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Introductory Econometrics: A Modern Approach

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Efnisyfirlit

  • Brief Contents
  • Contents
  • Chapter 1: The Nature of Econometrics and Economic Data
    • 1-1 What Is Econometrics?
    • 1-2 Steps in Empirical Economic Analysis
    • 1-3 The Structure of Economic Data
      • 1-3a Cross-Sectional Data
      • 1-3b Time Series Data
      • 1-3c Pooled Cross Sections
      • 1-3d Panel or Longitudinal Data
      • 1-3e A Comment on Data Structures
    • 1-4 Causality, Ceteris Paribus, and Counterfactual Reasoning
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Part 1: Regression Analysis with ­Cross-Sectional Data
    • Chapter 2: The Simple Regression Model
      • 2-1 Definition of the Simple Regression Model
      • 2-2 Deriving the Ordinary Least Squares Estimates
        • 2-2a A Note on Terminology
      • 2-3 Properties of OLS on Any Sample of Data
        • 2-3a Fitted Values and Residuals
        • 2-3b Algebraic Properties of OLS Statistics
        • 2-3c Goodness-of-Fit
      • 2-4 Units of Measurement and Functional Form
        • 2-4a The Effects of Changing Units of Measurement on OLS Statistics
        • 2-4b Incorporating Nonlinearities in Simple Regression
        • 2-4c The Meaning of “Linear” Regression
      • 2-5 Expected Values and Variances of the OLS Estimators
        • 2-5a Unbiasedness of OLS
        • 2-5b Variances of the OLS Estimators
        • 2-5c Estimating the Error Variance
      • 2-6 Regression through the Origin and Regression on a Constant
      • 2-7 Regression on a Binary Explanatory Variable
        • 2-7a Counterfactual Outcomes, Causality, and Policy Analysis
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 3: Multiple Regression Analysis: Estimation
      • 3-1 Motivation for Multiple Regression
        • 3-1a The Model with Two Independent Variables
        • 3-1b The Model with k Independent Variables
      • 3-2 Mechanics and Interpretation of Ordinary Least Squares
        • 3-2a Obtaining the OLS Estimates
        • 3-2b Interpreting the OLS Regression Equation
        • 3-2c On the Meaning of “Holding Other Factors Fixed” in Multiple Regression
        • 3-2d Changing More Than One Independent Variable Simultaneously
        • 3-2e OLS Fitted Values and Residuals
        • 3-2f A “Partialling Out” Interpretation of Multiple Regression
        • 3-2g Comparison of Simple and Multiple Regression Estimates
        • 3-2h Goodness-of-Fit
        • 3-2i Regression through the Origin
      • 3-3 The Expected Value of the OLS Estimators
        • 3-3a Including Irrelevant Variables in a Regression Model
        • 3-3b Omitted Variable Bias: The Simple Case
        • 3-3c Omitted Variable Bias: More General Cases
      • 3-4 The Variance of the OLS Estimators
        • 3-4a The Components of the OLS Variances: Multicollinearity
        • 3-4b Variances in Misspecified Models
        • 3-4c Estimating s2: Standard Errors of the OLS Estimators
      • 3-5 Efficiency of OLS: The Gauss-Markov Theorem
      • 3-6 Some Comments on the Language of Multiple Regression Analysis
      • 3-7 Several Scenarios for Applying Multiple Regression
        • 3-7a Prediction
        • 3-7b Efficient Markets
        • 3-7c Measuring the Tradeoff between Two Variables
        • 3-7d Testing for Ceteris Paribus Group Differences
        • 3-7e Potential Outcomes, Treatment Effects, and Policy Analysis
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 4: Multiple Regression Analysis: Inference
      • 4-1 Sampling Distributions of the OLS Estimators
      • 4-2 Testing Hypotheses about a Single Population Parameter: The t Test
        • 4-2a Testing against One-Sided Alternatives
        • 4-2b Two-Sided Alternatives
        • 4-2c Testing Other Hypotheses about bj
        • 4-2d Computing p-Values for t Tests
        • 4-2e A Reminder on the Language of Classical Hypothesis Testing
        • 4-2f Economic, or Practical, versus Statistical Significance
      • 4-3 Confidence Intervals
      • 4-4 Testing Hypotheses about a Single Linear Combination of the Parameters
      • 4-5 Testing Multiple Linear Restrictions: The F Test
        • 4-5a Testing Exclusion Restrictions
        • 4-5b Relationship between F and t Statistics
