Forecasting, structural time series models, and the Kalman filter /:
This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York :
Cambridge University Press,
1989.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose. |
| Beschreibung: | 1 online resource (xvi, 554 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 529-542) and indexes. |
| ISBN: | 9781107720039 1107720036 9781107049994 1107049997 9781107715905 1107715903 9781107714557 1107714559 |
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| 100 | 1 | |a Harvey, A. C. |q (Andrew C.) |1 https://id.oclc.org/worldcat/entity/E39PBJxxj79qDhWGrRQTFHdbBP |0 http://id.loc.gov/authorities/names/n81064640 | |
| 245 | 1 | 0 | |a Forecasting, structural time series models, and the Kalman filter / |c Andrew Harvey. |
| 264 | 1 | |a Cambridge ; |a New York : |b Cambridge University Press, |c 1989. | |
| 300 | |a 1 online resource (xvi, 554 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references (pages 529-542) and indexes. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Half Title; TitlePage; Copyright; Contents; List of figures; Acknowledgement; Preface; Notation and conventions; List of abbreviations; 1 Introduction; 1.1 The nature of time series; 1.2 Explanatory variables and intervention analysis; 1.3 Multivariate models; 1.4 Statistical treatment; 1.5 Modelling methodology; 1.6 Forecasting; 1.7 Computer software; 2 Univariate time series models; 2.1 Introduction; 2.2 Ad hoc forecasting procedures; 2.3 The structure of time series models; 2.4 Stochastic properties; 2.5 ARIMA models and the reduced form; 2.6 ARIMA modelling; 2.7 Applications. | |
| 505 | 8 | |a Exercises3 State space models and the Kalman filter; 3.1 The state space form; 3.2 The Kalman filter; 3.3 Properties of time-invariant models; 3.4 Maximum likelihood estimation and the prediction errordecomposition; 3.5 Prediction; 3.6 Smoothing; 3.7 Non-linearity and non-normality; Appendix. Properties of the multivariate normal distribution; Exercises; 4 Estimation, prediction and smoothing for univariate structuraltime series models; 4.1 Application of the Kalman filter; 4.2 Estimation in the time domain; 4.3 Estimation in the frequency domain; 4.4 Identifiability. | |
| 505 | 8 | |a 4.5 Properties of estimators4.6 Prediction; 4.7 Estimation of components; Exercises; 5 Testing and model selection; 5.1 Principles of testing; 5.2 Lagrange multiplier tests; 5.3 Tests of specification for structural models; 5.4 Diagnostics; 5.5 Goodness of fit; 5.6 Post-sample predictive testing and model evaluation; 5.7 Strategy for model selection; Exercises; 6 Extensions of the univariate model; 6.1 Trends, detrending and unit roots; 6.2 Seasonality and seasonal adjustment; 6.3 Different timing intervals for the model and observations; 6.4 Data irregularities. | |
| 505 | 8 | |a 6.5 Time-varyingand non-linear models6.6 Non-normality, count data and qualitative observations; Exercises; 7 Explanatory variables; 7.1 Introduction; 7.2 Estimation in the frequency domain; 7.3 Estimation of models with explanatory variables andstructural time series components; 7.4 Tests and measures of goodness of fit; 7.5 Model selection strategy and applications; 7.6 Intervention analysis; 7.7 Time-varying parameters; 7.8 Instrumental variables; 7.9 Count data; Exercises; 8 Multivariate models; 8.1 Stochastic properties of multivariate models. | |
| 505 | 8 | |a 8.2 Seemingly unrelated time series equations8.3 Homogeneous systems; 8.4 Testing and model selection; 8.5 Dynamic factor analysis; 8.6 Intervention analysis with control groups; 8.7 Missing observations, delayed observations and contemporaneousaggregation; 8.8 Vector autoregressive models; 8.9 Simultaneous equation models; Exercises; 9 Continuous time; 9.1 Introduction; 9.2 Stock variables; 9.3 Flow variables; 9.4 Multivariate models; Appendix 1 Principal structural time series components and models; Appendix 2 Data sets; A. Energy demand of Other Final Users. | |
| 520 | |a This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose. | ||
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
| 650 | 0 | |a Kalman filtering. |0 http://id.loc.gov/authorities/subjects/sh85071360 | |
| 650 | 6 | |a Série chronologique. | |
| 650 | 6 | |a Filtre de Kalman. | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Techniques de prévision. |2 eclas | |
| 650 | 7 | |a Modèles économétriques. |2 eclas | |
| 650 | 7 | |a Kalman filtering |2 fast | |
| 650 | 7 | |a Time-series analysis |2 fast | |
| 650 | 7 | |a Kalman-Filter |2 gnd | |
