Forecasting economic time series using locally stationary processes: a new approach with applications
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Frankfurt am Main
Peter Lang
2012
|
| Schriftenreihe: | Volkswirtschaftliche Analysen
Bd. 19 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Includes bibliographical references 1 Introduction; 2 From stationarity to local stationarity; 2.1 Stationary stochastic processes; 2.1.1 A short introduction to stationarity; 2.1.2 Spectral representation of stationary processes; 2.1.3 Stationary ARMA processes; 2.1.4 Asymptotical properties of the sample partial autocorrelations of a stationary AR(p) process; 2.2 Locally stationary processes; 2.2.1 Evolutionary spectrum; 2.2.2 Definition of local stationarity; 2.2.3 Local covariance estimation; 2.2.4 Local partial autocorrelation; 2.2.5 TVAR; 3 Estimation 3.1 Maximum likelihood estimation with the Kullback-Leibler information divergence3.2 Sieve estimation; 4 Forecasting; 4.1 Prediction in the case of stationarity; 4.2 Approaches to forecast time series using TVAR processes; 4.3 Iterative stages in the selection of a model; 4.4 Simulations; 5 Application; 5.1 Motivation; 5.2 Futures data; 5.2.1 Course of action; 5.2.2 Practical evaluation of TVAR processes on futures series; 5.3 Dow Jones index data; 6 Conclusion; 6.1 Contributions; 6.2 Possible directions for future research; References; Notations and abbreviations; List of tables List of figuresA Appendix; B GAUSS source code; B.1 Fitting time-varying autoregressive models to non-stationaryprocesses; B.2 Procedures for computing the coefficient functions |
| Beschreibung: | 1 Online-Ressource (138 pages :) |
| ISBN: | 9783653017069 3653017068 9783631621875 |
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| 500 | |a Includes bibliographical references | ||
| 500 | |a 1 Introduction; 2 From stationarity to local stationarity; 2.1 Stationary stochastic processes; 2.1.1 A short introduction to stationarity; 2.1.2 Spectral representation of stationary processes; 2.1.3 Stationary ARMA processes; 2.1.4 Asymptotical properties of the sample partial autocorrelations of a stationary AR(p) process; 2.2 Locally stationary processes; 2.2.1 Evolutionary spectrum; 2.2.2 Definition of local stationarity; 2.2.3 Local covariance estimation; 2.2.4 Local partial autocorrelation; 2.2.5 TVAR; 3 Estimation | ||
| 500 | |a 3.1 Maximum likelihood estimation with the Kullback-Leibler information divergence3.2 Sieve estimation; 4 Forecasting; 4.1 Prediction in the case of stationarity; 4.2 Approaches to forecast time series using TVAR processes; 4.3 Iterative stages in the selection of a model; 4.4 Simulations; 5 Application; 5.1 Motivation; 5.2 Futures data; 5.2.1 Course of action; 5.2.2 Practical evaluation of TVAR processes on futures series; 5.3 Dow Jones index data; 6 Conclusion; 6.1 Contributions; 6.2 Possible directions for future research; References; Notations and abbreviations; List of tables | ||
| 500 | |a List of figuresA Appendix; B GAUSS source code; B.1 Fitting time-varying autoregressive models to non-stationaryprocesses; B.2 Procedures for computing the coefficient functions | ||
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Datensatz im Suchindex
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|---|---|
| any_adam_object | |
| author | Loll, Tina |
| author_facet | Loll, Tina |
| author_role | aut |
| author_sort | Loll, Tina |
| author_variant | t l tl |
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| dewey-full | 330.015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.015195 |
| dewey-search | 330.015195 |
