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Statements

Subject Item
n2:RIV%2F00216224%3A14560%2F05%3A00031203%21RIV10-MSM-14560___
rdf:type
skos:Concept n16:Vysledek
dcterms: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 the techniques 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. 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 the techniques 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.
dcterms:title
Sparse Parameter Estimation in Economic Time Series Models Sparse Parameter Estimation in Economic Time Series Models
skos:prefLabel
Sparse Parameter Estimation in Economic Time Series Models Sparse Parameter Estimation in Economic Time Series Models
skos:notation
RIV/00216224:14560/05:00031203!RIV10-MSM-14560___
n3:aktivita
n9:S
n3:aktivity
S
n3:dodaniDat
n6:2010
n3:domaciTvurceVysledku
n17:1347411
n3:druhVysledku
n19:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
543774
n3:idVysledku
RIV/00216224:14560/05:00031203
n3:jazykVysledku
n7:eng
n3:klicovaSlova
sparse system; parameter estimation; overcomplete system; ARMA models; l1 norm optimization; stationary time series
n3:klicoveSlovo
n4:stationary%20time%20series n4:overcomplete%20system n4:l1%20norm%20optimization n4:parameter%20estimation n4:ARMA%20models n4:sparse%20system
n3:kontrolniKodProRIV
[480407E16E9F]
n3:mistoKonaniAkce
Hradec Králové
n3:mistoVydani
Hradec Králové
n3:nazevZdroje
Mathematical Methods in Economics 2005
n3:obor
n5:AH
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:rokUplatneniVysledku
n6:2005
n3:tvurceVysledku
Tonner, Jaromír
n3:typAkce
n15:CST
n3:wos
000260962400063
n3:zahajeniAkce
2005-01-01+01:00
s:numberOfPages
6
n18:hasPublisher
Gaudeamus, University of Hradec Králové
n12:isbn
80-7041-535-5
n14:organizacniJednotka
14560