| 650 | 7 | |a Zeitreihenanalyse |2 gnd |0 http://d-nb.info/gnd/4067486-1 | |
| 650 | 1 | 7 | |a Tijdreeksen. |2 gtt |
| 650 | 1 | 7 | |a Kalman-filters. |2 gtt |
| 650 | 1 | 7 | |a Modellen. |2 gtt |
| 650 | 1 | 7 | |a Prognoses. |2 gtt |
| 650 | 7 | |a Série chronologique. |2 ram | |
| 650 | 7 | |a Kalman, filtrage de. |2 ram | |
| 655 | 0 | |a Electronic books. | |
| 655 | 4 | |a Electronic books. | |
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| contents | Cover; Half Title; TitlePage; Copyright; Contents; List of figures; Acknowledgement; Preface; Notation and conventions; List of abbreviations; 1 Introduction; 1.1 The nature of time series; 1.2 Explanatory variables and intervention analysis; 1.3 Multivariate models; 1.4 Statistical treatment; 1.5 Modelling methodology; 1.6 Forecasting; 1.7 Computer software; 2 Univariate time series models; 2.1 Introduction; 2.2 Ad hoc forecasting procedures; 2.3 The structure of time series models; 2.4 Stochastic properties; 2.5 ARIMA models and the reduced form; 2.6 ARIMA modelling; 2.7 Applications. Exercises3 State space models and the Kalman filter; 3.1 The state space form; 3.2 The Kalman filter; 3.3 Properties of time-invariant models; 3.4 Maximum likelihood estimation and the prediction errordecomposition; 3.5 Prediction; 3.6 Smoothing; 3.7 Non-linearity and non-normality; Appendix. Properties of the multivariate normal distribution; Exercises; 4 Estimation, prediction and smoothing for univariate structuraltime series models; 4.1 Application of the Kalman filter; 4.2 Estimation in the time domain; 4.3 Estimation in the frequency domain; 4.4 Identifiability. 4.5 Properties of estimators4.6 Prediction; 4.7 Estimation of components; Exercises; 5 Testing and model selection; 5.1 Principles of testing; 5.2 Lagrange multiplier tests; 5.3 Tests of specification for structural models; 5.4 Diagnostics; 5.5 Goodness of fit; 5.6 Post-sample predictive testing and model evaluation; 5.7 Strategy for model selection; Exercises; 6 Extensions of the univariate model; 6.1 Trends, detrending and unit roots; 6.2 Seasonality and seasonal adjustment; 6.3 Different timing intervals for the model and observations; 6.4 Data irregularities. 6.5 Time-varyingand non-linear models6.6 Non-normality, count data and qualitative observations; Exercises; 7 Explanatory variables; 7.1 Introduction; 7.2 Estimation in the frequency domain; 7.3 Estimation of models with explanatory variables andstructural time series components; 7.4 Tests and measures of goodness of fit; 7.5 Model selection strategy and applications; 7.6 Intervention analysis; 7.7 Time-varying parameters; 7.8 Instrumental variables; 7.9 Count data; Exercises; 8 Multivariate models; 8.1 Stochastic properties of multivariate models. 8.2 Seemingly unrelated time series equations8.3 Homogeneous systems; 8.4 Testing and model selection; 8.5 Dynamic factor analysis; 8.6 Intervention analysis with control groups; 8.7 Missing observations, delayed observations and contemporaneousaggregation; 8.8 Vector autoregressive models; 8.9 Simultaneous equation models; Exercises; 9 Continuous time; 9.1 Introduction; 9.2 Stock variables; 9.3 Flow variables; 9.4 Multivariate models; Appendix 1 Principal structural time series components and models; Appendix 2 Data sets; A. Energy demand of Other Final Users. |
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| discipline | Mathematik Wirtschaftswissenschaften |
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| genre | Electronic books. |
| genre_facet | Electronic books. |
| id | ZDB-4-EBA-ocn871172601 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:25:49Z |
| institution | BVB |
| isbn | 9781107720039 1107720036 9781107049994 1107049997 9781107715905 1107715903 9781107714557 1107714559 |
| language | English |
| oclc_num | 871172601 |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvi, 554 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 1989 |
| publishDateSearch | 1990 |
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| publisher | Cambridge University Press, |
