Fundamentals of Applied Econometrics
Author: 
Richard A. Ashley 
Publisher: 
Wiley 
Copyright: 
2012 
ISBN13: 
9780470591826 
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 twosemester undergraduate econometrics course or a
master’slevel 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 timeseries 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 secondsemester course when supplemented with other
materials, discusses panel data, forecasting time series, and binarychoice
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
“handson” 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 TimeSeries
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 Ybar
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 Leadin to Next Chapter
Exercises
ALE 3a: Investigating the Consistency of the Sample Mean and Sample
Variance Using ComputerGenerated 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 Ybar
4.3 Confidence Intervals for μ When σ^{2} Is Known
4.4 Hypothesis Testing when σ^{2} Is Known
4.5 Using S^{2} to Estimate σ^{2} (and Introducing the
ChiSquared Distribution)
4.6 Inference Results on μ When σ^{2} is Unknown (and Introducing the
Student's t Distribution)
4.7 Application: StateLevel 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 σ^{2}
across the Sample
4.10 Some General Comments on Diagnostic Checking
4.11 Closing Comments
Exercises
ALE 4a: Investigating the Sensitivity of Hypothesis Test
pValues to Departures from the NIID (μ, σ^{2}) Assumption
Using ComputerGenerated Data
ALE 4b: Individual Income Data from the Panel Study on Income
Dynamics (PSID) — Does BirthMonth 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, U_{i}
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 αhat^{*}_{ols} and βhat^{*}_{ols} over a Very Small Data Set
ALE 5c: Extracting and Downloading CPS Data from the Census Bureau
Web Site
ALE 5d: Verifying that βhat^{*}_{ols} on a Dummy Variable Equals the Difference
in the Sample Means
Appendix 5.1: βhat^{*}_{ols} When x_{i} 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 βhat as a Linear Estimator and the Least Squares Weights
6.4 The Sampling Distribution of βhat
6.5 Properties of βhat: Consistency
6.6 Properties of βhat: Best Linear Unbiasedness
6.7 Summary
Exercises
ALE 6a: Outliers and Other Perhaps Overly Influential Observations:
Investigating the Sensitivity of βhat to an Outlier Using
ComputerGenerated Data
ALE 6b: Investigating the Consistency of βhat Using ComputerGenerated
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 σ^{2} Given
7.4 Estimates versus Estimators and the Role of the Model Assumptions
7.5 Testing a Hypothesis about β with σ^{2} Given
7.6 Estimating σ^{2}
7.7 Properties of S^{2}
7.8 A Statistic for β Not Involving σ^{2}
7.9 A 95% Confidence Interval for β with σ^{2} Unknown
7.10 Testing a Hypothesis about β with σ^{2} 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 U_{i}~NIID(O, σ^{2}) Assumption
Using ComputerGenerated Data
ALE 7b: Distorted Inference in TimeSeries Regressions with Serially
Correlated Model Errors: An Investigation Using ComputerGenerated
Data
Appendix 7.1: Proof That S^{2} Is Independent of βhat
Chapter 8 THE BIVARIATE REGRESSION MODEL: R^{2} 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 Y_{N+1} given x_{N+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 βhat_{ols,1}...βhat_{ols,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
CROSSSECTIONAL DATA CASE
10.1 Introduction
10.2 The Fitting Errors as LargeSample Estimates of the Model
Errors, U_{1}...U_{N}
10.3 Reasons for Checking the Normality of the Model Errors,
U_{1}...U_{N}
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 βhat^{OLS} 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 TwoStage 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 MeasurementError 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 TIMESERIES DATA CASE (PART A)
13.1 An Introduction to TimeSeries Data, with a "Road Map" for this
Chapter
13.2 The Bivariate TimeSeries Regression Model with Fixed
Regressors but Serially Correlated Model Errors,
U_{1} ... U_{T}
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 TimeSeries
13.5 The Consistency of φhat_{1}^{OLS} as an Estimator of φ_{1} 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 φhat_{1}^{OLS} in the AR(1) Model
Chapter 14 DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE REGRESSION
MODEL: THE TIMESERIES DATA CASE (PART B)
14.1 Introduction: Generalizing the Results to Multiple TimeSeries
14.2 The Dynamic Multiple Regression Model
14.3 I(1) or “Random Walk” TimeSeries
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 SubIndex 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 FirstDifferences Model
16.3 Summary
Exercises
ALE 16a: Assessing the Impact of 4H 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 TIMESERIES ANALYSIS AND FORECASTING
(PART A)
17.1 Introduction: The Difference between TimeSeries Analysis
and TimeSeries Econometrics
17.2 Optimal Forecasts: The Primacy of the ConditionalMean Forecast
and When It Is Better to Use a Biased Forecast
17.3 The Crucial Assumption (Stationarity) and the Fundamental
Tools: The TimePlot and the Sample Correlogram
17.4 A Polynomial in the Lag Operator and Its Inverse: The Key to
Understanding and Manipulating Linear TimeSeries 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 LargeScale
Macroeconometric Model
ALE 17b: Modeling U.S. GNP
Chapter 18 A CONCISE INTRODUCTION TO TIMESERIES 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 TimeSeries Data and ARMA Deseasonalization of the
U.S. Total Nonfarm Payroll TimeSeries
18.4 Multivariate TimeSeries Models
18.5 PostSample Model Forecast Evaluation and Testing for
GrangerCausation
18.6 Modeling Nonlinear Serial Dependence in a TimeSeries
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 CURVEFITTING: MLE (WITH AN APPLICATION
TO BINARYCHOICE 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 BinaryChoice 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 PenaltyWeights 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