Environmental Econometrics Using Stata |
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Preface
Author index Subject index Errata Download the datasets used in this book (from stata-press.com) Review from the Stata Journal
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Comment from the Stata technical groupEnvironmental 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 an 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. |
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About the authorsChristopher F. Baum is a professor of economics and social work at Boston College. Baum has taught econometrics for many years, using Stata extensively in academic and nonacademic settings. He has over 40 years of experience with computer programming and has authored or coauthored several widely used Stata commands. He is the author of An Introduction to Modern Econometrics Using Stata and An Introduction to Stata Programming, Second Edition. He is an associate editor of the Stata Journal and maintains the Statistical Software Components Archive of community-contributed Stata materials. Stan Hurn is a professor of econometrics at Queensland University of Technology. He held previous positions at the University of Glasgow and at Brasenose College, Oxford. He is a fellow of the Society for Financial Econometrics. His main research interests are in the field of time-series econometrics, and he has been published widely in leading international journals. He is also the coauthor of Econometric Modelling with Time Series: Specification, Estimation and Testing i> and Financial Econometric Modeling. |
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Table of contentsView table of contents >> List of figures
List of tables
Preface (PDF)
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
2.4 Estimating standard errors Tests of significance 2.3.2 Residual diagnostics Homoskedasticity Serial independence Normality 3 Beyond ordinary least squares
3.1 Distribution of particulate matter
3.2 Properties of estimators
Consistency
3.3 Maximum likelihood and the linear model Asymptotic normality Asymptotic efficiency 3.4 Hypothesis testing
Likelihood-ratio test
3.5 Method-of-moments estimators and the linear model Wald test LM test 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
4.3 Exploring the properties of dynamic models Moving-average models ARMA 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
5.5 SVARs 5.4.2 Impulse–responses Vector moving-average form Orthogonalized impulses 5.4.3 Forecast-error variance decomposition
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
6.4 Second-generation unit-root tests 6.3.2 Phillips–Perron tests
6.4.1 KPSS test
6.5 Structural breaks 6.4.2 Elliott–Rothenberg–Stock DFGLS test
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
7.5 Testing for cointegration 7.4.2 System estimation 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
8.4 Forecast evaluation 8.3.2 Multiple-equation forecasts 8.3.3 Properties of recursive forecasts 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
9.5 A bivariate model of sea level and global temperature 9.4.2 Seasonals 9.4.3 Cycles 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
12.1 The pollution haven hypothesis
12.2 Data organization
12.2.1 Wide and long forms of panel data
12.3 The pooled model 12.2.2 Reshaping the data 12.4 Fixed effects and random effects
12.4.1 Individual FEs
12.5 Dynamic panel-data models 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 13 Spatial models
13.1 Regulatory compliance
13.2 The spatial weighting matrix
13.2.1 Specifications
13.3 Exploratory data analysis Distance weights Contiguity weights 13.2.2 Construction 13.4 Spatial models
Spatial lag model
13.5 Fitting spatial models by maximum likelihood Spatial error model
Spatial lag model
13.6 Estimating spillover effects Spatial error model 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
14.4 Ordered dependent variables 14.3.2 Binomial logit and probit models 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.2 Basic data management A.1.2 Organization of do-, ado-, and data files A.1.3 Editing Stata do- and ado-files
A.2.1 Data types
A.3 General programming hints 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
Variable names
A.4 A smorgasbord of important topics 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
Date and time handling
A.5 Factor variables and operators Time-series operators A.6 Circular variables References
Author index (PDF)
Subject index (PDF)
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