        • 4-5c The R-Squared Form of the F Statistic
        • 4-5d Computing p-values for F Tests
        • 4-5e The F Statistic for Overall Significance of a Regression
        • 4-5f Testing General Linear Restrictions
      • 4-6 Reporting Regression Results
      • 4-7 Revisiting Causal Effects and Policy Analysis
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 5: Multiple Regression Analysis: OLS Asymptotics
      • 5-1 Consistency
        • 5-1a Deriving the Inconsistency in OLS
      • 5-2 Asymptotic Normality and Large Sample Inference
        • 5-2a Other Large Sample Tests: The Lagrange Multiplier Statistic
      • 5-3 Asymptotic Efficiency of OLS
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 6: Multiple Regression Analysis: Further Issues
      • 6-1 Effects of Data Scaling on OLS Statistics
        • 6-1a Beta Coefficients
      • 6-2 More on Functional Form
        • 6-2a More on Using Logarithmic Functional Forms
        • 6-2b Models with Quadratics
        • 6-2c Models with Interaction Terms
        • 6-2d Computing Average Partial Effects
      • 6-3 More on Goodness-of-Fit and Selection of Regressors
        • 6-3a Adjusted R-Squared
        • 6-3b Using Adjusted R-Squared to Choose between Nonnested Models
        • 6-3c Controlling for Too Many Factors in Regression Analysis
        • 6-3d Adding Regressors to Reduce the Error Variance
      • 6-4 Prediction and Residual Analysis
        • 6.4 a Confidence Intervals for Predictions
        • 6-4b Residual Analysis
        • 6-4c Predicting y When log(y) Is the Dependent Variable
        • 6-4d Predicting y When the Dependent Variable Is log(y)
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 7: Multiple Regression Analysis with Qualitative Information
      • 7-1 Describing Qualitative Information
      • 7-2 A Single Dummy Independent Variable
        • 7-2a Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y)
      • 7-3 Using Dummy Variables for Multiple Categories
        • 7-3a Incorporating Ordinal Information by Using Dummy Variables
      • 7-4 Interactions Involving Dummy Variables
        • 7-4a Interactions among Dummy Variables
        • 7-4b Allowing for Different Slopes
        • 7-4c Testing for Differences in Regression Functions across Groups
      • 7-5 A Binary Dependent Variable: The Linear Probability Model
      • 7-6 More on Policy Analysis and Program Evaluation
        • 7-6a Program Evaluation and Unrestricted Regression Adjustment
      • 7-7 Interpreting Regression Results with Discrete Dependent Variables
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 8: Heteroskedasticity
      • 8-1 Consequences of Heteroskedasticity for OLS
      • 8-2 Heteroskedasticity-Robust Inference after OLS Estimation
        • 8-2a Computing Heteroskedasticity-Robust LM Tests
      • 8-3 Testing for Heteroskedasticity
        • 8-3a The White Test for Heteroskedasticity
      • 8-4 Weighted Least Squares Estimation
        • 8-4a The Heteroskedasticity Is Known up to a Multiplicative Constant
        • 8-4b The Heteroskedasticity Function Must Be Estimated: Feasible GLS
        • 8-4c What If the Assumed Heteroskedasticity Function Is Wrong?
        • 8-4d Prediction and Prediction Intervals with Heteroskedasticity
      • 8-5 The Linear Probability Model Revisited
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 9: More on Specification and Data Issues
      • 9-1 Functional Form Misspecification
        • 9-1a RESET as a General Test for Functional Form Misspecification
        • 9-1b Tests against Nonnested Alternatives
      • 9-2 Using Proxy Variables for Unobserved Explanatory Variables
        • 9-2a Using Lagged Dependent Variables as Proxy Variables
        • 9-2b A Different Slant on Multiple Regression
        • 9-2c Potential Outcomes and Proxy Variables
      • 9-3 Models with Random Slopes
      • 9-4 Properties of OLS under Measurement Error
        • 9-4a Measurement Error in the Dependent Variable
        • 9-4b Measurement Error in an Explanatory Variable
      • 9-5 Missing Data, Nonrandom Samples, and Outlying Observations
        • 9-5a Missing Data
        • 9-5b Nonrandom Samples
        • 9-5c Outliers and Influential Observations
      • 9-6 Least Absolute Deviations Estimation
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
