*Environmental Econometrics Using Stata* is written for applied researchers that want to understand the basic theory of modern statistical methods and how to use them. It is also perfectly suited for teaching. Each chapter is motivated with real data and ends with a set of exercises. The book is also inherently interdisciplinary. The questions posed by environmental issues are relevant to researchers in the physical sciences, economics, sociology, political science, and public health, among other fields.

Each chapter begins with a real dataset and research question. The authors then provide a gentle introduction to the statistical method and demonstrate how to use it to answer the research question. The authors discuss the assumptions about the data and the model, demonstrate the Stata commands used to fit the model and check the model assumptions, and interpret the results. The workflow of the book mimics the workflow that would be required to present your results to an academic audience.

The book is of interest not only for its exposition of the topics but also for its breadth. The book presents estimators for continuous, binary, and ordered outcomes in cross-sectional data; univariate and multivariate time series with stationary and nonstationary data; linear and dynamic panel data; and spatial models and fractional integration. The range of methods is not arbitrary; it is a function of the questions posed by environmental data and reflects the challenges faced by researchers from different disciplines to answer a wide range of questions using modern statistical methods.

© Copyright 1996–2023 StataCorp LLC

**List of figures**

**List of tables**

**Preface**(PDF)

**Acknowledgments**

**Notation and typography**

1.1 Features of the data

1.1.2 Nonlinearity

1.1.3 Structural breaks and nonstationarity

1.1.4 Time-carrying volatility

2.2 Linear regression and OLS estimation

2.3 Interpreting and assessing the regression model

Serial independence

Normality

2.4 Estimating standard errors

3.2 Properties of estimators

Asymptotic normality

Asymptotic efficiency

3.3 Maximum likelihood and the linear model

3.4 Hypothesis testing

Wald test

LM test

3.5 Method-of-moments estimators and the linear model

3.6 Testing for exogeneity

4.2 Specifying and fitting dynamic time-series models

Moving-average models

ARMA models

4.3 Exploring the properties of dynamic models

4.4 ARMA models for load-weighted electricity price

4.5 Seasonal ARMA models

_{2}emissions and growth

5.2 The VARMA model

5.3 The VAR model

5.4 Analyzing the dynamics of a VAR

5.4.2 Impulse–responses

Orthogonalized impulses

5.5 SVARs

5.5.2 Long-run restrictions

_{2}emissions

6.2 Unit roots

6.3 First-generation unit-root tests

6.3.2 Phillips–Perron tests

6.4 Second-generation unit-root tests

6.4.2 Elliott–Rothenberg–Stock DFGLS test

6.5 Structural breaks

6.5.2 Single-break unit-root tests

6.5.3 Double-break unit-roots tests

7.2 Illustrating equilibrium relationships

7.3 The VECM

7.4 Fitting VECMs

7.4.2 System estimation

7.5 Testing for cointegration

7.6 Cointegration and structural breaks

8.2 Introductory terminology

8.3 Recursive forecasting in time-series models

8.3.2 Multiple-equation forecasts

8.3.3 Properties of recursive forecasts

8.4 Forecast evaluation

8.5 Daily forecasts of wind speed for Santiago

8.6 Forecasting with logarithmic dependent variables

8.6.2 Generalized linear models

9.2 The Kalman filter

9.3 Vector autoregressive moving-average models in state-space form

9.4 Unobserved component time-series models

9.4.2 Seasonals

9.4.3 Cycles

9.5 A bivariate model of sea level and global temperature

10.2 Testing

10.3 Bilinear time-series models

10.4 Threshold autoregressive models

10.5 Smooth transition models

10.6 Markov switching models

11.2 The generalized autoregressive conditional heteroskedasticity model

11.3 Alternative distributional assumptions

11.4 Asymmetries

11.5 Motivating multivariate volatility models

11.6 Multivariate volatility models

11.6.2 The dynamic conditional correlation model

12.2 Data organization

12.2.2 Reshaping the data

12.3 The pooled model

12.4 Fixed effects and random effects

12.4.2 Two-way FE

12.4.3 REs

12.4.4 The Hausman test in a panel context

12.4.5 Correlated RE

12.5 Dynamic panel-data models

13.2 The spatial weighting matrix

Contiguity weights

13.3 Exploratory data analysis

13.4 Spatial models

Spatial error model

13.5 Fitting spatial models by maximum likelihood

Spatial error model

13.6 Estimating spillover effects

13.7 Model selection

14.2 The data

14.3 Binary dependent variables

14.3.2 Binomial logit and probit models

14.4 Ordered dependent variables

14.5 Censored dependent variables

15.2 Autocorrelations and long memory

15.3 Testing for long memory

15.4 Estimating d in the frequency domain

15.5 Maximum likelihood estimation of the ARFIMA model

15.6 Fractional cointegration

A.1 File management

A.1.2 Organization of do-, ado-, and data files

A.1.3 Editing Stata do- and ado-files

A.2 Basic data management

A.2.2 Getting your data into Stata

The import delimited command

Accessing data stored in spreadsheets

Importing data from other package formats

Missing data handling

Recoding missing values: the mvdecode and mvencode commands

A.3 General programming hints

Observation numbering:_n and _N

The varlist

The numlist

The if exp and in range qualifiers

Local macros

Global macros

Scalars

Matrices

Looping

The generate command

The egen command

Computation for by-groups

A.4 A smorgasbord of important topics

Time-series operators

A.5 Factor variables and operators

A.6 Circular variables

**References**

**Author index** (PDF)

**Subject index** (PDF)

© Copyright 1996–2023 StataCorp LLC