Data Analysis for Business, Economics, and Policy

Cover
Half-title page
Review
Title page
Copyright page
Brief Contents
Contents
Why Use This Book
Simplified Notation
Acknowledgments
I Data Exploration
	1 Origins of Data
		1.1 What Is Data?
		1.2 Data Structures
		1.A1 CASE STUDY – Finding a Good Deal among Hotels: Data Collection
		1.3 Data Quality
		1.B1 CASE STUDY – Comparing Online and Offline Prices: Data Collection
		1.C1 CASE STUDY – Management Quality and Firm Performance: Data Collection
		1.4 How Data Is Born: The Big Picture
		1.5 Collecting Data from Existing Sources
		1.A2 CASE STUDY – Finding a Good Deal among Hotels: Data Collection
		1.B2 CASE STUDY – Comparing Online and Offline Prices: Data Collection
		1.6 Surveys
		1.C2 CASE STUDY – Management Quality and Firm Size: Data Collection
		1.7 Sampling
		1.8 Random Sampling
		1.B3 CASE STUDY – Comparing Online and Offline Prices: Data Collection
		1.C3 CASE STUDY – Management Quality and Firm Size: Data Collection
		1.9 Big Data
		1.10 Good Practices in Data Collection
		1.11 Ethical and Legal Issues of Data Collection
		1.12 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
	2 Preparing Data for Analysis
		2.1 Types of Variables
		2.2 Stock Variables, Flow Variables
		2.3 Types of Observations
		2.4 Tidy Data
		2.A1 CASE STUDY – Finding a Good Deal among Hotels: Data Preparation
		2.5 Tidy Approach for Multi-dimensional Data
		2.B1 CASE STUDY – Displaying Immunization Rates across Countries
		2.6 Relational Data and Linking Data Tables
		2.C1 CASE STUDY – Identifying Successful Football Managers
		2.7 Entity Resolution: Duplicates, Ambiguous Identification, and Non-entity Rows
		2.C2 CASE STUDY – Identifying Successful Football Managers
		2.8 Discovering Missing Values
		2.9 Managing Missing Values
		2.A2 CASE STUDY – Finding a Good Deal among Hotels: Data Preparation
		2.10 The Process of Cleaning Data
		2.11 Reproducible Workflow: Write Code and Document Your Steps
		2.12 Organizing Data Tables for a Project
		2.C3 CASE STUDY – Identifying Successful Football Managers
		2.C4 CASE STUDY – Identifying Successful Football Managers
		2.13 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		2.U1 Under the Hood: Naming Files
	3 Exploratory Data Analysis
		3.1 Why Do Exploratory Data Analysis?
		3.2 Frequencies and Probabilities
		3.3 Visualizing Distributions
		3.A1 CASE STUDY – Finding a Good Deal among Hotels: Data Exploration
		3.4 Extreme Values
		3.A2 CASE STUDY – Finding a Good Deal among Hotels: Data Exploration
		3.5 Good Graphs: Guidelines for Data Visualization
		3.A3 CASE STUDY – Finding a Good Deal among Hotels: Data Exploration
		3.6 Summary Statistics for Quantitative Variables
		3.B1 CASE STUDY – Comparing Hotel Prices in Europe: Vienna vs. London
		3.7 Visualizing Summary Statistics
		3.C1 CASE STUDY – Measuring Home Team Advantage in Football
		3.8 Good Tables
		3.C2 CASE STUDY – Measuring Home Team Advantage in Football
		3.9 Theoretical Distributions
		3.D1 CASE STUDY – Distributions of Body Height and Income
		3.10 Steps of Exploratory Data Analysis
		3.11 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		3.U1 Under the Hood: More on Theoretical Distributions
		Bernoulli distribution
		Binomial distribution
		Uniform distribution
		Power-law distribution
	4 Comparison and Correlation
		4.1 The y and the x
		4.A1 CASE STUDY – Management Quality and Firm Size: Describing Patterns of Association
		4.2 Conditioning
		4.3 Conditional Probabilities
		4.A2 CASE STUDY – Management Quality and Firm Size: Describing Patterns of Association
		4.4 Conditional Distribution, Conditional Expectation
		4.5 Conditional Distribution, Conditional Expectation with Quantitative
		4.A3 CASE STUDY – Management Quality and Firm Size: Describing Patterns of Association
		4.6 Dependence, Covariance, Correlation
		4.7 From Latent Variables to Observed Variables
		4.A4 CASE STUDY – Management Quality and Firm Size: Describing Patterns of Association
		4.8 Sources of Variation in x
		4.9 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		4.U1 Under the Hood: Inverse Conditional Probabilities, Bayes’ Rule
	5 Generalizing from Data
		5.1 When to Generalize and to What?
		5.A1 CASE STUDY – What Likelihood of Loss to Expect on a Stock Portfolio?
