Forecasting, Structural Time Series Models and the Kalman Filter

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Cambridge University Press, 1990 - Business & Economics - 554 pages
3 Reviews
This book provides a synthesis of concepts and materials that ordinarily appear separately in time series and econometrics literature, presenting a comprehensive review of both theoretical and applied concepts. Perhaps the most novel feature of the book is its use of Kalman filtering together with econometric and time series methodology. From a technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. This technique was originally developed in control engineering but is becoming increasingly important in economics and operations research. The book is primarily concerned with modeling economic and social time series and with addressing the special problems that the treatment of such series pose.
 

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Contents

Introduction
1
12 Explanatory variables and intervention analysis
4
13 Multivariate models
7
14 Statistical treatment
10
15 Modelling methodology
11
16 Forecasting
14
17 Computer software
15
Univariate time series models
17
65 Timevarying and nonlinear models
341
66 Nonnormality count data and qualitative observations
348
Exercises
362
Explanatory variables
365
72 Estimation in the frequency domain
376
73 Estimation of models with explanatory variables and structural time series components
381
74 Tests and measures of goodness of fit
385
75 Model selection strategy and applications
390

21 Introduction
18
22 Ad hoc forecasting procedures
23
23 The structure of time series models
31
24 Stochastic properties
49
25 ARIMA models and the reduced form
65
26 ARIMA modelling
75
27 Applications
81
Exercises
99
State space models and the Kalman filter
100
32 The Kalman filter
104
33 Properties of timeinvariant models
113
34 Maximum likelihood estimation and the prediction error decomposition
125
35 Prediction
147
36 Smoothing
149
37 Nonlinearity and nonnormality
155
Appendix Properties of the multivariate normal distribution
165
Exercises
166
Estimation prediction and smoothing for univariate structural time series models
168
42 Estimation in the time domain
180
43 Estimation in the frequency domain
191
44 Identifiability
205
45 Properties of estimators
209
46 Prediction
222
47 Estimation of components
226
Exercises
232
Testing and model selection
234
52 Lagrange multiplier tests
239
53 Tests of specification for structural models
248
54 Diagnostics
256
55 Goodness of fit
263
56 Postsample predictive testing and model evaluation
270
57 Strategy for model selection
273
Exercises
281
Extensions of the univariate model
283
62 Seasonally and seasonal adjustment
300
63 Different timing intervals for the model and observations
309
64 Data irregularities
326
76 Intervention analysis
397
77 Timevarying parameters
408
78 Instrumental variables
411
79 Count data
418
Exercises
422
Multivariate models
423
82 Seemingly unrelated time series equations
429
83 Homogeneous systems
435
84 Testing and model selection
442
85 Dynamic factor analysis
449
86 Intervention analysis with control groups
456
87 Missing observations delayed observations and contemporaneous aggregation
463
88 Vector autoregressive models
468
89 Simultaneous equation models
474
Exercises
477
Continuous time
479
91 Introduction
480
92 Stock variables
486
93 Flow variables
492
94 Multivariate models
501
Appendix 1 Principal structural time series components and models
510
Appendix 2 Data sets
512
B US Real Gross National Product GNP Annual data 191070
515
C Purses snatched in Hyde Park area of Chicago
516
D Rainfall in Fortaleza northeast Brazil
517
E International airline passengers OOOs
518
F Deaths and serious injuries in road accidents Great Britain Monthly data January 1969December 1984
519
G Tractors in Spain Annual data 195176
523
H Goals scored by England against Scotland in international football matches
524
I Employment OOOs and output 1980100 in UK manufacturing seasonally adjusted Quarterly data 1963Q11983Q3
525
J Mink and muskrat furs sold by Hudsons Bay Company Annual data 18481909
526
Selected answers to exercises
527
References
529
Author index
543
Subject index
547
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