HKSYU Course Resources
MARC Display
LEADER 13230cam a2202977 a 4500
001
991001257819707546
005
20220623152231.0
008
111208s2013 maua b 001 0 eng
010
a| 2011049940
020
a| 9780131474932 (hbk.)
020
a| 0131474936 (hbk.)
035
a| (HKSYU)b14639300-852hksyu_inst
040
a| DLC
b| eng
c| DLC
d| YDX
d| CDX
d| YDXCP
d| BWX
d| NhCcYME
d| HK-SYU
042
a| pcc
043
a| n-us---
050
4
a| HB3730
b| .G57 2013
082
0
0
a| 338.5/44
2| 23
092
0
a| 338.544
b| GON 2013
100
1
a| González-Rivera, Gloria.
245
1
0
a| Forecasting for economics and business /
c| Gloria Gonzalez-Rivera.
260
a| Boston :
b| Pearson,
c| c2013.
300
a| xxii, 490 p. :
b| ill. ;
c| 24 cm.
490
1
a| The Pearson series in economics
504
a| Includes bibliographical references (p. 481-482) and index.
650
0
a| Economic forecasting.
650
0
a| Economic forecasting
z| United States.
830
0
a| Pearson series in economics.
907
a| b14639300
b| 08-01-22
c| 02-01-14
910
a| ykc
b| df
935
a| (HK-SYU)500826074
9| ExL
970
0
1
t| Preface
p| xvi
970
0
1
t| Module I Statistics And Time Series
970
1
1
l| ch. 1
t| Introduction and Context
p| 1
970
1
1
l| 1.1.
t| What Is Forecasting?
p| 1
970
1
1
l| 1.1.1.
t| The First Forecaster in History: The Delphi Oracle
p| 1
970
1
1
l| 1.1.2.
t| Examples of Modern Forecasts
p| 2
970
1
1
l| 1.1.3.
t| Definition of Forecasting
p| 3
970
1
1
l| 1.1.4.
t| Two Types of Forecasts
p| 4
970
1
1
l| 1.2.
t| Who Are the Users of Forecasts?
p| 4
970
1
1
l| 1.2.1.
t| Firms
p| 4
970
1
1
l| 1.2.2.
t| Consumers and Investors
p| 5
970
1
1
l| 1.2.3.
t| Government
p| 5
970
1
1
l| 1.3.
t| Becoming Familiar with Economic Time Series: Features of a Time Series
p| 6
970
1
1
l| 1.3.1.
t| Trends
p| 7
970
1
1
l| 1.3.2.
t| Cycles
p| 8
970
1
1
l| 1.3.3.
t| Seasonality
p| 9
970
1
1
l| 1.4.
t| Basic Notation and the Objective of the Forecaster
p| 11
970
1
1
l| 1.4.1.
t| Basic Notation
p| 11
970
1
1
l| 1.4.2.
t| The Forecaster's Objective
p| 12
970
1
1
l| 1.5.
t| A Road Map for This Forecasting Book
p| 13
970
1
1
l| 1.6.
t| Resources
p| 14
970
1
1
t| Key Words
p| 16
970
1
1
t| Exercises
p| 17
970
1
1
l| ch. 2
t| Review of the Linear Regression Model
p| 24
970
1
1
l| 2.1.
t| Conditional Density and Conditional Moments
p| 24
970
1
1
l| 2.2.
t| Linear Regression Model
p| 27
970
1
1
l| 2.3.
t| Estimation: Ordinary Least Squares
p| 29
970
1
1
l| 2.3.1.
t| R-squared and Adjusted R-squared
p| 32
970
1
1
l| 2.3.2.
t| Linearity and OLS
p| 33
970
1
1
l| 2.3.3.
t| Assumptions of OLS: The Gauss-Markov Theorem
p| 35
970
1
1
l| 2.3.4.
t| An Example: House Prices and Interest Rates
p| 38
970
1
1
l| 2.4.
t| Hypothesis Testing in a Regression Model
p| 41
970
1
1
l| 2.4.1.
t| The t-ratio
p| 41
970
1
1
l| 2.4.2.
t| The F-test
p| 44
970
1
1
t| Key Words
p| 46
970
1
1
t| Appendix
p| 47
970
1
1
t| Exercises
p| 49
970
1
1
l| ch. 3
t| Statistics and Time Series
p| 52
970
1
1
l| 3.1.
t| Stochastic Process and Time Series
p| 54
970
1
1
l| 3.1.1.
t| Stochastic Process
p| 55
970
1
1
l| 3.1.2.
t| Time Series
p| 56
970
1
1
l| 3.2.
t| The Interpretation of a Time Average
p| 57
970
1
1
l| 3.2.1.
t| Stationarity
p| 58
970
1
1
l| 3.2.2.
