Sparse Parameter Estimation in Economic Time Series Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F05%3A00031203" target="_blank" >RIV/00216224:14560/05:00031203 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Sparse Parameter Estimation in Economic Time Series Models

Popis výsledku v původním jazyce

The aim of this contribution is to study techniques and algorithms which are appropriate for modeling and analysis of data in economic models with a lot of parameters. So the aim is to reach a reduction of information underlying in data into the least possible number of parameters and to find their estimates with appropriately constructed and numerically stable algorithms. An attention will be devoted to predictions in economic time series and for estimation of parameters in models of small opened economics. An identification of redundant parameters and their displacement from the model will enable us an essential reduction of uncertainty of estimations of the rest of significant parameters. In this article we would like to explain and demonstrate thetechniques based on l1 optimization for the estimation of parameters in models of univariate time series ( ARIMA models ). We will use simulated data as well as real data.

Název v anglickém jazyce

Sparse Parameter Estimation in Economic Time Series Models

Popis výsledku anglicky

The aim of this contribution is to study techniques and algorithms which are appropriate for modeling and analysis of data in economic models with a lot of parameters. So the aim is to reach a reduction of information underlying in data into the least possible number of parameters and to find their estimates with appropriately constructed and numerically stable algorithms. An attention will be devoted to predictions in economic time series and for estimation of parameters in models of small opened economics. An identification of redundant parameters and their displacement from the model will enable us an essential reduction of uncertainty of estimations of the rest of significant parameters. In this article we would like to explain and demonstrate thetechniques based on l1 optimization for the estimation of parameters in models of univariate time series ( ARIMA models ). We will use simulated data as well as real data.

Druh

D - Stať ve sborníku

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2005

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Mathematical Methods in Economics 2005

ISBN

80-7041-535-5

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

Gaudeamus, University of Hradec Králové

Místo vydání

Hradec Králové

Místo konání akce

Hradec Králové

Datum konání akce

1. 1. 2005

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000260962400063