Applied Econometric Times Series / Edition 3

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Enders continues to provide business professionals with an accessible introduction to time-series analysis. He clearly shows them how to develop models capable of forecasting, interpreting, and testing hypotheses concerning economic data using the latest techniques. The third edition includes new discussions on parameter instability and structural breaks as well as out-of-sample forecasting methods. New developments in unit root test and cointegration tests are covered. Multivariate GARCH models are also presented. In addition, several statistical examples have been updated with real-world data to help business professionals understand the relevance of the material.

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Editorial Reviews

A review of recent advances in time series analysis, providing a balance between macro and microeconomic application and using examples drawn from agricultural economics, international finance, and transnational terrorism. Emphasizes the importance of difference equations, and assumes some background in multiple regression analysis. Software packages such as RATS, SAS, or SHAZAM are necessary to work through the exercises. A data disk with computer programs accompanies the Instructor's Manual. Annotation c. Book News, Inc., Portland, OR (
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Product Details

  • ISBN-13: 9780470505397
  • Publisher: Wiley
  • Publication date: 11/2/2009
  • Series: Wiley Series in Probability and Statistics Series, #804
  • Edition description: Older Edition
  • Edition number: 3
  • Pages: 544
  • Sales rank: 608,701
  • Product dimensions: 6.10 (w) x 9.10 (h) x 1.00 (d)

Meet the Author

About the Author:

Walter Enders is Professor and Lee Bidgood Chair of Economicsand Finance at the University of Alabama. He received his doctoratein economics from Columbia University. His current research focuseson the development and application of time-series models to areasin economics and finance, including documenting the cyclic andshifting nature of terrorist attacks in response to defensivecounteractions. Dr. Enders has published numerous research articlesin such journals as the Review of Economics and Statistics,Quarterly Journal of Economics, and the Journal ofInternational Economics. He has also published articles in theAmerican Economic Review, the Journal of Business andEconomic Statistics, and the American Political ScienceReview.

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Table of Contents




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.


Appendix 1 Imaginary Roots and de Moivre’s Theorem.

Appendix 2 Characteristic Roots in Higher-Order Equations.


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.


Appendix 1 Estimation of an MA(1) Process.

Appendix 2 Model Selection Criteria.


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.


Appendix 1 Multivariate GARCH Models.


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.


Appendix 1 The Bootstrap.


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 AnExample.

Summary and Conclusions.

Questions and Exercises.



1 Linear Combinations of Integrated Variables.

2 Cointegration and Common Trends.

3 Cointegration and Error Correction.

4 Testing for Cointegration The Engle–GrangerMethodology.

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.


Appendix 1 Characteristic Roots Stability and Rank.

Appendix 2 Inference on a Cointegrating Vector.


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.



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) andI(2) Variables.

E. Empirical Distributions of the λmax andλtrace Statistics.

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

G. Critical Values for Threshold Unit Roots.



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