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Introduction to time series analysis and forecasting with applications of SAS and SPSS

Author: Robert A Yaffee; Monnie McGee
Publisher: San Diego, CA : Academic Press, ©2000.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:

Explaining the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two statistical packages - SAS and SPSS. It also features treatments of  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Robert A Yaffee; Monnie McGee
ISBN: 0127678700 9780127678702
OCLC Number: 50666578
Description: xxv, 528 pages : illustrations ; 24 cm
Contents: 1.2. Time Series 2 --
1.3. Missing Data 3 --
1.4. Sample Size 3 --
1.5. Representativeness 4 --
1.6. Scope of Application 4 --
1.7. Stochastic and Deterministic Processes 5 --
1.8. Stationarity 5 --
1.9. Methodological Approaches 7 --
1.10. Importance 9 --
1.11.1. Gender 9 --
1.11.2. Summation 10 --
1.11.3. Expectation 11 --
1.11.4. Lag Operator 12 --
1.11.5. Difference Operator 12 --
1.11.6. Mean-Centering the Series 12 --
Chapter 2 Extrapolative and Decomposition Models --
2.2. Goodness-of-Fit Indicators 15 --
2.3. Averaging Techniques 18 --
2.3.1. Simple Average 18 --
2.3.2. Single Moving Average 18 --
2.3.3. Centered Moving Averages 20 --
2.3.4. Double Moving Averages 20 --
2.3.5. Weighted Moving Averages 22 --
2.4. Exponential Smoothing 23 --
2.4.1. Simple Exponential Smoothing 23 --
2.4.2. Holt's Linear Exponential Smoothing 32 --
2.4.3. Dampened Trend Linear Exponential Smoothing Model 38 --
2.4.4. Exponential Smoothing for Series with Trend and Seasonality: Winter's Methods 39 --
2.4.5. Basic Evaluation of Exponential Smoothing 43 --
2.5. Decomposition Methods 45 --
2.5.1. Components of a Series 45 --
2.5.2. Trends 46 --
2.5.3. Seasonality 50 --
2.5.4. Cycles 50 --
2.5.6. Overview of X-11 52 --
2.6. New Features of Census X-12 66 --
Chapter 3 Introduction to Box-Jenkins Time Series Analysis --
3.2. Importance of Time Series Analysis Modeling 69 --
3.3. Limitations 70 --
3.4. Assumptions 70 --
3.5. Time Series 74 --
3.5.1. Moving Average Processes 74 --
3.5.2. Autoregressive Processes 76 --
3.5.3. ARMA Processes 77 --
3.5.4. Nonstationary Series and Transformations to Stationarity 77 --
3.6. Tests for Nonstationarity 81 --
3.6.1. Dickey-Fuller Test 81 --
3.6.2. Augmented Dickey-Fuller Test 84 --
3.6.3. Assumptions of the Dickey-Fuller and Augmented Dickey-Fuller Tests 85 --
3.6.4. Programming the Dickey-Fuller Test 86 --
3.7. Stabilizing the Variance 90 --
3.8. Structural or Regime Stability 92 --
3.9. Strict Stationarity 93 --
3.10. Implications of Stationarity 94 --
3.10.1. For Autoregression 94 --
3.10.2. Implications of Stationarity for Moving Average Processes 97 --
Chapter 4 Basic ARIMA Model --
4.1. Introduction to ARIMA 101 --
4.2. Graphical Analysis of Time Series Data 102 --
4.2.1. Time Sequence Graphs 102 --
4.2.2. Correlograms and Stationarity 106 --
4.3. Basic Formulation of the Autoregressive Integrated Moving Average Model 108 --
4.4. Sample Autocorrelation Function 110 --
4.5. Standard Error of the ACF 118 --
4.6. Bounds of Stationarity and Invertibility 119 --
4.7. Sample Partial Autocorrelation Function 122 --
4.7.1. Standard Error of the PACF 125 --
4.8. Bounds of Stationarity and Invertibility Reviewed 125 --
4.9. Other Sample Autocorrelation Functions 126 --
4.10. Tentative Identification of Characteristic Patterns of Integrated, Autoregressive, Moving Average, and ARMA Processes 128 --
4.10.1. Preliminary Programming Syntax for Identification of the Model 128 --
4.10.2. Stationarity Assessment 132 --
4.10.3. Identifying Autoregressive Models 134 --
4.10.4. Identifying Moving Average Models 137 --
4.10.5. Identifying Mixed Autoregressive-Moving Average Models 142 --
Chapter 5 Seasonal ARIMA Models --
5.1. Cyclicity 151 --
5.2. Seasonal Nonstationarity 154 --
5.3. Seasonal Differencing 161 --
5.4. Multiplicative Seasonal Models 162 --
5.4.1. Seasonal Autoregressive Models 164 --
5.4.2. Seasonal Moving Average Models 166 --
5.4.3. Seasonal Autoregressive Moving Average Models 168 --
5.5. Autocorrelation Structure of Seasonal ARIMA Models 169 --
5.6. Stationarity and Invertibility of Seasonal ARIMA Models 170 --
5.7. A Modeling Strategy for the Seasonal ARIMA Model 171 --
5.7.1. Identification of Seasonal Nonstationarity 171 --
5.7.2. Purely5 Seasonal Models 171 --
5.7.3. A Modeling Strategy for General Multiplicative Seasonal Models 173 --
5.8. Programming Seasonal Multiplicative Box-Jenkins Models 183 --
