List of tables

List of figures

Preface

Acknowledgments

1 Just enough Stata

1.1 Getting started

1.1.1 Action first, explanation later

1.1.2 Now some explanation

1.1.3 Navigating the interface

1.1.4 The gestalt of Stata

1.1.5 The parts of Stata speech

1.2 All about data

1.3 Looking at data

1.4 Statistics

1.4.1 Basics

1.4.2 Estimation

1.5 Odds and ends

1.6 Making a date

1.6.1 How to look good

1.6.2 Transformers

1.7 Typing dates and date variables

1.8 Looking ahead

2 Just enough statistics

2.1 Random variables and their moments

2.2 Hypothesis tests

2.3 Linear regression

2.3.1 Ordinary least squares

2.3.2 Instrumental variables

2.3.3 FGLS

2.4 Multiple-equation models

2.5 Time series

2.5.1 White noise, autocorrelation, and stationarity

2.5.2 ARMA models

3 Filtering time-series data

3.1 Preparing to analyze a time series

3.1.1 Questions for all types of data

How are the variables defined?

What is the relationship between the data and the phenomenon of interest?

Who compiled the data?

What processes generated the data?

3.1.2 Questions specifically for time-series data

What is the frequency of measurement?

Are the data seasonally adjusted?

Are the data revised?

3.2 The four components of a time series

Trend

Cycle

Seasonal

3.3 Some simple filters

3.3.1 Smoothing a trend

3.3.2 Smoothing a cycle

3.3.3 Smoothing a seasonal pattern

3.3.4 Smoothing real data

3.4 Additional filters

3.4.1 ma: Weighted moving averages

3.4.2 EWMAs

exponential: EWMAs

dexponential: Double-exponential moving averages

3.4.3 Holt–Winters smoothers

hwinters: Holt–Winters smoothers without a seasonal component

shwinters: Holt–Winters smoothers including a seasonal component

3.5 Points to remember

4 A first pass at forecasting

4.1 Forecast fundamentals

4.1.1 Types of forecasts

4.1.2 Measuring the quality of a forecast

4.1.3 Elements of a forecast

4.2 Filters that forecast

4.2.1 Forecasts based on EWMAs

4.2.2 Forecasting a trending series with a seasonal component

4.3 Points to remember

4.4 Looking ahead

5 Autocorrelated disturbances

5.1 Autocorrelation

5.1.1 Example: Mortgage rates

5.2 Regression models with autocorrelated disturbances

5.2.1 First-order autocorrelation

5.2.2 Example: Mortgage rates (cont.)

5.3 Testing for autocorrelation

5.3.1 Other tests

5.4 Estimation with first-order autocorrelated data

5.4.1 Model 1: Strictly exogenous regressors and autocorrelated disturbances

The OLS strategy

The transformation strategy

The FGLS strategy

Comparison of estimates of model

5.4.2 Model 2: A lagged dependent variable and i.i.d. errors

5.4.3 Model 3: A lagged dependent variable with AR(1) errors

The transformation strategy

The IV strategy

5.5 Estimating the mortgage rate equation

5.6 Points to remember

6 Univariate time-series models

6.1 The general linear process

6.2 Lag polynomials: Notation or prestidigitation?

6.3 The ARMA model

6.4 Stationarity and invertibility

6.5 What can ARMA models do?

6.6 Points to remember

6.7 Looking ahead

7 Modeling a real-world time series

7.1 Getting ready to model a time series

7.2 The Box–Jenkins approach

7.3 Specifying an ARMA model

7.3.1 Step 1: Induce stationarity (ARMA becomes ARIMA)

7.3.2 Step 2: Mind your p’s and q’s

7.4 Estimation

7.5 Looking for trouble: Model diagnostic checking

7.5.1 Overfitting

7.5.2 Tests of the residuals

7.6 Forecasting with ARIMA models

7.7 Comparing forecasts

7.8 Points to remember

7.9 What have we learned so far?

7.10 Looking ahead

8 Time-varying volatility

8.1 Examples of time-varying volatility

8.2 ARCH: A model of time-varying volatility

8.3 Extensions to the ARCH model

8.3.1 GARCH: Limiting the order of the model

8.3.2 Other extensions

Asymmetric responses to “news”

Variations in volatility affect the mean of the observable
series

Nonnormal errors

Odds and ends

8.4 Points to remember

9 Models of multiple time series

9.1 Vector autoregressions

9.1.1 Three types of VARs

9.2 A VAR of the U.S. macroeconomy

9.2.1 Using Stata to estimate a reduced-form VAR

9.2.2 Testing a VAR for stationarity

Other tests

9.2.3 Forecasting

Evaluating a VAR forecast

9.3 Who’s on first?

9.3.1 Cross correlations

9.3.2 Summarizing temporal relationships in a VAR

Granger causality

How to impose order

FEVDs

Using Stata to calculate IRFs and FEVDs

9.4 SVARs

9.4.1 Examples of a short-run SVAR

9.4.2 Examples of a long-run SVAR

9.5 Points to remember

9.6 Looking ahead

10 Models of nonstationary time series

10.1 Trends and unit roots

10.2 Testing for unit roots

10.3 Cointegration: Looking for a long-term relationship

10.4 Cointegrating relationships and VECMs

10.4.1 Deterministic components in the VECM

10.5 From intuition to VECM: An example

Step 1: Confirm the unit root

Step 2: Identify the number of lags

Step 3: Identify the number of cointegrating relationships

Step 4: Fit a VECM

Step 5: Test for stability and white-noise residuals

Step 6: Review the model implications for reasonableness

10.6 Points to remember

10.7 Looking ahead

11 Closing observations

11.1 Making sense of it all

11.2 What did we miss?

11.2.1 Advanced time-series topics

11.2.2 Additional Stata time-series features

Data management tools and utilities

Univariate models

Multivariate models

11.3 Farewell

References

Author index

Subject index