×

Uh-oh, it looks like your Internet Explorer is out of date.

For a better shopping experience, please upgrade now.

Applied Econometric Times Series / Edition 3
     

Applied Econometric Times Series / Edition 3

4.5 2
by Walter Enders
 

See All Formats & Editions

ISBN-10: 0470505397

ISBN-13: 9780470505397

Pub. Date: 11/02/2009

Publisher: Wiley

Accessible & Modern Techniques for Time-Series Analysis

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

Overview

Accessible & Modern Techniques for Time-Series Analysis

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 example 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 test. Data sets are available on the text’s companion Web site.
  • Emphasizes difference equations as the foundation of all time-series models.

Product Details

ISBN-13:
9780470505397
Publisher:
Wiley
Publication date:
11/02/2009
Series:
Wiley Series in Probability and Statistics Series , #804
Edition description:
Older Edition
Pages:
544
Product dimensions:
6.10(w) x 9.10(h) x 1.00(d)

Table of Contents

Preface v

About The Author viii

Chapter 1 Difference Equations 1

Introduction 1

1 Time-Series Models 1

2 Difference Equations and their Solutions 7

3 Solution by Iteration 10

4 An Alternative Solution Methodology 14

5 The Cobweb Model 18

6 Solving Homogeneous Difference Equations 22

7 Particular Solutions for Deterministic Processes 31

8 The Method of Undetermined Coefficients 33

9 Lag Operators 39

10 Summary 42

Questions and Exercises 43

Endnotes 45

Appendix 1.1 Imaginary Roots and de Moivre's Theorem 45

Appendix 1.2 Characteristic Roots in Higher-Order Equations 47

Chapter 2 Stationary Time-Series Models 49

1 Stochastic Difference Equation Models 49

2 Arma Models 52

3 Stationarity 53

4 Stationarity Restrictions for an Arma (p, q) Model 57

5 The Autocorrelation Function 62

6 The Partial Autocorrelation Function 65

7 Sample Autocorrelations of Stationary Series 69

8 Box-Jenkins Model Selection 78

9 Properties of Forecasts 81

10 A Model of the Interest Rate Spread 89

11 Seasonality 97

12 Parameter Instability and Structural Change 103

13 Summary and Conclusions 110

Questions and Exercises 110

Endnotes 116

Appendix 2.1 Estimation of an MA (1) Process 116

Appendix 2.2 Model Selection Criteria 118

Chapter 3 Modeling Volatility 121

1 Economic Time Series: The Stylized Facts 121

2 Arch Processes 125

3 Arch and Garch Estimates of Inflation 132

4 Two Examples of Garch Models 136

5 A Garch Model of Risk 141

6 The Arch-M Model 143

7 Additional Properties of Garch Processes 146

8 Maximum-Likelihood Estimation of Garch Models 152

9 Other Models of Conditional Variance 154

10 Estimating the NyseInternational 100 Index 158

11 Multivariate Garch 165

12 Summary and Conclusions 170

Questions and Exercises 172

Endnotes 176

Appendix 3.1 Multivariate Garch Models 176

Chapter 4 Models With Trends 181

1 Deterministic and Stochastic Trends 181

2 Removing the Trend 189

3 Unit Roots and Regression Residuals 195

4 The Monte Carlo Method 200

5 Dickey-Fuller Tests 206

6 Examples of the Dickey-Fuller Test 209

7 Extensions of the Dickey-Fuller Test 215

8 Structural Change 227

9 Power and the Deterministic Regressors 234

10 Tests with More Power 239

11 Panel Unit Root Tests 244

12 Trends and Univariate Decompositions 247

13 Summary and Conclusions 257

Questions and Exercises 258

Endnotes 262

Appendix 4.1 The Bootstrap 263

Appendix 4.2 Determination of the Deterministic Regressors 267

Chapter 5 Multiequation Time-Series Models 272

1 Intervention Analysis 273

2 Transfer Function Models 280

3 Estimating a Transfer Function 290

4 Limits to Structural Multivariate Estimation 294

5 Introduction to Var Analysis 297

6 Estimation and Identification 303

7 The Impulse Response Function 307

8 Testing Hypotheses 315

9 Example of a Simple Var: Terrorism and Tourism in Spain 321

10 Structural Vars 325

11 Examples of Structural Decompositions 329

12 The Blanchard-Quah Decomposition 338

13 Decomposing Real and Nominal Exchange Rates: An Example 344

14 Summary and Conclusions 347

Questions and Exercises 349

Endnotes 354

Chapter 6 Cointegration and Error-Correction Models 356

1 Linear Combinations of Integrated Variables 357

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 3827

7 Characteristic Roots, Rank, and Cointegration 385

8 Hypothesis Testing 393

9 Illustrating the Johansen Methodology 401

10 Error-Correction and ADL Tests 405

11 Comparing the Three Methods 409

Summary and Conclusions 412

Questions and Exercises 413

Endnotes 418

Appendix 6.1 Characteristic Roots, Stability, and Rank 419

Appendix 6.2 Inference on a Cointegrating Vector 425

Chapter 7 Nonlinear Time-Series Models 428

1 Linear Versus Nonlinear Adjustment 428

2 Simple Extensions of the ARMA Model 431

3 Pretesting in Nonlinearity 434

4 Threshold Autoregressive Models 439

5 Extensions of the Tar Model 445

6 Three Threshold Models 451

7 Smooth-Transition Models 457

8 Other Regime Switching Models 462

9 Estimates of Star Models 466

10 Generalized Impulse Responses and Forecasting 470

11 Unit Roots and Nonlinearity 477

12 Summary and Conclusions 482

Questions and Exercises 483

Endnotes 486

Statistical Tables 488

A Empirical Cumulative Distribution of the ? 488

B Empirical Distribution of ø 489

C Critical Values for the Engle-Granger Cointegration Test 490

D Residual-Based Cointegration Test with I(1) and I(2) Variables 491

E Empirical Distributions of the $max and $trace Statistics 492

F Critical Values for β1 = 0 in the Error-correction Model 493

G Critical Values for Threshold Unit Roots 494

References 495

Subject Index 503

Customer Reviews

Average Review:

Post to your social network

     

Most Helpful Customer Reviews

See all customer reviews

Applied Econometric Times Series 0 out of 5 based on 0 ratings. 0 reviews.