		5.2 Repeated Samples, Sampling Distribution, Standard Error
		5.A2 CASE STUDY – What Likelihood of Loss to Expect on a Stock Portfolio?
		5.3 Properties of the Sampling Distribution
		5.A3 CASE STUDY – What Likelihood of Loss to Expect on a Stock Portfolio?
		5.4 The Confidence Interval
		5.A4 CASE STUDY – What Likelihood of Loss to Expect on a Stock Portfolio?
		5.5 Discussion of the CI: Confidence or Probability?
		5.6 Estimating the Standard Error with the Bootstrap Method
		5.A5 CASE STUDY – What Likelihood of Loss to Expect on a Stock Portfolio?
		5.7 The Standard Error Formula
		5.A6 CASE STUDY – What Likelihood of Loss to Expect on a Stock Portfolio?
		5.8 External Validity
		5.A7 CASE STUDY – What Likelihood of Loss to Expect on a Stock Portfolio?
		5.9 Big Data, Statistical Inference, External Validity
		5.10 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		5.U1 Under the Hood: The Law of Large Numbers and the Central Limit Theorem
	6 Testing Hypotheses
		6.1 The Logic of Testing Hypotheses
		6.A1 CASE STUDY – Comparing Online and Offline Prices: Testing the Difference
		6.2 Null Hypothesis, Alternative Hypothesis
		6.3 The t-Test
		6.4 Making a Decision; False Negatives, False Positives
		6.5 The p-Value
		6.A2 CASE STUDY – Comparing Online and Offline Prices: Testing the Difference
		6.6 Steps of Hypothesis Testing
		6.7 One-Sided Alternatives
		6.B1 CASE STUDY – Testing the Likelihood of Loss on a Stock Portfolio
		6.8 Testing Multiple Hypotheses
		6.A3 CASE STUDY – Comparing Online and Offline Prices: Testing the Difference
		6.9 p-Hacking
		6.10 Testing Hypotheses with Big Data
		6.11 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
II Regression Analysis
	7 Simple Regression
		7.1 When and Why Do Simple Regression Analysis?
		7.2 Regression: Definition
		7.3 Non-parametric Regression
		7.A1 CASE STUDY – Finding a Good Deal among Hotels with Simple Regression
		7.4 Linear Regression: Introduction
		7.5 Linear Regression: Coefficient Interpretation
		7.6 Linear Regression with a Binary Explanatory Variable
		7.7 Coefficient Formula
		7.A2 CASE STUDY – Finding a Good Deal among Hotels with Simple Regression
		7.8 Predicted Dependent Variable and Regression Residual
		7.A3 CASE STUDY – Finding a Good Deal among Hotels with Simple Regression
		7.9 Goodness of Fit, R-Squared
		7.10 Correlation and Linear Regression
		7.11 Regression Analysis, Regression toward the Mean, Mean Reversion
		7.12 Regression and Causation
		7.A4 CASE STUDY – Finding a Good Deal among Hotels with Simple Regression
		7.13 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		7.U1 Under the Hood: Derivation of the OLS Formulae For the Intercept and Slope Coefficients
		7.U2 Under the Hood: More on Residuals and Predicted Values with ols
	8 Complicated Patterns and Messy Data
		8.1 When and Why Care about the Shape of the Associationbetween y and x?
		8.2 Taking Relative Differences or Log
		8.3 Log Transformation and Non-positive Values
		8.4 Interpreting Log Values in a Regression
		8.A1 CASE STUDY – Finding a Good Deal among Hotels with Nonlinear Function
		8.5 Other Transformations of Variables
		8.B1 CASE STUDY – How is Life Expectancy Related to the Average Income of a Country?
