Applied Econometric Times Series / Edition 3

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Overview

Assuming only a basic understanding of multiple regression analysis, this classic introduction to time-series analysis shows how to develop models capable of forecasting, interpreting, and testing hypotheses concerning economic data using modern techniques. Numerous real-world examples from fields ranging from agricultural economics to transnational terrorism further illustrate the various techniques.

This new edition reflects both sound structure and recent advances in time-series econometrics, such as out-of-sample forecasting techniques, nonlinear time-series models, Monte Carlo analysis, and bootstrapping.

Features:

New discussion of parameter instability and structural breaks including tests for endogenous breaks.

New coverage of developments in cointegration tests and in unit root tests.

Improved discussions on out-of-sample forecasting methods and multivariate Garch models.

Numerous illustrations of key concepts and detailed examples using real-world data.

Step-by-step approach to time-series estimation.

Additional questions and empirical exercises that enable students to practice the techniques covered in the text. Data sets are available on the text's companion Web site.

Emphasizes difference equations as the foundation of all time-series models.

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Editorial Reviews

Booknews
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 (booknews.com)
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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: New Edition
  • Edition number: 3
  • Pages: 544
  • Sales rank: 497,141
  • 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 Economics and Finance at the University of Alabama. He received his doctorate in economics from Columbia University. His current research focuses on the development and application of time-series models to areas in economics and finance, including documenting the cyclic and shifting nature of terrorist attacks in response to defensive counteractions. Dr. Enders has published numerous research articles in such journals as the Review of Economics and Statistics, Quarterly Journal of Economics, and the Journal of International Economics. He has also published articles in the American Economic Review, the Journal of Business and Economic Statistics, and the American Political Science Review.

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Table of Contents

Ch. 1 Difference Equations 1
1 Time-Series Models 2
2 Difference Equations and Their Solutions 7
3 Solution by Iteration 10
4 An Alternative Solution Methodology 16
5 The Cobweb Model 20
6 Solving Homogeneous Difference Equations 25
7 Finding Particular Solutions for Determimstic Processes 35
8 The Method of Undetermined Coefficients 38
9 Lag Operators 45
10 Forward-Versus Backward-Looking Solutions 48
Ch. 2 Stationary Time-Series Models 63
1 Stochastic Difference Equation Models 63
2 ARMA Models 67
3 Stationarity 68
4 Stationarity Restrictions for an ARMA(p, q) Model 72
5 The Autocorrelation Function 78
6 The Partial Autocorrelation Function 82
7 Sample Autocorrelations of Stationary Series 86
8 Box-Jenkins Model Selection 95
9 The Forecast Function 99
10 A Model of the WPI 106
11 Seasonality 111
Ch. 3 Modeling Economic Time Series: Trends and Volatility 135
1 Economic Time Series: The Stylized Facts 135
2 ARCH Processes 139
3 ARCH and GARCH Estimates of Inflation 149
4 Estimating a GARCH Model of the WPI: An Example 152
5 A GARCH Model of Risk 156
6 The ARCH-M Model 158
7 Maximum Likelihood Estimation of GARCH and ARCH-M Models 162
8 Deterministic and Stochastic Trends 166
9 Removing the Trend 176
10 Are There Business Cycles? 181
11 Stochastic Trends and Univariate Decompositions 185
Ch. 4 Testing for Trends and Unit Roots 211
1 Unit Root Processes 212
2 Dickey-Fuller Tests 221
3 Extensions of the Dickey-Fuller Tests 225
4 Examples of the Augmented Dickey-Fuller Test 233
5 Phillips-Perron Tests 239
6 Structural Change 243
7 Problems in Testing for Unit Roots 251
Ch. 5 Multiequation Time-Series Models 269
1 Intervention Analysis 270
2 Transfer Function Models 277
3 Estimating a Transfer Function 286
4 Limits to Structural Multivariate Estimation 291
5 Introduction to VAR Analysis 294
6 Estimation and Identification 300
7 The Impulse Response Function 305
8 Hypothesis Testing 312
9 Example of a Simple VAR: Terrorism and Tourism in Spain 316
10 Structural VARs 320
11 Examples of Structural Decompositions 324
12 The Blanchard and Quah Decomposition 331
13 Decomposing Real and Nominal Exchange Rate Movements: An Example 338
Ch. 6 Cointegration and Error-Correction Models 355
1 Linear Combinations of Integrated Variables 356
2 Cointegration and Common Trends 363
3 Cointegration and Error Correction 365
4 Testing for Cointegration: The Engle-Granger Methodology 373
5 Illustrating the Engle-Granger Methodology 377
6 Cointegration and Purchasing-Power Parity 381
7 Characteristic Roots, Rank, and Cointegration 385
8 Hypothesis Testing in a Cointegration Framework 393
9 Illustrating the Johansen Methodology 396
10 Generalized Purchasing-Power Parity 400
Statistical tables A. Empirical Distributions of the [tau] Statistics 419
Statistical tables B. Empirical Distributions of the [phi] Statistics 420
Statistical tables C. Empirical Distributions of the [lambda[subscript max]] and [lambda[subscript trace]] Statistics 421
References 423
Author Index 427
Subject Index 429
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