Forecasting for economics and business:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; New York
Routledge
2016
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXII, 490 Seiten Diagramme |
| ISBN: | 9780131474932 0131474936 |
Internformat
MARC
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| 245 | 1 | 0 | |a Forecasting for economics and business |c Gloria González-Rivera |
| 264 | 1 | |a London ; New York |b Routledge |c 2016 | |
| 300 | |a XXII, 490 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 505 | 8 | |a Includes bibliographical references (p. 481-483) and index | |
| 650 | 4 | |a Economic forecasting | |
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Datensatz im Suchindex
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| adam_text | Contents
Preface
MODULE I STATISTICS AND TIME SERIES
CHAPTER 1 Introduction and Context
1.1 What Is Forecasting?
1.1.1 The First Forecaster in History: The Delphi Oracle
1.1.2Examples of Modem Forecasts
1.1.3Definition of Forecasting
1.1.4 Two Types of Forecasts
1.2 Who Are the Users of Forecasts?
1.2.1 Firms
1.2.2 Consumers and Investors
1.2.3 Government
1.3 Becoming Familiar with Economic Time Series:
Features of a Time Series
1.3.1 Trends
1.3.2 Cycles
1.3.3 Seasonality
1.4 Basic Notation and the Objective of the Forecaster
1.4.1 Basic Notation
1.4.2 The Forecaster’s Objective
1.5 A Road Map for This Forecasting Book
1.6 Resources
Key Words
Exercises
CHAPTER 2 Review of the Linear Regression Model
2.1 Conditional Density and Conditional Moments
2.2 Linear Regression Model
X
Contents
2.3 Estimation: Ordinary Least Squares 29
2.3. / R-squared and Adjusted R-squared 32
2.3.2 Linearity and OLS 33
2.3.3 Assumptions of OLS: The Gauss-Markov Theorem 35
2.3.4 An Example: House Prices and Interest Rates 38
2.4 Hypothesis Testing in a Regression Model 41
2.4.1 The t-ratio 41
2.4.2 The F-test 44
Key Words 46
Appendix 47
Exercises 49
CHAPTER 3 Statistics and Time Series 52
3.1 Stochastic Process and Time Series 54
3.1.1 Stochastic Process 55
3.1.2 Time Series 56
3.2 The Interpretation of a Time Average 57
3.2.1 Stationarity 58
3.2.2 Useful Transformations of Nonstationary Processes 62
3.3 A New Tool of Analysis: The Autocorrelation Functions 65
3.3.1 Partial Autocorrelation 69
3.3.2 Statistical Tests for Autocorrelation Coefficients 71
3.4 Conditional Moments and Time Series: What Lies Ahead 73
Key Words 74
Appendix 74
Exercises 76
MODULE II MODELING LINEAR DEPENDENCE
FORECASTING WITH TIME SERIES MODELS
CHAPTER 4 Tools of the Forecaster 79
4.1 The Information Set 80
4.1.1 Some Information Sets Are More
Valuable Than Others 82
4.1.2 Some Time Series Are More Forecastable
Than Others 84
4.2 The Forecast Horizon 84
4.2.1 Forecasting Environments 86
4.3 The Loss Function 89
4.3.1 Some Examples of Loss Functions 91
Contents
XI
4.3.2 Examples 91
4.3.3 Optimal Forecast: An Introduction 93
Key Words 96
Appendix 97
Exercises 98
A PAUSE Where Are We and Where Are We Going? 100
Where Are We Going from Here? 100
How to Organize Your Reading of the Forthcoming Chapters 102
CHAPTER 5 A Understanding Linear Dependence:
A Link to Economic Models 103
5.1 Price Dynamics: The Cob-Web Model (Beginner Level) 103
5.1.1 The Effect of Only One Supply Shock 105
5.1.2 The Effect of Many Supply Shocks 106
5.1.3 A Further Representation of the Dynamics
in the Cob-Web Model 107
5.1.4 Simulation of the Model, pt = p*(l ~ | ) + (J) pt~ + et,
and Autocorrelation Function 109
5.2 Portfolio Returns and Nonsynchronous
Trading (Intermediate Level) 113
5.3 Asset Prices and the Bid-Ask Bounce
