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
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
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
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
