Environmental Econometrics Using Stata

List of figures

List of tables

Preface

Acknowledgments

Notation and typography

1 Introduction

• 1.1 Features of the data
• 1.1.1 Periodicity
• 1.1.2 Nonlinearity
• 1.1.3 Structural breaks and nonstationarity
• 1.1.4 Time-carrying volatility
• Types of data

2 Linear regression models

• 2.1 Air pollution in Santiago, Chile
• 2.2 Linear regression and OLS estimation
• 2.3 Interpreting and assessing the regression model
• 2.3.1 Goodness of fit
• Tests of significance
• 2.3.2 Residual diagnostics
• Homoskedasticity
• Serial independence
• Normality
• 2.4 Estimating standard errors

3 Beyond ordinary least squares

• 3.1 Distribution of particulate matter
• 3.2 Properties of estimators
• Consistency
• Asymptotic normality
• Asymptotic efficiency
• 3.3 Maximum likelihood and the linear model
• 3.4 Hypothesis testing
• Likelihood-ratio test
• Wald test
• LM test
• 3.5 Method-of-moments estimators and the linear model
• 3.6 Testing for exogeneity

4 Introducing dynamics

• 4.1 Load-weighted electricity prices
• 4.2 Specifying and fitting dynamic time-series models
• AR 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 Multivariate time-series models

• 5.1 CO2 emissions and growth
• 5.2 The VARMA model
• 5.3 The VAR model
• 5.4 Analyzing the dynamics of a VAR
• 5.4.1 Granger causality testing
• 5.4.2 Impulse–responses
• Vector moving-average form
• Orthogonalized impulses
• 5.4.3 Forecast-error variance decomposition
• 5.5 SVARs
• 5.5.1 Short-run restrictions
• 5.5.2 Long-run restrictions

6 Testing for nonstationarity

• 6.1 Per capita CO2 emissions
• 6.2 Unit roots
• 6.3 First-generation unit-root tests
• 6.3.1 Dickey–Fuller tests
• 6.3.2 Phillips–Perron tests
• 6.4 Second-generation unit-root tests
• 6.4.1 KPSS test
• 6.4.2 Elliott–Rothenberg–Stock DFGLS test
• 6.5 Structural breaks
• 6.5.1 Known breakpoint
• 6.5.2 Single-break unit-root tests
• 6.5.3 Double-break unit-roots tests

7 Modeling nonstationary variables

• 7.1 The crush spread
• 7.2 Illustrating equilibrium relationships
• 7.3 The VECM
• 7.4 Fitting VECMs
• 7.4.1 Single-equation methods
• 7.4.2 System estimation
• 7.5 Testing for cointegration
• 7.6 Cointegration and structural breaks

8 Forecasting

• 8.1 Forecasting wind speed
• 8.2 Introductory terminology
• 8.3 Recursive forecasting in time-series models
• 8.3.1 Single-equation forecasts
• 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
• 8.6.1 Staying in the linear regression framework
• 8.6.2 Generalized linear models

9 Structural time-series models

• 9.1 Sea level and global temperature
• 9.2 The Kalman filter
• 9.3 Vector autoregressive moving-average models in state-space form
• 9.4 Unobserved component time-series models
• 9.4.1 Trends
• 9.4.2 Seasonals
• 9.4.3 Cycles
• 9.5 A bivariate model of sea level and global temperature

10 Nonlinear time-series models

• 10.1 Sunspot data
• 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 Modeling time-varying variance

• 11.1 Evaluating environmental risk
• 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
• 11.6.1 The vech model
• 11.6.2 The dynamic conditional correlation model

12 Longitudinal data models

13 Spatial models

• 13.1 Regulatory compliance
• 13.2 The spatial weighting matrix
• 13.2.1 Specifications
• Distance weights
• Contiguity weights
• 13.2.2 Construction
• 13.3 Exploratory data analysis
• 13.4 Spatial models
• Spatial lag model
• Spatial error model
• 13.5 Fitting spatial models by maximum likelihood
• Spatial lag model
• Spatial error model
• 13.6 Estimating spillover effects
• 13.7 Model selection

14 Discrete dependent variables

• 14.1 Humpback whales
• 14.2 The data
• 14.3 Binary dependent variables
• 14.3.1 Linear probability model
• 14.3.2 Binomial logit and probit models
• 14.4 Ordered dependent variables
• 14.5 Censored dependent variables

15 Fractional integration

• 15.1 Mean sea levels and global temperature
• 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 Using Stata

• A.1 File management
• A.1.1 Locating important directories: adopath
• 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.2.1 Data types
• A.2.2 Getting your data into Stata
• Handling text files
• The import delimited command
• Accessing data stored in spreadsheets
• Importing data from other package formats
• A.2.3 Other data issues
• Protecting the data in memory
• Missing data handling
• Recoding missing values: the mvdecode and mvencode commands
• A.2.4 String-to-numeric conversion and vice versa
• A.3 General programming hints
• Variable names
• 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
• Date and time handling
• Time-series operators
• A.5 Factor variables and operators
• A.6 Circular variables