t| Useful Transformations of Nonstationary Processes
p| 62
970
1
1
l| 3.3.
t| A New Tool of Analysis: The Autocorrelation Functions
p| 65
970
1
1
l| 3.3.1.
t| Partial Autocorrelation
p| 69
970
1
1
l| 3.3.2.
t| Statistical Tests for Autocorrelation Coefficients
p| 71
970
1
1
l| 3.4.
t| Conditional Moments and Time Series: What Lies Ahead
p| 73
970
1
1
t| Key Words
p| 74
970
1
1
t| Appendix
p| 74
970
1
1
t| Exercises
p| 76
970
0
1
t| Module II Modeling Linear Dependence Forecasting With Time Series Models
970
1
1
l| ch. 4
t| Tools of the Forecaster
p| 79
970
1
1
l| 4.1.
t| The Information Set
p| 80
970
1
1
l| 4.1.1.
t| Some Information Sets Are More Valuable Than Others
p| 82
970
1
1
l| 4.1.2.
t| Some Time Series Are More Forecastable Than Others
p| 84
970
1
1
l| 4.2.
t| The Forecast Horizon
p| 84
970
1
1
l| 4.2.1.
t| Forecasting Environments
p| 86
970
1
1
l| 4.3.
t| The Loss Function
p| 89
970
1
1
l| 4.3.1.
t| Some Examples of Loss Functions
p| 91
970
1
1
l| 4.3.2.
t| Examples
p| 91
970
1
1
l| 4.3.3.
t| Optimal Forecast: An Introduction
p| 93
970
1
1
t| Key Words
p| 96
970
1
1
t| Appendix
p| 97
970
1
1
t| Exercises
p| 98
970
1
1
t| A PAUSE Where Are We and Where Are We Going?
p| 100
970
1
1
t| Where Are We Going from Here?
p| 100
970
1
1
t| How to Organize Your Reading of the Forthcoming Chapters
p| 102
970
1
1
l| ch. 5
t| A Understanding Linear Dependence: A Link to Economic Models
p| 103
970
1
1
l| 5.1.
t| Price Dynamics: The Cob-Web Model (Beginner Level)
p| 103
970
1
1
l| 5.1.1.
t| The Effect of Only One Supply Shock
p| 105
970
1
1
l| 5.1.2.
t| The Effect of Many Supply Shocks
p| 106
970
1
1
l| 5.1.3.
t| A Further Representation of the Dynamics in the cob-Web Model
p| 107
970
1
1
l| 5.1.4.
t| Simulation of the model, p, = p*(1 - φ)+φρt-1+epsilonν and Autocorrelation Function
p| 109
970
1
1
l| 5.2.
t| Portfolio Returns and Nonsynchronous Trading (Intermediate Level)
p| 113
970
1
1
l| 5.3.
t| Asset Prices and the Bid-Ask Bounce (Advanced Level)
p| 116
970
1
1
l| 5.4.
t| Summary
p| 121
970
1
1
t| Key Words
p| 121
970
1
1
t| Appendix
p| 121
970
1
1
t| Exercises
p| 123
970
1
1
l| ch. 6
t| Forecasting with Moving Average (MA) Processes
p| 125
970
1
1
l| 6.1.
t| A Model with No Dependence: White Noise
p| 125
970
1
1
l| 6.1.1.
t| What Does This Process Look Like?
p| 126
970
1
1
l| 6.2.
t| The Wold Decomposition Theorem: The Origin of AR and MA Models (Advanced Section)
p| 129
970
1
1
l| 6.2.1.
t| Finite Representation of the Wold Decomposition
p| 131
970
1
1
l| 6.3.
t| Forecasting with Moving Average Models
p| 133
970
1
1
l| 6.3.1.
t| MA(1) Process
p| 135
970
1
1
l| 6.3.2.
t| MA(q) Process
p| 147
970
1
1
t| Key Words
p| 157
970
1
1
t| Appendix
p| 157
970
1
1
t| Exercises
p| 158
970
1
1
l| ch. 7
t| Forecasting with Autoregressive (AR) Processes
p| 160
970
1
1
l| 7.1.
t| Cycles
p| 162
970
1
1
l| 7.2.
t| Autoregressive Models
p| 165
970
1
1
l| 7.2.1.
t| The AR(1) Process
p| 165
970
1
1
l| 7.2.2.
t| AR(2) Process
p| 173
970
1
1
l| 7.2.3.
t| AR(p) Process
p| 185
970
1
1
l| 7.2.4.
t| Chain Rule of Forecasting
p| 187
970
1
1
l| 7.3.
t| Seasonal Cycles
p| 188
970
1
1
l| 7.3.1.
t| Deterministic and Stochastic Seasonal Cycles
p| 189
970
1
1
l| 7.3.2.
t| Seasonal ARMA Models
p| 192
970
1
1
l| 7.3.3.