(Advanced Level) 116
5.4 Summary 121
Key Words 121
Appendix 121
Exercises 123
CHAPTER 6 Forecasting with Moving Average (MA) Processes 125
6.1 A Model with No Dependence: White Noise 125
6.1.1 What Does This Process Look Like ? 126
6.2 The Wold Decomposition Theorem: The Origin of AR and MA Models
(Advanced Section) 129
6.2.1 Finite Representation of the Wold Decomposition 131
6.3 Forecasting with Moving Average Models 133
6.3.1 MA( 1) Process 135
6.3.2 MA(q) Process 147
Key Words 157
Appendix 157
Exercises 158
XII
Contents
CHAPTER 7 Forecasting with Autoregressive (AR) Processes 160
7.1 Cycles 162
7.2 Autoregressive Models 165
7.2.1 The AR( 1) Process 165
7.2.2 A R(2) Process 173
7.2.3 AR(p) Process 185
7.2.4 Chain Rule of Forecasting 187
7.3 Seasonal Cycles 188
7.3.1 Deterministic and Stochastic Seasonal Cycles 189
7.3.2 Seasonal ARM A Models 192
7.3.3 Combining ARM A and Seasonal ARM A Models 197
Key Words 200
Exercises 200
CHAPTER 8 Forecasting Practice I 202
8.1 The Data: San Diego House Price Index 202
8.2 Model Selection 205
8.2.1 Estimation: AR, MA, andARMA Models 205
8.2.2 Is the Process Covariance-Stationary;
and Is the Process Invertible? 206
8.2.3 Are the Residuals White Noise? 209
8.2.4 Are the Parameters of the Model Statistically Significant? 211
8.2.5 Is the Model Explaining a Substantial Variation
of the Variable of interest? 211
8.2.6 Is It Possible to Select One Model Among Many? 212
8.3 The Forecast 213
8.3.1 Who Are the Consumers of Forecasts? 213
8.3.2 Is It Possible To Have Different Forecasts
from the Same Model? 215
8.3.3 What Is the Most Common Loss Function in Economics
and Business? 215
8.3.4 Final Comments 221
Key Words 221
Exercises 222
CHAPTER 9 Forecasting Practice II: Assessment of Forecasts
and Combination of Forecasts 224
9.1 Optimal Forecast 225
9.1.1 Symmetric and Asymmetric Loss Functions 225
9.1.2 Testing the Optimality of the Forecast 229
9.2 Assessment of Forecasts 238
9.2.1 Descriptive Evaluation of the Average Loss 239
9.2.2 Statistical Evaluation of the Average Loss 240
Contents xiii
9.3 Combination of Forecasts 244
9.3.1 Simple Linear Combinations 244
9.3.2 Optimal Linear Combinations 245
Key Words 247
Appendix 248
Exercises 250
A PAUSE Where Are We and Where Are We Going? 252
Where Are We Going from Here? 253
CHAPTER 10 Forecasting the Long Term: Deterministic
and Stochastic Trends 255
10.1 Deterministic Trends 257
10.1.1 Trend Shapes 258
10.1.2 Trend Stationarity 261
10.1.3 Optimal Forecast 262
10.2 Stochastic Trends 270
10.2.1 Trend Shapes 2 70
10.2.2 Stationarity Properties 272
10.2.3 Optimal Forecast 279
Key Words 291
Exercises 291
CHAPTER 11 Forecasting with a System of Equations:
Vector Autoregression 293
11.1 What Is Vector Autoregression (VAR)? 294
11.2 Estimation of VAR 294
11.3 Granger Causality 299
11.4 Impulse-Response Functions 302
11.5 Forecasting with VAR 305
Key Words 309
Exercises 309
CHAPTER 12 Forecasting the Long Term and the
Short Term Jointly 311
12.1 Finding a Long-Term Equilibrium Relationship 315
12.2 Quantifying Short-Term Dynamics: Vector Error Correction Model 322
12.3 Constructing the Forecast 327
Key Words
Exercises
332
332
XIV
Contents
A PAUSE Where Are We and Where Are We Going? 334
Where We Are Going from Here 335
How to Organize Your Reading of the Forthcoming Chapters 336
MODULE III MODELING MORE COMPLEX DEPENDENCE
CHAPTER 13 Forecasting Volatility I 337
13.1 Motivation 337
13.1.1 The World is Concerned About Uncertainty 337
13.1.2 Volatility Within the Context of Our
Forecasting Problem 339
