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Fundamentals of Applied Econometrics

Author:
Richard A. Ashley
Publisher: Wiley
Copyright: 2012
ISBN-13: 978-0-470-59182-6
Pages: 710; hardcover
Price: $98.50

Comment from the Stata technical group

Fundamentals of Applied Econometrics by Richard A. Ashley is an elementary introduction to econometrics focused on the linear regression model. Because it uses no matrix algebra, the book can be used as the main text in a one- or two-semester undergraduate econometrics course or a master’s-level methods course in the social sciences. The book is also recommended for junior analysts in industry and government who need a reference book to guide them along while doing empirical work.

The book is divided into three parts and begins with a refresher on the basics of statistics and hypothesis testing. The core of the book is centered on linear regression, beginning with the simple bivariate regression model with independent errors. Later chapters then introduce multiple regression, stochastic regressors and endogeneity, and regression with time-series data. Three full chapters are devoted to diagnostics and to testing model specification. The third part of the book, which could form the basis of a second-semester course when supplemented with other materials, discusses panel data, forecasting time series, and binary-choice models.

Most of the numerical examples in the book are produced using Stata, as are all the graphs. Stata datasets for all examples and exercises are available at the publisher’s website. Throughout the book are what the author calls “Active Learning Exercises,” longer problems that guide readers through the analysis of real datasets and help them get a “hands-on” feel for doing econometrics.