		8.6 Regression with a Piecewise Linear Spline
		8.7 Regression with Polynomial
		8.8 Choosing a Functional Form in a Regression
		8.B2 CASE STUDY – How is Life Expectancy Related to the Average Income of a Country?
		8.9 Extreme Values and Influential Observations
		8.10 Measurement Error in Variables
		8.11 Classical Measurement Error
		8.C1 CASE STUDY – Hotel Ratings and Measurement Error
		8.12 Non-classical Measurement Error and General Advice
		8.13 Using Weights in Regression Analysis
		8.B3 CASE STUDY – How is Life Expectancy Related to the Average Income of a Country?
		8.14 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		8.U1 Under the Hood: Details of the Log Approximation
		8.U2 Under the Hood: Deriving the Consequences of Classical Measurement error
	9 Generalizing Results of a Regression
		9.1 Generalizing Linear Regression Coefficients
		9.2 Statistical Inference: CI and SE of Regression Coefficients
		9.A1 CASE STUDY – Estimating Gender and Age Differences in Earnings
		9.3 Intervals for Predicted Values
		9.A2 CASE STUDY – Estimating Gender and Age Differences in Earnings
		9.4 Testing Hypotheses about Regression Coefficients
		9.5 Testing More Complex Hypotheses
		9.A3 CASE STUDY – Estimating Gender and Age Differences in Earnings
		9.6 Presenting Regression Results
		9.A4 CASE STUDY – Estimating Gender and Age Differences in Earnings
		9.7 Data Analysis to Help Assess External Validity
		9.B1 CASE STUDY – How Stable is the Hotel Price–Distance to Center Relationship?
		9.8 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		9.U1 Under the Hood: The Simple SE Formula for Regression Intercept
		9.U2 Under the Hood: The Law of Large Numbers for β
		9.U3 Under the Hood: Derving SE(β) with the Central Limit Theorem
		9.U4 Under the Hood: Degrees of Freedom Adjustment for the SE Formula
	10 Multiple Linear Regression
		10.1 Multiple Regression: Why and When?
		10.2 Multiple Linear Regression with Two Explanatory Variables
		10.3 Multiple Regression and Simple Regression: Omitted Variable Bias
		10.A1 CASE STUDY – Understanding the Gender Difference in Earnings
		10.4 Multiple Linear Regression Terminology
		10.5 Standard Errors and Confidence Intervals in Multiple Linear Regression
		10.6 Hypothesis Testing in Multiple Linear Regression
		10.A2 CASE STUDY – Understanding the Gender Difference in Earnings
		10.7 Multiple Linear Regression with Three or More Explanatory Variables
		10.8 Nonlinear Patterns and Multiple Linear Regression
		10.A3 CASE STUDY – Understanding the Gender Difference in Earnings
		10.9 Qualitative Right-Hand-Side Variables
		10.A4 CASE STUDY – Understanding the Gender Difference in Earnings
		10.10 Interactions: Uncovering Different Slopes across Groups
		10.A5 CASE STUDY – Understanding the Gender Difference in Earnings
		10.11 Multiple Regression and Causal Analysis
		10.A6 CASE STUDY – Understanding the Gender Difference in Earnings
		10.12 Multiple Regression and Prediction
		10.B1 CASE STUDY – Finding a Good Deal among Hotels with Multiple Regression
		10.13 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		10.U1 Under the Hood: A Two-Step Procedure to Get the Multiple Regression Coefficient
	11 Modeling Probabilities
		11.1 The Linear Probability Model
		11.2 Predicted Probabilities in the Linear Probability Model
		11.A1 CASE STUDY – Does Smoking Pose a Health Risk?
		11.3 Logit and Probit
		11.A2 CASE STUDY – Does Smoking Pose a Health Risk?
		11.4 Marginal Differences
		11.A3 CASE STUDY – Does Smoking Pose a Health Risk?
		11.5 Goodness of Fit: R-Squared and Alternatives
		11.6 The Distribution of Predicted Probabilities
		11.7 Bias and Calibration
		11.B1 CASE STUDY – Are Australian Weather Forecasts Well Calibrated?
		11.8 Refinement
		11.A4 CASE STUDY – Does Smoking Pose a Health risk?
