Introduction to time series analysis and forecasting: with applications of SAS and SPSS
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
San Diego [u.a.]
Acad. Press
2009
|
| Edition: | [Nachdr.] |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXV, 528 S. graph. Darst. |
| ISBN: | 0127678700 9780127678702 |
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MARC
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| 245 | 1 | 0 | |a Introduction to time series analysis and forecasting |b with applications of SAS and SPSS |c Robert A. Yaffee with Monnie McGee |
| 250 | |a [Nachdr.] | ||
| 264 | 1 | |a San Diego [u.a.] |b Acad. Press |c 2009 | |
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| 650 | 4 | |a Social sciences -- Statistical methods -- Computer programs | |
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| 650 | 4 | |a Time-series analysis -- Computer programs | |
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Record in the Search Index
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|---|---|
| adam_text | Contents
Preface
xv
Chapter
1
Introduction and Overview
1.1.
Purpose
1
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.
Notation
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.
The Difference Operator
12
1.11.6.
Mean-Centering the Series
12
References
13
Chapter
2
Extrapolative and Decomposition Models
2.1.
Introduction
15
vu
viii Contents
2.2.
Goodness-of-Fit Indicators
15
2.3.
Averaging Techniques
18
2.3.1.
The Simple Average
18
2.3.2.
The 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.
The 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
2.5.5.
Background
50
2.5.6.
Overview of X-ll
52
2.6.
New Features of Census X-12
66
References
66
Chapter
3
Introduction to Box-Jenkins Time Series Analysis
3.1.
Introduction
69
3.2.
The 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.
The Dickey-Fuller Test
81
Contents ix
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
References
99
Chapter
4
The Basic
ARMA
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.
The Sample Autocorrelation Function
110
4.5.
The Standard Error of the ACF
118
4.6.
The Bounds of Stationarity and Invertibility
119
4.7.
The 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
References
149
x
Contents
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.
The 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.
Purely 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.
The Question of Deterministic or Stochastic
Seasonality
188
References
189
Chapter
6
Estimation and Diagnosis
6.1.
Introduction
191
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
References
213
Contents xi
Chapter
7
Metadiagnosis
and Forecasting
7.1.
Introduction
215
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.
The Forecast Function
225
7.3.4.
The 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
7.7.1.
Introduction
251
7.7.2.
SAS
Syntax
252
7.7.3.
SPSS Syntax
254
7.8.
Regression Combination of Forecasts
256
References
263
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.
The Intervention (Impulse Response) Function
270
8.3.3.
The 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
xii Contents
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.
The 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
References
350
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.
The Assumption of the Single-Input Case
355
9.3.2.
The Basic Nature of the Single-Input
Transfer Function
355
9.4.
Modeling Strategies
368
9.4.1.
The Conventional Box-Jenkins
Modeling Strategy
368
9.4.2.
The 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
References
423
Chapter
10
Autoregressive
Error Models
10.1.
The Nature of Serial Correlation of Error
425
10.1.1.
Regression Analysis and the Consequences of
Autocorrelated Error
426
Contents
хні
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 Forecasts
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
References
465
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
References
478
Chapter
12
Power Analysis and Sample Size Determination for
Well-Known Time Series Models
Monnie McGee
12.1.
Census X-ll
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
Contents
12.6.
Conclusion
491
References
492
Appendix A
495
Glossary
497
Index
513
|
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| ctrlnum | (OCoLC)705702681 (DE-599)BVBBV036590380 |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | [Nachdr.] |
| format | Book |
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| id | DE-604.BV036590380 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:43:36Z |
| institution | BVB |
| isbn | 0127678700 9780127678702 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020511102 |
| oclc_num | 705702681 |
| open_access_boolean | |
| owner | DE-824 DE-473 DE-BY-UBG |
| owner_facet | DE-824 DE-473 DE-BY-UBG |
| physical | XXV, 528 S. graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Acad. Press |
| record_format | marc |
| spelling | Yaffee, Robert A. Verfasser (DE-588)129217522 aut Introduction to time series analysis and forecasting with applications of SAS and SPSS Robert A. Yaffee with Monnie McGee [Nachdr.] San Diego [u.a.] Acad. Press 2009 XXV, 528 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier SAS (Computer file) SPSS (Computer file) Sozialwissenschaften Social sciences -- Statistical methods Social sciences -- Statistical methods -- Computer programs Social sciences -- Forecasting -- Computer programs Social prediction -- Computer programs Time-series analysis -- Computer programs Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf SPSS (DE-588)4056588-9 gnd rswk-swf SAS Programm (DE-588)4195685-0 gnd rswk-swf Prognose (DE-588)4047390-9 s SAS Programm (DE-588)4195685-0 s SPSS (DE-588)4056588-9 s DE-604 Zeitreihenanalyse (DE-588)4067486-1 s McGee, Monnie Verfasser (DE-588)128376961 aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020511102&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Yaffee, Robert A. McGee, Monnie Introduction to time series analysis and forecasting with applications of SAS and SPSS SAS (Computer file) SPSS (Computer file) Sozialwissenschaften Social sciences -- Statistical methods Social sciences -- Statistical methods -- Computer programs Social sciences -- Forecasting -- Computer programs Social prediction -- Computer programs Time-series analysis -- Computer programs Zeitreihenanalyse (DE-588)4067486-1 gnd Prognose (DE-588)4047390-9 gnd SPSS (DE-588)4056588-9 gnd SAS Programm (DE-588)4195685-0 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4047390-9 (DE-588)4056588-9 (DE-588)4195685-0 |
| title | Introduction to time series analysis and forecasting with applications of SAS and SPSS |
| title_auth | Introduction to time series analysis and forecasting with applications of SAS and SPSS |
| title_exact_search | Introduction to time series analysis and forecasting with applications of SAS and SPSS |
| title_full | Introduction to time series analysis and forecasting with applications of SAS and SPSS Robert A. Yaffee with Monnie McGee |
| title_fullStr | Introduction to time series analysis and forecasting with applications of SAS and SPSS Robert A. Yaffee with Monnie McGee |
| title_full_unstemmed | Introduction to time series analysis and forecasting with applications of SAS and SPSS Robert A. Yaffee with Monnie McGee |
| title_short | Introduction to time series analysis and forecasting |
| title_sort | introduction to time series analysis and forecasting with applications of sas and spss |
| title_sub | with applications of SAS and SPSS |
| topic | SAS (Computer file) SPSS (Computer file) Sozialwissenschaften Social sciences -- Statistical methods Social sciences -- Statistical methods -- Computer programs Social sciences -- Forecasting -- Computer programs Social prediction -- Computer programs Time-series analysis -- Computer programs Zeitreihenanalyse (DE-588)4067486-1 gnd Prognose (DE-588)4047390-9 gnd SPSS (DE-588)4056588-9 gnd SAS Programm (DE-588)4195685-0 gnd |
| topic_facet | SAS (Computer file) SPSS (Computer file) Sozialwissenschaften Social sciences -- Statistical methods Social sciences -- Statistical methods -- Computer programs Social sciences -- Forecasting -- Computer programs Social prediction -- Computer programs Time-series analysis -- Computer programs Zeitreihenanalyse Prognose SPSS SAS Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020511102&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT yaffeeroberta introductiontotimeseriesanalysisandforecastingwithapplicationsofsasandspss AT mcgeemonnie introductiontotimeseriesanalysisandforecastingwithapplicationsofsasandspss |