  • Part 2: Regression Analysis with Time Series Data
    • Chapter 10: Basic Regression Analysis with Time Series Data
      • 10-1 The Nature of Time Series Data
      • 10-2 Examples of Time Series Regression Models
        • 10-2a Static Models
        • 10-2b Finite Distributed Lag Models
        • 10-2c A Convention about the Time Index
      • 10-3 Finite Sample Properties of OLS under Classical Assumptions
        • 10-3a Unbiasedness of OLS
        • 10-3b The Variances of the OLS Estimators and the Gauss-Markov Theorem
        • 10-3c Inference under the Classical Linear Model Assumptions
      • 10-4 Functional Form, Dummy Variables, and Index Numbers
      • 10-5 Trends and Seasonality
        • 10-5a Characterizing Trending Time Series
        • 10-5b Using Trending Variables in Regression Analysis
        • 10-5c A Detrending Interpretation of Regressions with a Time Trend
        • 10-5d Computing R-Squared When the Dependent Variable Is Trending
        • 10-5e Seasonality
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 11: Further Issues in Using OLS with Time Series Data
      • 11-1 Stationary and Weakly Dependent Time Series
        • 11-1a Stationary and Nonstationary Time Series
        • 11-1b Weakly Dependent Time Series
      • 11-2 Asymptotic Properties of OLS
      • 11-3 Using Highly Persistent Time Series in Regression Analysis
        • 11-3a Highly Persistent Time Series
        • 11-3b Transformations on Highly Persistent Time Series
        • 11-3c Deciding Whether a Time Series Is I(1)
      • 11-4 Dynamically Complete Models and the Absence of Serial Correlation
      • 11-5 The Homoskedasticity Assumption for Time Series Models
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 12: Serial Correlation and Heteroskedasticity in Time Series Regressions
      • 12-1 Properties of OLS with Serially Correlated Errors
        • 12-1a Unbiasedness and Consistency
        • 12-1b Efficiency and Inference
        • 12-1c Goodness-of-Fit
        • 12-1d Serial Correlation in the Presence of Lagged Dependent Variables
      • 12-2 Serial Correlation–Robust Inference after OLS
      • 12-3 Testing for Serial Correlation
        • 12-3a A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors
        • 12-3b The Durbin-Watson Test under Classical Assumptions
        • 12-3c Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors
        • 12-3d Testing for Higher-Order Serial Correlation
      • 12-4 Correcting for Serial Correlation with Strictly Exogenous Regressors
        • 12-4a Obtaining the Best Linear Unbiased Estimator in the AR(1) Model
        • 12-4b Feasible GLS Estimation with AR(1) Errors
        • 12-4c Comparing OLS and FGLS
        • 12-4d Correcting for Higher-Order Serial Correlation
        • 12-4e What if the Serial Correlation Model Is Wrong?
      • 12-5 Differencing and Serial Correlation
      • 12-6 Heteroskedasticity in Time Series Regressions
        • 12-6a Heteroskedasticity-Robust Statistics
        • 12-6b Testing for Heteroskedasticity
        • 12-6c Autoregressive Conditional Heteroskedasticity
        • 12-6d Heteroskedasticity and Serial Correlation in Regression Models
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
  • Part 3: Advanced Topics
    • Chapter 13: Pooling Cross Sections across Time: Simple Panel Data Methods
      • 13-1 Pooling Independent Cross Sections across Time
        • 13-1a The Chow Test for Structural Change across Time
      • 13-2 Policy Analysis with Pooled Cross Sections
        • 13-2a Adding an Additional Control Group
        • 13-2b A General Framework for Policy Analysis with Pooled Cross Sections
      • 13-3 Two-Period Panel Data Analysis
        • 13-3a Organizing Panel Data
      • 13-4 Policy Analysis with Two-Period Panel Data
      • 13-5 Differencing with More Than Two Time Periods
        • 13-5a Potential Pitfalls in First Differencing Panel Data
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 14: Advanced Panel Data Methods
      • 14-1 Fixed Effects Estimation
        • 14-1a The Dummy Variable Regression
        • 14-1b Fixed Effects or First Differencing?
        • 14-1c Fixed Effects with Unbalanced Panels
      • 14-2 Random Effects Models
        • 14-2a Random Effects or Pooled OLS?
        • 14-2b Random Effects or Fixed Effects?