Table of contents

What’s Different about This Book
Working with Data in the “Active Learning Exercises”
Acknowledgments
Notation
Part I INTRODUCTION AND STATISTICS REVIEW
Chapter 1 INTRODUCTION
1.1 Preliminaries
1.2 Example: Is Growth Good for the Poor?
1.3 What’s to Come
ALE 1a: An Econometrics “Time Capsule”
ALE 1b: Investigating the Slope Graphically Using a Scatterplot
ALE 1c: Examining Some Disturbing Variations on Dollar & Kraay’s Model
ALE 1d: The Pitfalls of Making Scatterplots with Trended Time-Series Data
Chapter 2 A REVIEW OF PROBABILITY THEORY
2.1 Introduction
2.2 Random Variables
2.3 Discrete Random Variables
2.4 Continuous Random Variables
2.5 Some Initial Results on Expectations
2.6 Some Results on Variances
2.7 A Pair of Random Variables
2.8 The Linearity Property of Expectations
2.9 Statistical Independence
2.10 Normally Distributed Random Variables
2.11 Three Special Properties of Normally Distributed Variables
2.12 Distribution of a Linear Combination of Normally Distributed Random Variables
2.13 Conclusion
Exercises
ALE 2a: The Normal Distribution
ALE 2b: Central Limit Theorem Simulators on the Web
Appendix 2.1: The Conditional Mean of a Random Variable
Appendix 2.2: Proof of the Linearity Property for the Expectation of a Weighted Sum of Two Discretely Distributed Random Variables
Chapter 3 ESTIMATING THE MEAN OF A NORMALLY DISTRIBUTED RANDOM VARIABLE
3.1 Introduction
3.2 Estimating μ by Curve Fitting
3.3 The Sampling Distribution of Y-bar
3.4 Consistency — A First Pass
3.5 Unbiasedness and the Optimal Estimator
3.6 The Squared Error Loss Function and the Optimal Estimator
3.7 The Feasible Optimality Properties: Efficiency and BLUness
3.8 Summary
3.9 Conclusions and Lead-in to Next Chapter
Exercises
ALE 3a: Investigating the Consistency of the Sample Mean and Sample Variance Using Computer-Generated Data
ALE 3b: Estimating Means and Variances Regarding the Standard & Poor's SP500 Stock Index
Chapter 4 STATISTICAL INFERENCE ON THE MEAN OF A NORMALLY DISTRIBUTED RANDOM VARIABLE
4.1 Introduction
4.2 Standardizing the distribution of Y-bar
4.3 Confidence Intervals for μ When σ2 Is Known
4.4 Hypothesis Testing when σ2 Is Known
4.5 Using S2 to Estimate σ2 (and Introducing the Chi-Squared Distribution)
4.6 Inference Results on μ When σ2 is Unknown (and Introducing the Student's t Distribution)
4.7 Application: State-Level U.S. Unemployment Rates
4.8 Introduction to Diagnostic Checking: Testing the Constancy of μ across the Sample
4.9 Introduction to Diagnostic Checking: Testing the Constancy of σ2 across the Sample
4.10 Some General Comments on Diagnostic Checking
4.11 Closing Comments
Exercises
ALE 4a: Investigating the Sensitivity of Hypothesis Test p-Values to Departures from the NIID (μ, σ2) Assumption Using Computer-Generated Data
ALE 4b: Individual Income Data from the Panel Study on Income Dynamics (PSID) — Does Birth-Month Matter?
Part II REGRESSION ANALYSIS
Chapter 5 THE BIVARIATE REGRESSION MODEL: INTRODUCTION, ASSUMPTIONS, AND PARAMETER ESTIMATES
5.1 Introduction
5.2 The Transition from Mean Estimation to Regression: Analyzing the Variation of Per Capita Real Output across Countries
5.3 The Bivariate Regression Model — Its Form and the “Fixed in Repeated Samples” Causality Assumption
5.4 The Assumptions on the Model Error Term, Ui
5.5 Least Squares Estimation of α and β
5.6 Interpreting the Least Squares Estimates of α and β
5.7 Bivariate Regression with a Dummy Variable: Quantifying the Impact of College Graduation on Weekly Earnings
Exercises
ALE 5a: Exploring the Penn World Table Data
ALE 5b: Verifying α-hat*ols and β-hat*ols over a Very Small Data Set
ALE 5c: Extracting and Downloading CPS Data from the Census Bureau Web Site
ALE 5d: Verifying that β-hat*ols on a Dummy Variable Equals the Difference in the Sample Means
Appendix 5.1: β-hat*ols When xi Is a Dummy Variable
Chapter 6 THE BIVARIATE LINEAR REGRESSION MODEL: SAMPLING DISTRIBUTIONS AND ESTIMATOR PROPERTIES
6.1 Introduction
6.2 Estimates and Estimators
6.3 β-hat as a Linear Estimator and the Least Squares Weights
6.4 The Sampling Distribution of β-hat
6.5 Properties of β-hat: Consistency
6.6 Properties of β-hat: Best Linear Unbiasedness
6.7 Summary
Exercises
ALE 6a: Outliers and Other Perhaps Overly Influential Observations: Investigating the Sensitivity of β-hat to an Outlier Using Computer-Generated Data
ALE 6b: Investigating the Consistency of β-hat Using Computer-Generated Data
Chapter 7 THE BIVARIATE LINEAR REGRESSION MODEL: INFERENCE ON β
7.1 Introduction
7.2 A Statistic for β with a Known Distribution
7.3 A 95% Confidence Interval for β with σ2 Given
7.4 Estimates versus Estimators and the Role of the Model Assumptions
7.5 Testing a Hypothesis about β with σ2 Given
7.6 Estimating σ2
7.7 Properties of S2
7.8 A Statistic for β Not Involving σ2
7.9 A 95% Confidence Interval for β with σ2 Unknown
7.10 Testing a Hypothesis about β with σ2 Unknown