5.8.1. SAS Programming Syntax 183 --
5.8.2. SPSS Programming Syntax 185 --
5.9. Alternative Methods of Modeling Seasonality 186 --
5.10. Question of Deterministic or Stochastic Seasonality 188 --
Chapter 6 Estimation and Diagnosis --
6.2. Estimation 191 --
6.2.1. Conditional Least Squares 192 --
6.2.2. Unconditional Least Squares 195 --
6.2.3. Maximum Likelihood Estimation 198 --
6.2.4. Computer Applications 204 --
6.3. Diagnosis of the Model 208 --
Chapter 7 Metadiagnosis and Forecasting --
7.2. Metadiagnosis 217 --
7.2.1. Statistical Program Output of Metadiagnostic Criteria 221 --
7.3. Forecasting with Box-Jenkins Models 222 --
7.3.1. Forecasting Objectives 222 --
7.3.2. Basic Methodology of Forecasting 224 --
7.3.3. Forecast Function 225 --
7.3.4. Forecast Error 232 --
7.3.5. Forecast Error Variance 232 --
7.3.6. Forecast Confidence Intervals 233 --
7.3.7. Forecast Profiles for Basic Processes 234 --
7.4. Characteristics of the Optimal Forecast 244 --
7.5. Basic Combination of Forecasts 245 --
7.6. Forecast Evaluation 248 --
7.7. Statistical Package Forecast Syntax 251 --
7.7.2. SAS Syntax 252 --
7.7.3. SPSS Syntax 254 --
7.8. Regression Combination of Forecasts 256 --
Chapter 8 Intervention Analysis --
8.1. Introduction: Event Interventions and Their Impacts 265 --
8.2. Assumptions of the Event Intervention (Impact) Model 267 --
8.3. Impact Analysis Theory 268 --
8.3.1. Intervention Indicators 268 --
8.3.2. Intervention (Impulse Response) Function 270 --
8.3.3. Simple Step Function: Abrupt Onset, Permanent Duration 270 --
8.3.4. First-Order Step Function: Gradual Onset, Permanent Duration 272 --
8.3.5. Abrupt Onset, Temporary Duration 276 --
8.3.6. Abrupt Onset and Oscillatory Decay 278 --
8.3.7. Graduated Onset and Gradual Decay 279 --
8.4. Significance Tests for Impulse Response Functions 280 --
8.5. Modeling Strategies for Impact Analysis 282 --
8.5.1. Box-Jenkins-Tiao Strategy 283 --
8.5.2. Full Series Modeling Strategy 285 --
8.6. Programming Impact Analysis 288 --
8.6.1. An Example of SPSS Impact Analysis Syntax 290 --
8.6.2. An Example of SAS Impact Analysis Syntax 297 --
8.6.3. Example: The Impact of Watergate on Nixon Presidential Approval Ratings 314 --
8.7. Applications of Impact Analysis 342 --
8.8. Advantages of Intervention Analysis 345 --
8.9. Limitations of Intervention Analysis 346 --
Chapter 9 Transfer Function Models --
9.1. Definition of a Transfer Function 353 --
9.2. Importance 354 --
9.3. Theory of the Transfer Function Model 355 --
9.3.1. Assumption of the Single-Input Case 355 --
9.3.2. Basic Nature of the Single-Input Transfer Function 355 --
9.4. Modeling Strategies 368 --
9.4.1. Conventional Box-Jenkins Modeling Strategy 368 --
9.4.2. Linear Transfer Function Modeling Strategy 399 --
9.5. Cointegration 420 --
9.6. Long-Run and Short-Run Effects in Dynamic Regression 421 --
9.7. Basic Characteristics of a Good Time Series Model 422 --
Chapter 10 Autoregressive Error Models --
10.1. Nature of Serial Correlation of Error 425 --
10.1.1. Regression Analysis and the Consequences of Autocorrelated Error 426 --
10.2. Sources of Autoregressive Error 435 --
10.3. Autoregressive Models with Serially Correlated Errors 437 --
10.4. Tests for Serial Correlation of Error 437 --
10.5. Corrective Algorithms for Regression Models with Autocorrelated Error 439 --
10.6. Forecasting with Autocorrelated Error Models 441 --
10.7. Programming Regression with Autocorrelated Errors 443 --
10.7.1. SAS Proc Autoreg 443 --
10.7.2. SPSS ARIMA Procedures for Autoregressive Error Models 452 --
10.8. Autoregression in Combining Forecastsh 458 --
10.9. Models with Stochastic Variance 462 --
10.9.1. ARCH and GARCH Models 463 --
10.9.2. ARCH Models for Combining Forecasts 464 --
Chapter 11 A Review of Model and Forecast Evaluation --
11.1. Model and Forecast Evaluation 467 --
11.2. Model Evaluation 468 --
11.3. Comparative Forecast Evaluation 469 --
11.3.1. Capability of Forecast Methods 471 --
11.4. Comparison of Individual Forecast Methods 476 --
11.5. Comparison of Combined Forecast Models 477 --
Chapter 12 Power Analysis and Sample Size Determination for Well-Known Time Series Models / Monnie McGee --
12.1. Census X-11 482 --
12.2. Box-Jenkins Models 483 --
12.3. Tests for Nonstationarity 486 --
12.4. Intervention Analysis and Transfer Functions 487 --
12.5. Regression with Autoregressive Errors 490.
Responsibility: Robert A. Yaffee with Monnie McGee.
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