Maximum LikelihoodC-4c Least SquaresC-5 Interval Estimation and Confidence IntervalsC-5a The Nature of Interval EstimationC-5b Confidence Intervals for the Mean from a Normally Distributed PopulationC-5c A Simple Rule of Thumb for a 95% Confidence IntervalC-5d Asymptotic Confidence Intervals for Nonnormal PopulationsC-6 Hypothesis TestingC-6a Fundamentals of Hypothesis TestingC-6b Testing Hypotheses about the Mean in a Normal PopulationC-6c Asymptotic Tests for Nonnormal PopulationsC-6d Computing and Using p-ValuesC-6e The Relationship between Confidence Intervals and Hypothesis TestingC-6f Practical versus Statistical SignificanceC-7 Remarks on NotationSummaryKey TermsProblemsAdvanced Treatment D Summary of Matrix AlgebraD-1 Basic DefinitionsD-2 Matrix OperationsD-2a Matrix AdditionD-2b Scalar MultiplicationD-2c Matrix MultiplicationD-2d TransposeD-2e Partitioned Matrix MultiplicationD-2f TraceD-2g InverseD-3 Linear Independence and Rank of a MatrixD-4 Quadratic Forms and Positive Definite MatricesD-5 Idempotent MatricesD-6 Differentiation of Linear and Quadratic FormsD-7 Moments and Distributions of Random VectorsD-7a Expected ValueD-7b Variance-Covariance MatrixD-7c Multivariate Normal DistributionD-7d Chi-Square DistributionD-7e t DistributionD-7f F DistributionSummaryKey TermsProblemsAdvanced Treatment E The Linear Regression Model in Matrix FormE-1 The Model and Ordinary Least Squares EstimationE-1a The Frisch-Waugh TheoremE-2 Finite Sample Properties of OLSE-3 Statistical InferenceE-4 Some Asymptotic AnalysisE-4a Wald Statistics for Testing Multiple HypothesesSummaryKey TermsProblemsAnswers to Going Further QuestionsStatistical TablesReferencesGlossaryIndex