7.11 Application: The Impact of College Graduation on Weekly Earnings (Inference Results)
7.12 Application: Is Growth Good for the Poor?
7.13 Summary
Exercises
ALE 7a: Investigating the Sensitivity of Slope Coefficient Inference to Departures from the Ui~NIID(O, σ2) Assumption Using Computer-Generated Data
ALE 7b: Distorted Inference in Time-Series Regressions with Serially Correlated Model Errors: An Investigation Using Computer-Generated Data
Appendix 7.1: Proof That S2 Is Independent of β-hat
Chapter 8 THE BIVARIATE REGRESSION MODEL: R2 AND PREDICTION
8.1 Introduction
8.2 Quantifying How Well the Model Fits the Data
8.3 Prediction as a Tool for Model Variation
8.4 Predicting YN+1 given xN+1
Exercises
ALE 8a: On the Folly of Trying Too Hard: A Simple Example of "Data Mining"
Chapter 9 THE MULTIPLE REGRESSION MODEL
9.1 Introduction
9.2 The Multiple Regression Model
9.3 Why the Multiple Regression Model is Necessary and Important
9.4 Multiple Regression Parameter Estimates via Least Squares Fitting
9.5 Properties and Sampling Distribution of β-hatols,1...β-hatols,k
9.6 Overelaborate Multiple Regression Models
9.7 Underelaborate Multiple Regression Models
9.8 Application: The Curious Relationship between Marriage and Death
9.9 Multicollinearity
9.10 Application: The Impact of College Graduation and Gender on Weekly Earnings
9.11 Application: Vote Fraud in Philadelphia Senatorial Elections
Exercises
ALE 9a: A Statistical Examination of the Florida Voting in the November 2000 Presidential Election — Did Mistaken Votes for Pat Buchanan Swing the Election from Gore to Bush?
ALE 9b: Observing and Interpreting the Symptoms of Multicollinearity
ALE 9c: The Market Value of a Bathroom in Georgia
Appendix 9.1: Prediction Using the Multiple Regression Model
Chapter 10 DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE REGRESSION MODEL: DEALING WITH POTENTIAL OUTLIERS AND HETEROSCEDASTICITY IN THE CROSS-SECTIONAL DATA CASE
10.1 Introduction
10.2 The Fitting Errors as Large-Sample Estimates of the Model Errors, U1...UN
10.3 Reasons for Checking the Normality of the Model Errors, U1...UN
10.4 Heteroscedasticity and Its Consequences
10.5 Testing for Heteroscedasticity
10.6 Correcting for Heteroscedasticity of Known Form
10.7 Correcting for Heteroscedasticity of Unknown Form
10.8 Application: Is Growth Good for the Poor? Diagnostically Checking the Dollar/Kraay (2002) Model.
Exercises
ALE 10a: The Fitting Errors as Approximates for the Model Errors
ALE 10b: Does Output Per Person Depend on Human Capital? (A Test of the Augmented Solow Model of Growth)
ALE 10c: Is Trade Good or Bad for the Environment? (First Pass)
Chapter 11 STOCHASTIC REGRESSORS AND ENDOGENEITY
11.1 Introduction
11.2 Unbiasedness of the OLS Slope Estimator with a Stochastic Regressor Independent of the Model Error
11.3 A Brief Introduction to Asymptotic Theory
11.4 Asymptotic Results for the OLS Slope Estimator with a Stochastic Regressor
11.5 Endogenous Regressors: Omitted Variables
11.6 Endogenous Regressors: Measurement Error
11.7 Endogenous Regressors: Joint Determination — Introduction to Simultaneous Equation Macroeconomic and Microeconomic Models
11.8 How Large a Sample Is “Large Enough”? The Simulation Alternative
11.9 An Example: Bootstrapping the Angrist–Krueger (1991) Model
Exercises
ALE 11a: Central Limit Theorem Convergence for β-hatOLS in the Bivariate Regression Model
ALE 11b: Bootstrap Analysis of the Convergence of the Asymptotic Sampling Distributions for Multiple Regression Model Parameter Estimators
Appendix 11.1: The Algebra of Probability Limits
Appendix 11.2: Derivation of the Asymptotic Sampling Distribution of the OLS Slope Estimator
Chapter 12 INSTRUMENTAL VARIABLES ESTIMATION
12.1 Introduction — Why It Is Challenging to Test for Endogeneity
12.2 Correlation versus Causation — Two Ways to Untie the Knot
12.3 The Instrumental Variables Slope Estimator (and Proof of Its Consistency) in the Bivariate Regression Model
12.4 Inference Using the Instrumental Variables Slope Estimator
12.5 The Two-Stage Least Squares Estimator for the Overidentified Case
12.6 Application: The Relationship between Education and Wages (Angrist and Krueger, 1991)
Exercises
ALE 12a: The Role of Institutions "Rule of Law" in Economic Growth
ALE 12b: Is Trade Good or Bad for the Environment? (Completion)
ALE 12c: The Impact of Military Service on the Smoking Behavior of Veterans
ALE 12d: The Effect of Measurement-Error Contamination on OLS Regression Estimates and the Durbin/Bartlett IV Estimators
Appendix 12.1: Derivation of the Asymptotic Sampling Distribution of the Instrumental Variables Slope Estimator
Appendix 12.2: Proof That the 2SLS Composite Instrument is Asymptotically Uncorrelated with the Model Error Term
Chapter 13 DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE REGRESSION MODEL: THE TIME-SERIES DATA CASE (PART A)
13.1 An Introduction to Time-Series Data, with a "Road Map" for this Chapter