t| Combining ARMA and Seasonal ARMA Models
p| 197
970
1
1
t| Key Words
p| 200
970
1
1
t| Exercises
p| 200
970
1
1
l| ch. 8
t| Forecasting Practice I
p| 202
970
1
1
l| 8.1.
t| The Data: San Diego House Price Index
p| 202
970
1
1
l| 8.2.
t| Model Selection
p| 205
970
1
1
l| 8.2.1.
t| Estimation: AR, MA, and ARMA Models
p| 205
970
1
1
l| 8.2.2.
t| Is the Process Covariance-Stationary and Is the Process Invertible?
p| 206
970
1
1
l| 8.2.3.
t| Are the Residuals White Noise?
p| 209
970
1
1
l| 8.2.4.
t| Are the Parameters of the Model Statistically Significant?
p| 211
970
1
1
l| 8.2.5.
t| Is the Model Explaining a Substantial Variation of the Variable of Interest?
p| 211
970
1
1
l| 8.2.6.
t| Is It Possible to Select One Model Among Many?
p| 212
970
1
1
l| 8.3.
t| The Forecast
p| 213
970
1
1
l| 8.3.1.
t| Who Are the Consumers of Forecasts?
p| 213
970
1
1
l| 8.3.2.
t| Is It Possible To Have Different Forecasts from the Same Model?
p| 215
970
1
1
l| 8.3.3.
t| What Is the Most Common Loss Function in Economics and Business?
p| 215
970
1
1
l| 8.3.4.
t| Final Comments
p| 221
970
1
1
t| Key Words
p| 221
970
1
1
t| Exercises
p| 222
970
1
1
l| ch. 9
t| Forecasting Practice II: Assessment of Forecasts and Combination of Forecasts
p| 224
970
1
1
l| 9.1.
t| Optimal Forecast
p| 225
970
1
1
l| 9.1.1.
t| Symmetric and Asymmetric Loss Functions
p| 225
970
1
1
l| 9.1.2.
t| Testing the Optimality, of the Forecast
p| 229
970
1
1
l| 9.2.
t| Assessment of Forecasts
p| 238
970
1
1
l| 9.2.1.
t| Descriptive Evaluation of the Average Loss
p| 239
970
1
1
l| 9.2.2.
t| Statistical Evaluation of the Average Loss
p| 240
970
1
1
l| 9.3.
t| Combination of Forecasts
p| 244
970
1
1
l| 9.3.1.
t| Simple Linear Combinations
p| 244
970
1
1
l| 9.3.2.
t| Optimal Linear Combinations
p| 245
970
1
1
t| Key Words
p| 247
970
1
1
t| Appendix
p| 248
970
1
1
t| Exercises
p| 250
970
1
1
t| A PAUSE Where Are We and Where Are We Going?
p| 252
970
1
1
t| Where Are We Going from Here?
p| 253
970
1
1
l| ch. 10
t| Forecasting the Long Term: Deterministic and Stochastic Trends
p| 255
970
1
1
l| 10.1.
t| Deterministic Trends
p| 257
970
1
1
l| 10.1.1.
t| Trend Shapes
p| 258
970
1
1
l| 10.1.2.
t| Trend Stationarity
p| 261
970
1
1
l| 10.1.3.
t| Optimal Forecast
p| 262
970
1
1
l| 10.2.
t| Stochastic Trends
p| 270
970
1
1
l| 10.2.1.
t| Trend Shapes
p| 270
970
1
1
l| 10.2.2.
t| Stationarity Properties
p| 272
970
1
1
l| 10.2.3.
t| Optimal Forecast
p| 279
970
1
1
t| Key Words
p| 291
970
1
1
t| Exercises
p| 291
970
1
1
l| ch. 11
t| Forecasting with a System of Equations: Vector Autoregression
p| 293
970
1
1
l| 11.1.
t| What Is Vector Autoregression (VAR)?
p| 294
970
1
1
l| 11.2.
t| Estimation of VAR
p| 294
970
1
1
l| 11.3.
t| Granger Causality
p| 299
970
1
1
l| 11.4.
t| Impulse-Response Functions
p| 302
970
1
1
l| 11.5.
t| Forecasting with VAR
p| 305
970
1
1
t| Key Words
p| 309
970
1
1
t| Exercises
p| 309
970
1
1
l| ch. 12
t| Forecasting the Long Term and the Short Term Jointly
p| 311
970
1
1
l| 12.1.
t| Finding a Long-Term Equilibrium Relationship
p| 315
970
1
1
l| 12.2.
t| Quantifying Short-Term Dynamics: Vector Error Correction Model
p| 322
970
1
1
l| 12.3.
t| Constructing the Forecast
p| 327
970
1
1
t| Key Words
p| 332
970
1
1
t| Exercises
p| 332
970
1
1
t| A PAUSE Where Are We and Where Are We Going?