		11.9 Using Probability Models for Other Kinds of y Variables
		11.10 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		11.U1 Under the Hood: Saturated Models
		11.U2 Under the Hood: Maximum Likelihood Estimation and Search Algorithms
		11.U3 Under the Hood: From logit and probit Coefficients to Marginal Differences
	12 Regression with Time Series Data
		12.1 Preparation of Time Series Data
		12.2 Trend and Seasonality
		12.3 Stationarity, Non-stationarity, Random Walk
		12.A1 CASE STUDY – Returns on a Company Stock and Market Returns
		12.4 Time Series Regression
		12.A2 CASE STUDY – Returns on a Company Stock and Market Returns
		12.5 Trends, Seasonality, Random Walks in a Regression
		12.B1 CASE STUDY – Electricity Consumption and Temperature
		12.6 Serial Correlation
		12.7 Dealing with Serial Correlation in Time Series Regressions
		12.B2 CASE STUDY – Electricity Consumption and Temperature
		12.8 Lags of x in a Time Series Regression
		12.B3 CASE STUDY – Electricity Consumption and Temperature
		12.9 The Process of Time Series Regression Analysis
		12.10 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		12.U1 Under the Hood: Testing for Unit Root
III Prediction
	13 A Framework for Prediction
		13.1 Prediction Basics
		13.2 Various Kinds of Prediction
		13.A1 CASE STUDY – Predicting Used Car Value with Linear Regressions
		13.3 The Prediction Error and Its Components
		13.A2 CASE STUDY – Predicting Used Car Value with Linear Regressions
		13.4 The Loss Function
		13.5 Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
		13.6 Bias and Variance of Predictions
		13.7 The Task of Finding the Best Model
		13.8 Finding the Best Model by Best Fit and Penalty: The BIC
		13.9 Finding the Best Model by Training and Test Samples
		13.10 Finding the Best Model by Cross-Validation
		13.A3 CASE STUDY – Predicting Used Car Value with Linear Regressions
		13.11 External Validity and Stable Patterns
		13.A4 CASE STUDY – Predicting Used Car Value with Linear Regressions
		13.12 Machine Learning and the Role of Algorithms
		13.13 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
	14 Model Building for Prediction
		14.1 Steps of Prediction
		14.2 Sample Design
		14.3 Label Engineering and Predicting Log y
		14.A1 CASE STUDY – Predicting Used Car Value: Log Prices
		14.4 Feature Engineering: Dealing with Missing Values
		14.5 Feature Engineering: What x Variables to Have and in What Functional Form
		14.B1 CASE STUDY – Predicting Airbnb Apartment Prices: Selecting a Regression Model
		14.6 We Can’t Try Out All Possible Models
		14.7 Evaluating the Prediction Using a Holdout Set
		14.B2 CASE STUDY – Predicting Airbnb Apartment Prices: Selecting a Regression Model
		14.8 Selecting Variables in Regressions by LASSO
		14.B3 CASE STUDY – Predicting Airbnb Apartment Prices: Selecting a Regression Model
		14.9 Diagnostics
		14.B4 CASE STUDY – Predicting Airbnb Apartment Prices: Selecting a Regression Model
		14.10 Prediction with Big Data
		14.11 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		14.U1 Under the Hood: Text Parsing
		14.U2 Under the Hood: Log Correction
	15 Regression Trees
		15.1 The Case for Regression Trees
		15.2 Regression Tree Basics
		15.3 Measuring Fit and Stopping Rules
		15.A1 CASE STUDY – Predicting Used Car Value with a Regression Tree
		15.4 Regression Tree with Multiple Predictor Variables
		15.5 Pruning a Regression Tree
		15.6 A Regression Tree is a Non-parametric Regression
		15.A2 CASE STUDY – Predicting Used Car Value with a Regression Tree
		15.7 Variable Importance
		15.8 Pros and Cons of Using a Regression Tree for Prediction
		15.A3 CASE STUDY – Predicting Used Car Value with a Regression Tree
		15.9 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
	16 Random Forest and Boosting
		16.1 From a Tree to a Forest: Ensemble Methods
		16.2 Random Forest
		16.3 The Practice of Prediction with Random Forest
		16.A1 CASE STUDY – Predicting Airbnb Apartment Prices with Random Forest
		16.4 Diagnostics: The Variable Importance Plot