| dewey-sort | 3330.015195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| spelling | Loll, Tina Verfasser aut Forecasting economic time series using locally stationary processes a new approach with applications Tina Loll Frankfurt am Main Peter Lang 2012 1 Online-Ressource (138 pages :) txt rdacontent c rdamedia cr rdacarrier Volkswirtschaftliche Analysen Bd. 19 Includes bibliographical references 1 Introduction; 2 From stationarity to local stationarity; 2.1 Stationary stochastic processes; 2.1.1 A short introduction to stationarity; 2.1.2 Spectral representation of stationary processes; 2.1.3 Stationary ARMA processes; 2.1.4 Asymptotical properties of the sample partial autocorrelations of a stationary AR(p) process; 2.2 Locally stationary processes; 2.2.1 Evolutionary spectrum; 2.2.2 Definition of local stationarity; 2.2.3 Local covariance estimation; 2.2.4 Local partial autocorrelation; 2.2.5 TVAR; 3 Estimation 3.1 Maximum likelihood estimation with the Kullback-Leibler information divergence3.2 Sieve estimation; 4 Forecasting; 4.1 Prediction in the case of stationarity; 4.2 Approaches to forecast time series using TVAR processes; 4.3 Iterative stages in the selection of a model; 4.4 Simulations; 5 Application; 5.1 Motivation; 5.2 Futures data; 5.2.1 Course of action; 5.2.2 Practical evaluation of TVAR processes on futures series; 5.3 Dow Jones index data; 6 Conclusion; 6.1 Contributions; 6.2 Possible directions for future research; References; Notations and abbreviations; List of tables List of figuresA Appendix; B GAUSS source code; B.1 Fitting time-varying autoregressive models to non-stationaryprocesses; B.2 Procedures for computing the coefficient functions BUSINESS & ECONOMICS / Econometrics bisacsh BUSINESS & ECONOMICS / Statistics bisacsh Economic forecasting fast Stationary processes fast Statistik Wirtschaft Economic forecasting Stationary processes Nichtparametrisches Modell (DE-588)4434654-2 gnd rswk-swf Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Ökonometrisches Modell (DE-588)4043212-9 s Prognoseverfahren (DE-588)4358095-6 s Zeitreihenanalyse (DE-588)4067486-1 s Regressionsmodell (DE-588)4127980-3 s Nichtparametrisches Modell (DE-588)4434654-2 s 2\p DE-604 http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=488091 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Loll, Tina Forecasting economic time series using locally stationary processes a new approach with applications BUSINESS & ECONOMICS / Econometrics bisacsh BUSINESS & ECONOMICS / Statistics bisacsh Economic forecasting fast Stationary processes fast Statistik Wirtschaft Economic forecasting Stationary processes Nichtparametrisches Modell (DE-588)4434654-2 gnd Regressionsmodell (DE-588)4127980-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| subject_GND | (DE-588)4434654-2 (DE-588)4127980-3 (DE-588)4067486-1 (DE-588)4043212-9 (DE-588)4358095-6 (DE-588)4113937-9 |
| title | Forecasting economic time series using locally stationary processes a new approach with applications |
| title_auth | Forecasting economic time series using locally stationary processes a new approach with applications |
| title_exact_search | Forecasting economic time series using locally stationary processes a new approach with applications |
| title_full | Forecasting economic time series using locally stationary processes a new approach with applications Tina Loll |
| title_fullStr | Forecasting economic time series using locally stationary processes a new approach with applications Tina Loll |
| title_full_unstemmed | Forecasting economic time series using locally stationary processes a new approach with applications Tina Loll |
| title_short | Forecasting economic time series using locally stationary processes |
| title_sort | forecasting economic time series using locally stationary processes a new approach with applications |
| title_sub | a new approach with applications |
| topic | BUSINESS & ECONOMICS / Econometrics bisacsh BUSINESS & ECONOMICS / Statistics bisacsh Economic forecasting fast Stationary processes fast Statistik Wirtschaft Economic forecasting Stationary processes Nichtparametrisches Modell (DE-588)4434654-2 gnd Regressionsmodell (DE-588)4127980-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| topic_facet | BUSINESS & ECONOMICS / Econometrics BUSINESS & ECONOMICS / Statistics Economic forecasting Stationary processes Statistik Wirtschaft Nichtparametrisches Modell Regressionsmodell Zeitreihenanalyse Ökonometrisches Modell Prognoseverfahren Hochschulschrift |
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| work_keys_str_mv | AT lolltina forecastingeconomictimeseriesusinglocallystationaryprocessesanewapproachwithapplications |