13.1.3 Setting the Objective 340
13.2 Time-Varying Dispersion: Empirical Evidence 341
13.3 Is There Time Dependence in Volatility? 345
13.4 What Have We Learned So Far? 353
13.5 Simple Specifications for the Conditional Variance 353
13.5.1 Rolling Window Volatility 354
13.5.2 Exponentially Weighted Moving Average
(EWMA) Volatility 355
Key Words 357
Exercises 357
CHAPTER 14 Forecasting Volatility II 359
14.1 The ARCH Family 360
14.1.1 ARCH(l) 362
14.1.2 ARCH(p) 368
14.1.3 GARCH(IJ) 370
14.1.4 Estimation Issues for the ARCH Family 378
14.2 Realized Volatility 380
Key Words 390
Appendix 390
Exercises 393
CHAPTER 15 Financial Applications of Time-Varying
Volatility 395
15.1 Risk Management 395
15.1.1 Value-at-Risk (VaR) 396
15.1.2 Expected Shortfall (ES) 400
Contents xv
15.2 Portfolio Allocation 401
15.3 Asset Pricing 404
15.4 Option Pricing 406
KeyWords 411
Appendix 411
Exercises 412
CHAPTER 16 Forecasting with Nonlinear Models:
An Introduction 413
16.1 Nonlinear Dependence 414
16.1.1 What Is It? 414
16.1.2 Is There Any Evidence of Nonlinear Dynamics in the Data ? 417
16.1.3 Nonlinearity, Correlation, and Dependence 419
16.1.4 What Have We Learned So Far? 420
16.2 Nonlinear Models: An Introduction 421
16.2.1 Threshold Autoregressive Models (TAR) 422
16.2.2 Smooth Transition Models 427
16.2.3 Markov Regime-Switching Models: A Descriptive Introduction 436
16.3 Forecasting with Nonlinear Models 440
16.3.1 One-Step-Ahead Forecast 440
16.3.2 Multistep-Ahead Forecast 441
Key Words 444
Appendix 444
Exercises 445
Appendix A: Review of Probability and Statistics 447
Appendix B: Statistical Tables 463
Glossary 472
References 481
Index
483
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| spelling | González-Rivera, Gloria Verfasser (DE-588)171008383 aut Forecasting for economics and business Gloria González-Rivera London ; New York Routledge 2016 XXII, 490 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 481-483) and index Economic forecasting Economic forecasting United States Wirtschaft (DE-588)4066399-1 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf USA Ökonometrisches Modell (DE-588)4043212-9 s Wirtschaft (DE-588)4066399-1 s Prognose (DE-588)4047390-9 s b DE-604 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029100793&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | González-Rivera, Gloria Forecasting for economics and business Includes bibliographical references (p. 481-483) and index Economic forecasting Economic forecasting United States Wirtschaft (DE-588)4066399-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Prognose (DE-588)4047390-9 gnd |
| subject_GND | (DE-588)4066399-1 (DE-588)4043212-9 (DE-588)4047390-9 |
| title | Forecasting for economics and business |
| title_auth | Forecasting for economics and business |
| title_exact_search | Forecasting for economics and business |
| title_full | Forecasting for economics and business Gloria González-Rivera |
| title_fullStr | Forecasting for economics and business Gloria González-Rivera |
| title_full_unstemmed | Forecasting for economics and business Gloria González-Rivera |
| title_short | Forecasting for economics and business |
| title_sort | forecasting for economics and business |
| topic | Economic forecasting Economic forecasting United States Wirtschaft (DE-588)4066399-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Prognose (DE-588)4047390-9 gnd |
| topic_facet | Economic forecasting Economic forecasting United States Wirtschaft Ökonometrisches Modell Prognose USA |
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