p| 334
970
1
1
t| Where We Are Going from Here
p| 335
970
1
1
t| How to Organize Your Reading of the Forthcoming Chapters
p| 336
970
0
1
t| Module III Modeling More Complex Dependence
970
1
1
l| ch. 13
t| Forecasting Volatility I
p| 337
970
1
1
l| 13.1.
t| Motivation
p| 337
970
1
1
l| 13.1.1.
t| The World is Concerned About Uncertainty
p| 337
970
1
1
l| 13.1.2.
t| Volatility Within the Context of Our Forecasting Problem
p| 339
970
1
1
l| 13.1.3.
t| Setting the Objective
p| 340
970
1
1
l| 13.2.
t| Time-Varying Dispersion: Empirical Evidence
p| 341
970
1
1
l| 13.3.
t| Is There Time Dependence in Volatility?
p| 345
970
1
1
l| 13.4.
t| What Have We Learned So Far?
p| 353
970
1
1
l| 13.5.
t| Simple Specifications for the Conditional Variance
p| 353
970
1
1
l| 13.5.1.
t| Rolling Window Volatility
p| 354
970
1
1
l| 13.5.2.
t| Exponentially Weighted Moving Average (EWMA) Volatility
p| 355
970
1
1
t| Key Words
p| 357
970
1
1
t| Exercises
p| 357
970
1
1
l| ch. 14
t| Forecasting Volatility II
p| 359
970
1
1
l| 14.1.
t| The ARCH Family
p| 360
970
1
1
l| 14.1.1.
t| ARCH(1)
p| 362
970
1
1
l| 14.1.2.
t| ARCH(p)
p| 368
970
1
1
l| 14.1.3.
t| GARCH(1,1)
p| 370
970
1
1
l| 14.1.4.
t| Estimation Issues for the ARCH Family
p| 378
970
1
1
l| 14.2.
t| Realized Volatility
p| 380
970
1
1
t| Key Words
p| 390
970
1
1
t| Appendix
p| 390
970
1
1
t| Exercises
p| 393
970
1
1
l| ch. 15
t| Financial Applications of Time-Varying Volatility
p| 395
970
1
1
l| 15.1.
t| Risk Management
p| 395
970
1
1
l| 15.1.1.
t| Value-at-Risk (VaR)
p| 396
970
1
1
l| 15.1.2.
t| Expected Shortfall (ES)
p| 400
970
1
1
l| 15.2.
t| Portfolio Allocation
p| 401
970
1
1
l| 15.3.
t| Asset Pricing
p| 404
970
1
1
l| 15.4.
t| Option Pricing
p| 406
970
1
1
t| Key Words
p| 411
970
1
1
t| Appendix
p| 411
970
1
1
t| Exercises
p| 412
970
1
1
l| ch. 16
t| Forecasting with Nonlinear Models: An Introduction
p| 413
970
1
1
l| 16.1.
t| Nonlinear Dependence
p| 414
970
1
1
l| 16.1.1.
t| What Is It?
p| 414
970
1
1
l| 16.1.2.
t| Is There Any Evidence of Nonlinear Dynamics in the Data?
p| 417
970
1
1
l| 16.1.3.
t| Nonlinearity, Correlation, and Dependence
p| 419
970
1
1
l| 16.1.4.
t| What Have We Learned So Far?
p| 420
970
1
1
l| 16.2.
t| Nonlinear Models: An Introduction
p| 421
970
1
1
l| 16.2.1.
t| Threshold Autoregressive Models (TAR)
p| 422
970
1
1
l| 16.2.2.
t| Smooth Transition Models
p| 427
970
1
1
l| 16.2.3.
t| Markov Regime-Switching Models. A Descriptive Introduction
p| 436
970
1
1
l| 16.3.
t| Forecasting with Nonlinear Models
p| 440
970
1
1
l| 16.3.1.
t| One-Step-Ahead Forecast
p| 440
970
1
1
l| 16.3.2.
t| Multistep-Ahead Forecast
p| 441
970
1
1
t| Key Words
p| 444
970
1
1
t| Appendix
p| 444
970
1
1
t| Exercises
p| 445
970
0
1
t| Appendix A: Review of Probability and Statistics
p| 447
970
0
1
t| Appendix B: Statistical Tables
p| 463
970
0
1
t| Glossary
p| 472
970
0
1
t| References
p| 481
970
0
1
t| Index
p| 483
998
a| book
b| 22-01-14
c| m
d| a
e| -
f| eng
g| mau
h| 0
i| 0
945
h| Supplement
l| location
i| barcode
y| id
f| bookplate
a| callnoa
b| callnob
n| ECON314
945
h| Supplement
l| location
i| barcode
y| id
f| bookplate
a| callnoa
b| callnob
n| ECON314