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| spelling | Harvey, A. C. (Andrew C.) https://id.oclc.org/worldcat/entity/E39PBJxxj79qDhWGrRQTFHdbBP http://id.loc.gov/authorities/names/n81064640 Forecasting, structural time series models, and the Kalman filter / Andrew Harvey. Cambridge ; New York : Cambridge University Press, 1989. 1 online resource (xvi, 554 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references (pages 529-542) and indexes. Print version record. Cover; Half Title; TitlePage; Copyright; Contents; List of figures; Acknowledgement; Preface; Notation and conventions; List of abbreviations; 1 Introduction; 1.1 The nature of time series; 1.2 Explanatory variables and intervention analysis; 1.3 Multivariate models; 1.4 Statistical treatment; 1.5 Modelling methodology; 1.6 Forecasting; 1.7 Computer software; 2 Univariate time series models; 2.1 Introduction; 2.2 Ad hoc forecasting procedures; 2.3 The structure of time series models; 2.4 Stochastic properties; 2.5 ARIMA models and the reduced form; 2.6 ARIMA modelling; 2.7 Applications. Exercises3 State space models and the Kalman filter; 3.1 The state space form; 3.2 The Kalman filter; 3.3 Properties of time-invariant models; 3.4 Maximum likelihood estimation and the prediction errordecomposition; 3.5 Prediction; 3.6 Smoothing; 3.7 Non-linearity and non-normality; Appendix. Properties of the multivariate normal distribution; Exercises; 4 Estimation, prediction and smoothing for univariate structuraltime series models; 4.1 Application of the Kalman filter; 4.2 Estimation in the time domain; 4.3 Estimation in the frequency domain; 4.4 Identifiability. 4.5 Properties of estimators4.6 Prediction; 4.7 Estimation of components; Exercises; 5 Testing and model selection; 5.1 Principles of testing; 5.2 Lagrange multiplier tests; 5.3 Tests of specification for structural models; 5.4 Diagnostics; 5.5 Goodness of fit; 5.6 Post-sample predictive testing and model evaluation; 5.7 Strategy for model selection; Exercises; 6 Extensions of the univariate model; 6.1 Trends, detrending and unit roots; 6.2 Seasonality and seasonal adjustment; 6.3 Different timing intervals for the model and observations; 6.4 Data irregularities. 6.5 Time-varyingand non-linear models6.6 Non-normality, count data and qualitative observations; Exercises; 7 Explanatory variables; 7.1 Introduction; 7.2 Estimation in the frequency domain; 7.3 Estimation of models with explanatory variables andstructural time series components; 7.4 Tests and measures of goodness of fit; 7.5 Model selection strategy and applications; 7.6 Intervention analysis; 7.7 Time-varying parameters; 7.8 Instrumental variables; 7.9 Count data; Exercises; 8 Multivariate models; 8.1 Stochastic properties of multivariate models. 8.2 Seemingly unrelated time series equations8.3 Homogeneous systems; 8.4 Testing and model selection; 8.5 Dynamic factor analysis; 8.6 Intervention analysis with control groups; 8.7 Missing observations, delayed observations and contemporaneousaggregation; 8.8 Vector autoregressive models; 8.9 Simultaneous equation models; Exercises; 9 Continuous time; 9.1 Introduction; 9.2 Stock variables; 9.3 Flow variables; 9.4 Multivariate models; Appendix 1 Principal structural time series components and models; Appendix 2 Data sets; A. Energy demand of Other Final Users. This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose. Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Kalman filtering. http://id.loc.gov/authorities/subjects/sh85071360 Série chronologique. Filtre de Kalman. MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Techniques de prévision. eclas Modèles économétriques. eclas Kalman filtering fast Time-series analysis fast Kalman-Filter gnd Zeitreihenanalyse gnd http://d-nb.info/gnd/4067486-1 Tijdreeksen. gtt Kalman-filters. gtt Modellen. gtt Prognoses. gtt Série chronologique. ram Kalman, filtrage de. ram Electronic books. Print version: Harvey, A.C. (Andrew C.). Forecasting, structural time series models, and the Kalman filter 9780521405737 (DLC) 89031417 (OCoLC)19458552 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=676270 Volltext |
| spellingShingle | Harvey, A. C. (Andrew C.) Forecasting, structural time series models, and the Kalman filter / Cover; Half Title; TitlePage; Copyright; Contents; List of figures; Acknowledgement; Preface; Notation and conventions; List of abbreviations; 1 Introduction; 1.1 The nature of time series; 1.2 Explanatory variables and intervention analysis; 1.3 Multivariate models; 1.4 Statistical treatment; 1.5 Modelling methodology; 1.6 Forecasting; 1.7 Computer software; 2 Univariate time series models; 2.1 Introduction; 2.2 Ad hoc forecasting procedures; 2.3 The structure of time series models; 2.4 Stochastic properties; 2.5 ARIMA models and the reduced form; 2.6 ARIMA modelling; 2.7 Applications. Exercises3 State space models and the Kalman filter; 3.1 The state space form; 3.2 The Kalman filter; 3.3 Properties of time-invariant models; 3.4 Maximum likelihood estimation and the prediction errordecomposition; 3.5 Prediction; 3.6 Smoothing; 3.7 Non-linearity and non-normality; Appendix. Properties of the multivariate normal distribution; Exercises; 4 Estimation, prediction and smoothing for univariate structuraltime series models; 4.1 Application of the Kalman filter; 4.2 Estimation in the time domain; 4.3 Estimation in the frequency domain; 4.4 Identifiability. 