      • 14-3 The Correlated Random Effects Approach
        • 14-3a Unbalanced Panels
      • 14-4 General Policy Analysis with Panel Data
        • 14-4a Advanced Considerations with Policy Analysis
      • 14-5 Applying Panel Data Methods to Other Data Structures
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 15: Instrumental Variables Estimation and Two-Stage Least Squares
      • 15-1 Motivation: Omitted Variables in a Simple Regression Model
        • 15-1a Statistical Inference with the IV Estimator
        • 15-1b Properties of IV with a Poor Instrumental Variable
        • 15-1c Computing R-Squared after IV Estimation
      • 15-2 IV Estimation of the Multiple Regression Model
      • 15-3 Two-Stage Least Squares
        • 15-3a A Single Endogenous Explanatory Variable
        • 15-3b Multicollinearity and 2SLS
        • 15-3c Detecting Weak Instruments
        • 15-3d Multiple Endogenous Explanatory Variables
        • 15-3e Testing Multiple Hypotheses after 2SLS Estimation
      • 15-4 IV Solutions to Errors-in-Variables Problems
      • 15-5 Testing for Endogeneity and Testing Overidentifying Restrictions
        • 15-5a Testing for Endogeneity
        • 15-5b Testing Overidentification Restrictions
      • 15-6 2SLS with Heteroskedasticity
      • 15-7 Applying 2SLS to Time Series Equations
      • 15-8 Applying 2SLS to Pooled Cross Sections and Panel Data
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 16: Simultaneous Equations Models
      • 16-1 The Nature of Simultaneous Equations Models
      • 16-2 Simultaneity Bias in OLS
      • 16-3 Identifying and Estimating a Structural Equation
        • 16-3a Identification in a Two-Equation System
        • 16-3b Estimation by 2SLS
      • 16-4 Systems with More Than Two Equations
        • 16-4a Identification in Systems with Three or More Equations
        • 16-4b Estimation
      • 16-5 Simultaneous Equations Models with Time Series
      • 16-6 Simultaneous Equations Models with Panel Data
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 17: Limited Dependent Variable Models and Sample Selection Corrections
      • 17-1 Logit and Probit Models for Binary Response
        • 17-1a Specifying Logit and Probit Models
        • 17-1b Maximum Likelihood Estimation of Logit and Probit Models
        • 17-1c Testing Multiple Hypotheses
        • 17-1d Interpreting the Logit and Probit Estimates
      • 17-2 The Tobit Model for Corner Solution Responses
        • 17-2a Interpreting the Tobit Estimates
        • 17-2b Specification Issues in Tobit Models
      • 17-3 The Poisson Regression Model
      • 17-4 Censored and Truncated Regression Models
        • 17-4a Censored Regression Models
        • 17-4b Truncated Regression Models
      • 17-5 Sample Selection Corrections
        • 17-5a When Is OLS on the Selected Sample Consistent?
        • 17-5b Incidental Truncation
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 18: Advanced Time Series Topics
      • 18-1 Infinite Distributed Lag Models
        • 18-1a The Geometric (or Koyck) Distributed Lag Model
        • 18-1b Rational Distributed Lag Models
      • 18-2 Testing for Unit Roots
      • 18-3 Spurious Regression
      • 18-4 Cointegration and Error Correction Models
        • 18-4a Cointegration
        • 18-4b Error Correction Models
      • 18-5 Forecasting
        • 18-5a Types of Regression Models Used for Forecasting
        • 18-5b One-Step-Ahead Forecasting
        • 18-5c Comparing One-Step-Ahead Forecasts
        • 18-5d Multiple-Step-Ahead Forecasts
        • 18-5e Forecasting Trending, Seasonal, and Integrated Processes
      • Summary
      • Key Terms
      • Problems
      • Computer Exercises
    • Chapter 19: Carrying Out an Empirical Project
      • 19-1 Posing a Question
      • 19-2 Literature Review
      • 19-3 Data Collection
        • 19-3a Deciding on the Appropriate Data Set
        • 19-3b Entering and Storing Your Data
        • 19-3c Inspecting, Cleaning, and Summarizing Your Data
      • 19-4 Econometric Analysis
      • 19-5 Writing an Empirical Paper
        • 19-5a Introduction
        • 19-5b Conceptual (or Theoretical) Framework
        • 19-5c Econometric Models and Estimation Methods
        • 19-5d The Data
        • 19-5e Results
        • 19.5f Conclusions
        • 19-5g Style Hints
      • Summary
      • Key Terms
      • Sample Empirical Projects
      • List of Journals
      • Data Sources
  • Math Refresher A Basic Mathematical Tools
    • A-1 The Summation Operator and Descriptive Statistics
    • A-2 Properties of Linear Functions
    • A-3 Proportions and Percentages
