Sparse Parameter Estimation in Economic Time Series Models

Result code in 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>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Sparse Parameter Estimation in Economic Time Series Models
Original language description
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.
Czech name
—
Czech description
—

Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)