		16.5 Diagnostics: The Partial Dependence Plot
		16.6 Diagnostics: Fit in Various Subsets
		16.A2 CASE STUDY – Predicting Airbnb Apartment Prices with Random Forest
		16.7 An Introduction to Boosting and the GBM Model
		16.A3 CASE STUDY – Predicting Airbnb Apartment Prices with Random Forest
		16.8 A Review of Different Approaches to Predict a Quantitative y
		16.9 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
	17 Probability Prediction and Classification
		17.1 Predicting a Binary y: Probability Prediction and Classification
		17.A1 CASE STUDY – Predicting Firm Exit: Probability and Classification
		17.2 The Practice of Predicting Probabilities
		17.A2 CASE STUDY – Predicting Firm Exit: Probability and Classification
		17.3 Classification and the Confusion Table
		17.4 Illustrating the Trade-Off between Different Classification Thresholds: The ROC Curve
		17.A3 CASE STUDY – Predicting Firm Exit: Probability and Classification
		17.5 Loss Function and Finding the Optimal Classification Threshold
		17.A4 CASE STUDY – Predicting Firm Exit: Probability and Classification
		17.6 Probability Prediction and Classification with Random Forest
		17.A5 CASE STUDY – Predicting Firm Exit: Probability and Classification
		17.7 Class Imbalance
		17.8 The Process of Prediction with a Binary Target Variable
		17.9 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		17.U1 Under the Hood: The Gini Node Impurity Measure and MSE
		17.U2 Under the Hood: On the Method of Finding an Optimal Threshold
	18 Forecasting from Time Series Data
		18.1 Forecasting: Prediction Using Time Series Data
		18.2 Holdout, Training, and Test Samples in Time Series Data
		18.3 Long-Horizon Forecasting: Seasonality and Predictable Events
		18.4 Long-Horizon Forecasting: Trends
		18.A1 CASE STUDY – Forecasting Daily Ticket Volumes for a Swimming Pool
		18.5 Forecasting for a Short Horizon Using the Patterns of Serial Correlation
		18.6 Modeling Serial Correlation: AR(1)
		18.7 Modeling Serial Correlation: ARIMA
		18.B1 CASE STUDY – Forecasting a Home Price Index
		18.8 VAR: Vector Autoregressions
		18.B2 CASE STUDY – Forecasting a Home Price Index
		18.9 External Validity of Forecasts
		18.B3 CASE STUDY – Forecasting a Home Price Index
		18.10 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		18.U1 Under the Hood: Details of the ARIMA Model
		18.U2 Under the Hood: Auto-Arima
IV Causal analysis
	19 A Framework for Causal Analysis
		19.1 Intervention, Treatment, Subjects, Outcomes
		19.2 Potential Outcomes
		19.3 The Individual Treatment Effect
		19.4 Heterogeneous Treatment Effects
		19.5 ATE: The Average Treatment Effect
		19.6 Average Effects in Subgroups and ATET
		19.7 Quantitative Causal Variables
		19.A1 CASE STUDY – Food and Health
		19.8 Ceteris Paribus: Other Things Being the Same
		19.9 Causal Maps
		19.10 Comparing Different Observations to Uncover Average Effects
		19.11 Random Assignment
		19.12 Sources of Variation in the Causal Variable
		19.A2 CASE STUDY – Food and Health
		19.13 Experimenting versus Conditioning
		19.14 Confounders in Observational Data
		19.15 From Latent Variables to Measured Variables
		19.16 Bad Conditioners: Variables Not to Condition On
		19.A3 CASE STUDY – Food and Health
		19.17 External Validity, Internal Validity
		19.18 Constructive Skepticism
		19.19 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
	20 Designing and Analyzing Experiments
		20.1 Randomized Experiments and Potential Outcomes
		20.2 Field Experiments, A/B Testing, Survey Experiments
		20.A1 CASE STUDY – Working from Home and Employee Performance
		20.B1 CASE STUDY – Fine Tuning Social Media Advertising
		20.3 The Experimental Setup: Definitions
		20.4 Random Assignment in Practice
		20.5 Number of Subjects and Proportion Treated
		20.6 Random Assignment and Covariate Balance
		20.A2 CASE STUDY – Working from Home and Employee Performance
		20.7 Imperfect Compliance and Intent-to-Treat
		20.A3 CASE STUDY – Working from Home and Employee Performance