    • A-4 Some Special Functions and Their Properties
      • A-4a Quadratic Functions
      • A-4b The Natural Logarithm
      • A-4c The Exponential Function
    • A-5 Differential Calculus
      • Summary
      • Key Terms
      • Problems
  • Math Refresher B Fundamentals of Probability
    • B-1 Random Variables and Their Probability Distributions
      • B-1a Discrete Random Variables
      • B-1b Continuous Random Variables
    • B-2 Joint Distributions, Conditional Distributions, and Independence
      • B-2a Joint Distributions and Independence
      • B-2b Conditional Distributions
    • B-3 Features of Probability Distributions
      • B-3a A Measure of Central Tendency: The Expected Value
      • B-3b Properties of Expected Values
      • B-3c Another Measure of Central Tendency: The Median
      • B-3d Measures of Variability: Variance and Standard Deviation
      • B-3e Variance
      • B-3f Standard Deviation
      • B-3g Standardizing a Random Variable
      • B-3h Skewness and Kurtosis
    • B-4 Features of Joint and Conditional Distributions
      • B-4a Measures of Association: Covariance and Correlation
      • B-4b Covariance
      • B-4c Correlation Coefficient
      • B-4d Variance of Sums of Random Variables
      • B-4e Conditional Expectation
      • B-4f Properties of Conditional Expectation
      • B-4g Conditional Variance
    • B-5 The Normal and Related Distributions
      • B-5a The Normal Distribution
      • B-5b The Standard Normal Distribution
      • B-5c Additional Properties of the Normal Distribution
      • B-5d The Chi-Square Distribution
      • B-5e The t Distribution
      • B-5f The F Distribution
    • Summary
    • Key Terms
    • Problems
  • Math Refresher C Fundamentals of Mathematical Statistics
    • C-1 Populations, Parameters, and Random Sampling
      • C-1a Sampling
    • C-2 Finite Sample Properties of Estimators
      • C-2a Estimators and Estimates
      • C-2b Unbiasedness
      • C-2c The Sampling Variance of Estimators
      • C-2d Efficiency
    • C-3 Asymptotic or Large Sample Properties of Estimators
      • C-3a Consistency
      • C-3b Asymptotic Normality
    • C-4 General Approaches to Parameter Estimation
      • C-4a Method of Moments
      • C-4b Maximum Likelihood
      • C-4c Least Squares
    • C-5 Interval Estimation and Confidence Intervals
      • C-5a The Nature of Interval Estimation
      • C-5b Confidence Intervals for the Mean from a Normally Distributed Population
      • C-5c A Simple Rule of Thumb for a 95% Confidence Interval
      • C-5d Asymptotic Confidence Intervals for Nonnormal Populations
    • C-6 Hypothesis Testing
      • C-6a Fundamentals of Hypothesis Testing
      • C-6b Testing Hypotheses about the Mean in a Normal Population
      • C-6c Asymptotic Tests for Nonnormal Populations
      • C-6d Computing and Using p-Values
      • C-6e The Relationship between Confidence Intervals and Hypothesis Testing
      • C-6f Practical versus Statistical Significance
    • C-7 Remarks on Notation
    • Summary
    • Key Terms
    • Problems
  • Advanced Treatment D Summary of Matrix Algebra
    • D-1 Basic Definitions
    • D-2 Matrix Operations
      • D-2a Matrix Addition
      • D-2b Scalar Multiplication
      • D-2c Matrix Multiplication
      • D-2d Transpose
      • D-2e Partitioned Matrix Multiplication
      • D-2f Trace
      • D-2g Inverse
    • D-3 Linear Independence and Rank of a Matrix
    • D-4 Quadratic Forms and Positive Definite Matrices
    • D-5 Idempotent Matrices
    • D-6 Differentiation of Linear and Quadratic Forms
    • D-7 Moments and Distributions of Random Vectors
      • D-7a Expected Value
      • D-7b Variance-Covariance Matrix
      • D-7c Multivariate Normal Distribution
      • D-7d Chi-Square Distribution
      • D-7e t Distribution
      • D-7f F Distribution
    • Summary
    • Key Terms
    • Problems
  • Advanced Treatment E The Linear Regression Model in Matrix Form
    • E-1 The Model and Ordinary Least Squares Estimation
      • E-1a The Frisch-Waugh Theorem
    • E-2 Finite Sample Properties of OLS
    • E-3 Statistical Inference
    • E-4 Some Asymptotic Analysis
      • E-4a Wald Statistics for Testing Multiple Hypotheses
    • Summary
    • Key Terms
    • Problems
  • Answers to Going Further Questions
  • Statistical Tables
  • References
  • Glossary
  • Index

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Vörumerki: Cengage
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Introductory Econometrics: A Modern Approach

Vörumerki: Cengage
Vörunúmer: 9781337671330
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