13.2 The Bivariate Time-Series Regression Model with Fixed Regressors but Serially Correlated Model Errors, U1 ... UT
13.3 Disastrous Parameter Inference with Correlated Model Errors: Two Cautionary Examples Based on U.S. Consumption Expenditures Data
13.4 The AR(1) Model for Serial Dependence in a Time-Series
13.5 The Consistency of φ-hat1OLS as an Estimator of φ1 in the AR(1) Model and Its Asymptotic Distribution
13.6 Application of the AR(1) Model to the Errors of the (Detrended) U.S. Consumption Function — and a Straightforward Test for Serially Correlated Regression Errors
13.7 Dynamic Model Respecification: An Effective Response to Serially Correlated Regression Model Errors, with an Application to the (Detrended) U.S. Consumption Function
Exercises
Appendix 13.1: Derivation of the Asymptotic Sampling Distribution of φ-hat1OLS in the AR(1) Model
Chapter 14 DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE REGRESSION MODEL: THE TIME-SERIES DATA CASE (PART B)
14.1 Introduction: Generalizing the Results to Multiple Time-Series
14.2 The Dynamic Multiple Regression Model
14.3 I(1) or “Random Walk” Time-Series
14.4 Capstone Example Part 1: Modeling Monthly U.S. Consumption Expenditures in Growth Rates
14.5 Capstone Example Part 2: Modeling Monthly U.S. Consumption Expenditures in Growth Rates and Levels (Cointegrated Model)
14.6 Capstone Example Part 3: Modeling the Level of Monthly U.S. Consumption Expenditures
14.7 Which is Better: To Model in Levels or to Model in Changes?
Exercises
ALE 14a: Analyzing the Food Price Sub-Index of the Monthly U.S. Consumer Price Index
ALE 14b: Estimating Taylor Rules for How the U.S. Fed Sets Interest Rates
PART III ADDITIONAL TOPICS IN REGRESSION ANALYSIS
Chapter 15 REGRESSION MODELING WITH PANEL DATA (PART A)
15.1 Introduction: A Source of Large (but Likely Heterogeneous) Data Sets
15.2 Revisiting the Chapter 5 Illustrative Example Using Data from the Penn World Table
15.3 A Multivariate Empirical Example
15.4 The Fixed Effects and the Between Effects Models
15.5 The Random Effects Model
15.6 Diagnostic Checking of an Estimated Panel Data Model
Exercises
Appendix 15.1: Stata Code for the Generalized Hausman Test
Chapter 16 REGRESSION MODELING WITH PANEL DATA (PART B)
16.1 Relaxing Strict Exogeneity: Dynamics and Lagged Dependent Variables
16.2 Relaxing Strict Exogeneity: The First-Differences Model
16.3 Summary
Exercises
ALE 16a: Assessing the Impact of 4-H Participation on the Standardized Test Scores of Florida Schoolchildren
ALE 16b: Using Panel Data Methods to Reanalyze Data from a Public Goods Experiment
Chapter 17 A CONCISE INTRODUCTION TO TIME-SERIES ANALYSIS AND FORECASTING (PART A)
17.1 Introduction: The Difference between Time-Series Analysis and Time-Series Econometrics
17.2 Optimal Forecasts: The Primacy of the Conditional-Mean Forecast and When It Is Better to Use a Biased Forecast
17.3 The Crucial Assumption (Stationarity) and the Fundamental Tools: The Time-Plot and the Sample Correlogram
17.4 A Polynomial in the Lag Operator and Its Inverse: The Key to Understanding and Manipulating Linear Time-Series Models
17.5 Identification/Estimation/Checking/Forecasting of an Invertible MA(q) Model
17.6 Identification/Estimation/Checking/Forecasting of a Stationary AR(p) Model
17.7 ARMA(p,q) Models and a Summary of the Box–Jenkins Modeling Algorithm
Exercises
ALE 17a: Conditional Forecasting Using a Large-Scale Macroeconometric Model
ALE 17b: Modeling U.S. GNP
Chapter 18 A CONCISE INTRODUCTION TO TIME-SERIES ANALYSIS AND FORECASTING (PART B)
18.1 Integrated — ARIMA(p,d,q) — Models and “Trend like” Behavior
18.2 A Univariate Application: Modeling the Monthly U.S. Treasury Bill Rate
18.3 Seasonal Time-Series Data and ARMA Deseasonalization of the U.S. Total Nonfarm Payroll Time-Series
18.4 Multivariate Time-Series Models
18.5 Post-Sample Model Forecast Evaluation and Testing for Granger-Causation
18.6 Modeling Nonlinear Serial Dependence in a Time-Series
18.7 Additional Topics in Forecasting
Exercises
ALE 18a: Modeling the South Korean Won–U.S. Dollar Exchange Rate
ALE 18b: Modeling the Daily Returns to Ford Motor Company Stock
Chapter 19 PARAMETER ESTIMATION BEYOND CURVE-FITTING: MLE (WITH AN APPLICATION TO BINARY-CHOICE MODELS) AND GMM (WITH AN APPLICATION TO IV REGRESSION)
19.1 Introduction
19.2 Maximum Likelihood Estimation of a Simple Bivariate Regression Model
19.3 Maximum Likelihood Estimation of Binary-Choice Regression Models
19.4 Generalized Method of Moments (GMM) Estimation
Exercises
ALE 19a: Probit Modeling of the Determinants of Labor Force Participation
Appendix 19.1: GMM Estimation of β in the Bivariate Regression Model (Optimal Penalty-Weights and Sampling Distribution)
Chapter 20 CONCLUDING COMMENTS
20.1 The Goals of This Book
20.2 Diagnostic Checking and Model Respecification
20.3 The Four “Big Mistakes”
Mathematics Review
Index
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