**Environmental Econometrics Using Stata**

81,600원

Authors: Christopher F. Baum and Stan Hurn Publisher: Stata Press Copyright: 2021 ISBN-13: 978-1-59718-355-0 Pages: 416; paperback

*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.

Christopher 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 and Financial Econometric Modeling.*

1.1.2 Nonlinearity

1.1.3 Structural breaks and nonstationarity

1.1.4 Time-carrying volatility

Types of data

2.2 Linear regression and OLS estimation

2.3 Interpreting and assessing the regression model

Tests of significance

2.3.2 Residual diagnostics

Homoskedasticity

Serial independence

Normality

3.2 Properties of estimators

Asymptotic normality

Asymptotic efficiency

3.4 Hypothesis testing

Wald test

LM test

3.6 Testing for exogeneity

4.2 Specifying and fitting dynamic time-series models

Moving-average models

ARMA 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

Vector moving-average form

Orthogonalized impulses

5.4.3 Forecast-error variance decomposition

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

6.4.2 Elliott–Rothenberg–Stock DFGLS test

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

7.6 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

8.5 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

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

12.4 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

13.2 The spatial weighting matrix

Distance weights

Contiguity weights

13.2.2 Construction

13.4 Spatial models

Spatial error model

Spatial error model

13.7 Model selection

14.2 The data

14.3 Binary dependent variables

14.3.2 Binomial logit and probit models

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

A.1.3 Editing Stata do- and ado-files

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

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

Time-series operators

A.6 Circular variables