4.5 Properties of estimators4.6 Prediction; 4.7 Estimation of components; Exercises; 5 Testing and model selection; 5.1 Principles of testing; 5.2 Lagrange multiplier tests; 5.3 Tests of specification for structural models; 5.4 Diagnostics; 5.5 Goodness of fit; 5.6 Post-sample predictive testing and model evaluation; 5.7 Strategy for model selection; Exercises; 6 Extensions of the univariate model; 6.1 Trends, detrending and unit roots; 6.2 Seasonality and seasonal adjustment; 6.3 Different timing intervals for the model and observations; 6.4 Data irregularities. 6.5 Time-varyingand non-linear models6.6 Non-normality, count data and qualitative observations; Exercises; 7 Explanatory variables; 7.1 Introduction; 7.2 Estimation in the frequency domain; 7.3 Estimation of models with explanatory variables andstructural time series components; 7.4 Tests and measures of goodness of fit; 7.5 Model selection strategy and applications; 7.6 Intervention analysis; 7.7 Time-varying parameters; 7.8 Instrumental variables; 7.9 Count data; Exercises; 8 Multivariate models; 8.1 Stochastic properties of multivariate models. 8.2 Seemingly unrelated time series equations8.3 Homogeneous systems; 8.4 Testing and model selection; 8.5 Dynamic factor analysis; 8.6 Intervention analysis with control groups; 8.7 Missing observations, delayed observations and contemporaneousaggregation; 8.8 Vector autoregressive models; 8.9 Simultaneous equation models; Exercises; 9 Continuous time; 9.1 Introduction; 9.2 Stock variables; 9.3 Flow variables; 9.4 Multivariate models; Appendix 1 Principal structural time series components and models; Appendix 2 Data sets; A. Energy demand of Other Final Users. Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Kalman filtering. http://id.loc.gov/authorities/subjects/sh85071360 Série chronologique. Filtre de Kalman. MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Techniques de prévision. eclas Modèles économétriques. eclas Kalman filtering fast Time-series analysis fast Kalman-Filter gnd Zeitreihenanalyse gnd http://d-nb.info/gnd/4067486-1 Tijdreeksen. gtt Kalman-filters. gtt Modellen. gtt Prognoses. gtt Série chronologique. ram Kalman, filtrage de. ram |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85135430 http://id.loc.gov/authorities/subjects/sh85071360 http://d-nb.info/gnd/4067486-1 |
| title | Forecasting, structural time series models, and the Kalman filter / |
| title_auth | Forecasting, structural time series models, and the Kalman filter / |
| title_exact_search | Forecasting, structural time series models, and the Kalman filter / |
| title_full | Forecasting, structural time series models, and the Kalman filter / Andrew Harvey. |
| title_fullStr | Forecasting, structural time series models, and the Kalman filter / Andrew Harvey. |
| title_full_unstemmed | Forecasting, structural time series models, and the Kalman filter / Andrew Harvey. |
| title_short | Forecasting, structural time series models, and the Kalman filter / |
| title_sort | forecasting structural time series models and the kalman filter |
| topic | Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Kalman filtering. http://id.loc.gov/authorities/subjects/sh85071360 Série chronologique. Filtre de Kalman. MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Techniques de prévision. eclas Modèles économétriques. eclas Kalman filtering fast Time-series analysis fast Kalman-Filter gnd Zeitreihenanalyse gnd http://d-nb.info/gnd/4067486-1 Tijdreeksen. gtt Kalman-filters. gtt Modellen. gtt Prognoses. gtt Série chronologique. ram Kalman, filtrage de. ram |
| topic_facet | Time-series analysis. Kalman filtering. Série chronologique. Filtre de Kalman. MATHEMATICS Applied. MATHEMATICS Probability & Statistics General. Techniques de prévision. Modèles économétriques. Kalman filtering Time-series analysis Kalman-Filter Zeitreihenanalyse Tijdreeksen. Kalman-filters. Modellen. Prognoses. Kalman, filtrage de. Electronic books. |
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| work_keys_str_mv | AT harveyac forecastingstructuraltimeseriesmodelsandthekalmanfilter |