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Applied Econometric Time Series, 3rd Edition

Applied Econometric Time Series, 3rd Edition

ISBN: 978-0-470-50539-7

Nov 2009

544 pages

Description

Learn to master time-series analysis efficiently and effectively with Applied Econometric Time Series.  Authored by Dr. Walter Enders, Professor and Lee Bidgood Chair of Economics and Finance, this classic text demonstrates modern techniques for developing models capable of forecasting, interpreting, and testing hypotheses concerning economic data.

Introducing the field’s core concepts and recent developments, the 3rd Edition continues to bring clarity, accessibility, and relevance to time-series econometrics.  Its coverage includes parameter instability and structural breaks, out-of-sample forecasting methods, multivariate GARCH models, the most recent developments in unit root tests and cointegration tests, and real-world implications in areas such as macroeconomics and transnational terrorism.

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PREFACE.

ABOUT THE AUTHOR.

Chapter DIFFERENCE EQUATIONS.

1 Time-Series Models.

2 Difference Equations and Their Solutions.

3 Solution by Iteration.

4 An Alternative Solution Methodology.

5 The Cobweb Model.

6 Solving Homogeneous Difference Equations.

7 Finding Particular Solutions for Deterministic Processes.

8 The Method of Undetermined Coefficients.

9 Lag Operators.

Summary and Conclusions.

Questions and Exercises.

Endnotes.

Appendix 1 Imaginary Roots and de Moivre’s Theorem.

Appendix 2 Characteristic Roots in Higher-Order Equations.

Chapter 2 STATIONARY TIME-SERIES MODELS.

1 Stochastic Difference Equation Models.

2 ARMA Models.

3 Stationarity.

4 Stationarity Restrictions for an ARMA(p, q) Model.

5 The Autocorrelation Function.

6 The Partial Autocorrelation Function.

7 Sample Autocorrelations of Stationary Series.

8 Box–Jenkins Model Selection.

9 Properties of Forecasts.

10 A Model of the Interest Rate Spread.

11 Seasonality.

12 Parameter Instability and Structural Change.

Summary and Conclusions.

Questions and Exercises.

Endnotes.

Appendix 1 Estimation of an MA(1) Process.

Appendix 2 Model Selection Criteria.

Chapter 3 MODELING VOLATILITY.

1 Economic Time Series The Stylized Facts.

2 ARCH Processes.

3 ARCH and GARCH Estimates of Inflation.

4 Two Examples of GARCH Models.

5 A GARCH Model of Risk.

6 The ARCH-M Model.

7 Additional Properties of GARCH Processes.

8 Maximum Likelihood Estimation of GARCH Models.

9 Other Models of Conditional Variance.

10 Estimating the NYSE International 100 Index.

11 Multivariate GARCH.

Summary and Conclusions.

Questions and Exercises.

Endnotes.

Appendix 1 Multivariate GARCH Models.

Chapter 4 MODELS WITH TREND.

1 Deterministic and Stochastic Trends.

2 Removing the Trend.

3 Unit Roots and Regression Residuals.

4 The Monte Carlo Method.

5 Dickey–Fuller Tests.

6 Examples of the ADF Test.

7 Extensions of the Dickey-Fuller Test.

8 Structural Change.

9 Power and the Deterministic Regressors.

10 Tests with More Power.

11 Panel Unit Root Tests.

12 Trends and Univariate Decompositions.

Summary and Conclusions.

Questions and Exercises.

Endnotes.

Appendix 1 The Bootstrap.

Chapter 5 MULTIEQUATION TIME-SERIES MODELS.

1 Intervention Analysis.

2 Transfer Function Models.

3 Estimating a Transfer Function.

4 Limits to Structural Multivariate Estimation.

5 Introduction to VAR Analysis.

6 Estimation and Identification.

7 The Impulse Response Function.

8 Testing Hypothesis.

9 Example of a Simple VAR Terrorism and Tourism in Spain.

10 Structural VARs.

11 Examples of Structural Decompositions.

12 The Blanchard and Quah Decomposition.

13 Decomposing Real and Nominal Exchange Rate Movements An Example.

Summary and Conclusions.

Questions and Exercises.

Endnotes.

Chapter 6 COINTEGRATION AND ERROR-CORRECTION MODELS.

1 Linear Combinations of Integrated Variables.

2 Cointegration and Common Trends.

3 Cointegration and Error Correction.

4 Testing for Cointegration The Engle–Granger Methodology.

5 Illustrating the Engle-Granger Methodology.

6 Cointegration and Purchasing-Power Parity.

7 Characteristic Roots, Rank, and Cointegration.

8 Hypothesis Testing.

9 Illustrating the Johansen Methodology.

10 Error-Correction and ADL Tests.

11 Comparing the Three Methods.

Summary and Conclusions.

Questions and Exercises.

Endnotes.

Appendix 1 Characteristic Roots Stability and Rank.

Appendix 2 Inference on a Cointegrating Vector.

Chapter 7 NONLINEAR TIME-SERIES MODELS.

1 Linear Versus Nonlinear Adjustment.

2 Simple Extensions of the ARMA Model.

3 Regime Switching Models.

4 Testing For Nonlinearity.

5 Estimates of Regime Switching Models.

6 Generalized Impulse Responses and Forecasting.

7 Unit Roots and Nonlinearity.

Summary and Conclusions.

Questions and Exercises.

Endnotes.

STATISTICAL TABLES.

A. Empirical Cumulative Distributions of the τ.

B. Empirical Distribution of Φ.

C. Critical Values for the Engle-Granger Cointegration Test.

D. Residual Based Cointegration Test with I(1) and I(2) Variables.

E. Empirical Distributions of the λmax and λtrace Statistics.

F. Critical Values for β1 = 0 in the Error-correction Model.

G. Critical Values for Threshold Unit Roots.

REFERENCES.

SUBJECT INDEX.

  • NEW discussion of parameter instability and structural breaks including tests for endogenous breaks.
  • NEW coverage developments in cointegration tests including the error-correction and ADL tests.
  • Coverage on new developments in unit root tests including the LM tests and the DF-GLS tests.
  • Improved discussion of out-of-sample forecasting methods including forecast comparisons with nested models.
  • Expanded coverage of multivariate GARCH models to include VECH, BEK and DCC specifications.
  • NEW developments in cointegration tests including the error-correction and ADL tests.
  • Many updated statistical examples using real-world data.
  • Unparalleled Coverage of Nonlinear Time-series Models:  An entire chapter is devoted to the estimation and testing of various nonlinear time-series models. Readers are taught to perform multiple-step-ahead out-of-sample forecasts, obtain the generalized impulse response function, and use regression-based techniques.
  • Real-world data and detailed examples from macroeconomics, agricultural economics, international finance, transnational terrorism, and current international finance literature.
  • Step-by-step approach to time-series estimation and procedural stages.
  • A comprehensive, full chapter on ARCH and GARCH models
  • Learn by doing:  Exposure to procedures appearing in econometric software packages, such as EVIEWS, MICROSIT, PC-GIVE, RATS, SAS, SHAZAM, and STATA, and assistance in matrix programming (MATLAB and GAUSS).
  • Numerous illustrations of key concepts.
  • Substantive number of questions and empirical exercises that enable students to practice the techniques covered in the text.
  • Continued emphasis on forecasting and on difference equations as the foundation of all time-series models.
  • Data sets available on the Book Companion Website.