		20.8 Estimation and Statistical Inference
		20.B2 CASE STUDY – Fine Tuning Social Media Advertising
		20.9 Including Covariates in a Regression
		20.A4 CASE STUDY – Working from Home and Employee Performance
		20.10 Spillovers
		20.11 Additional Threats to Internal Validity
		20.A5 CASE STUDY – Working from Home and Employee Performance
		20.12 External Validity, and How to Use the Results in Decision Making
		20.A6 CASE STUDY – Working from Home and Employee Performance
		20.13 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		20.U1 Under the Hood: LATE: The Local Average Treatment Effect
		20.U2 Under the Hood: The Formula for Sample Size Calculation
	21 Regression and Matching with Observational Data
		21.1 Thought Experiments
		21.A1 CASE STUDY – Founder/Family Ownership and Quality of Management
		21.2 Variables to Condition on, Variables Not to Condition On
		21.A2 CASE STUDY – Founder/Family Ownership and Quality of Management
		21.3 Conditioning on Confounders by Regression
		21.4 Selection of Variables and Functional Form in a Regression for Causal Analysis
		21.A3 CASE STUDY – Founder/Family Ownership and Quality of Management
		21.5 Matching
		21.6 Common Support
		21.7 Matching on the Propensity Score
		21.A4 CASE STUDY – Founder/Family Ownership and Quality of Management
		21.8 Comparing Linear Regression and Matching
		21.A5 CASE STUDY – Founder/Family Ownership and Quality of Management
		21.9 Instrumental Variables
		21.10 Regression-Discontinuity
		21.11 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
		21.U1 Under the Hood: Unobserved Heterogeneity and Endogenous x in a Regression
		21.U2 Under the hood: LATE is IV
	22 Difference-in-Differences
		22.1 Conditioning on Pre-intervention Outcomes
		22.2 Basic Difference-in-Differences Analysis: Comparing Average Changes
		22.A1 CASE STUDY – How Does a Merger between Airlines Affect Prices?
		22.3 The Parallel Trends Assumption
		22.A2 CASE STUDY – How Does a Merger between Airlines Affect Prices?
		22.4 Conditioning on Additional Confounders in Diff-in-Diffs Regressions
		22.A3 CASE STUDY – How Does a Merger between Airlines Affect Prices?
		22.5 Quantitative Causal Variable
		22.A4 CASE STUDY – How Does a Merger between Airlines Affect Prices?
		22.6 Difference-in-Differences with Pooled Cross-Sections
		22.A5 CASE STUDY – How Does a Merger between Airlines Affect Prices?
		22.7 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
	23 Methods for Panel Data
		23.1 Multiple Time Periods Can Be Helpful
		23.2 Estimating Effects Using Observational Time Series
		23.3 Lags to Estimate the Time Path of Effects
		23.4 Leads to Examine Pre-trends and Reverse Effects
		23.5 Pooled Time Series to Estimate the Effect for One Unit
		23.A1 CASE STUDY – Import Demand and Industrial Production
		23.6 Panel Regression with Fixed Effects
		23.7 Aggregate Trend
		23.B1 CASE STUDY – Immunization against Measles and Saving Children
		23.8 Clustered Standard Errors
		23.9 Panel Regression in First Differences
		23.10 Lags and Leads in FD Panel Regressions
		23.B2 CASE STUDY – Immunization against Measles and Saving Children
		23.11 Aggregate Trend and Individual Trends in FD Models
		23.B3 CASE STUDY – Immunization against Measles and Saving Children
		23.12 Panel Regressions and Causality
		23.13 First Differences or Fixed Effects?
		23.14 Dealing with Unbalanced Panels
		23.15 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
	24 Appropriate Control Groups for Panel Data
		24.1 When and Why to Select a Control Group in xt Panel Data
		24.2 Comparative Case Studies
		24.3 The Synthetic Control Method
		24.A1 CASE STUDY – Estimating the Effect of the 2010 Haiti Earthquake on GDP
		24.4 Event Studies
		24.B1 CASE STUDY – Estimating the Impact of Replacing Football Team Managers
		24.5 Selecting a Control Group in Event Studies
		24.B2 CASE STUDY – Estimating the Impact of Replacing Football Team Managers
		24.6 Main Takeaways
		Practice Questions
		Data Exercises
		References and Further Reading
References
Index