Výsledky vyhledávání - "Unobserved components models"

Akademický článek

Automatic Identification and Forecasting of Structural Unobserved Components Models with UComp

Autor: Diego J. Pedregal

Publikováno v: Journal of Statistical Software, Vol 103, Pp 1-33 (2022)

UComp is a powerful library for building unobserved components models, useful for forecasting and other important operations, such us de-trending, cycle analysis, seasonal adjustment, signal extraction, etc. One of the most outstanding features that

Externí odkaz: https://doaj.org/article/e38eaf6734564ef4be8a5273de3ef70e

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Akademický článek

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Akademický článek

Forecasting uranium prices: Some empirical results

Autor: Diego J. Pedregal

Publikováno v: Nuclear Engineering and Technology, Vol 52, Iss 6, Pp 1334-1339 (2020)

This paper presents an empirical and comprehensive forecasting analysis of the uranium price. Prices are generally difficult to forecast, and the uranium price is not an exception because it is affected by many external factors, apart from imbalances

Externí odkaz: https://doaj.org/article/29cfb61c088045d596d2cba64cdd727f

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The drop in female homicide victimisation in Australia

Autor: Walter Forrest

Despite widespread community concern about the incidence of lethal violence against women in Australia, the numbers and rates of women murdered each year have been falling for almost 30 years. The risk of homicide victimisation among Australian women

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d79181d4d24fc86f02d05bb71f36d01

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Akademický článek

Measuring equilibrium models: a multivariate approach

Autor: Nadji RAHMANIA

Publikováno v: Revista Română de Statistică, Vol 59, Iss 03, Pp 66-77 (2011)

This paper presents a multivariate methodology for obtaining measures of unobserved macroeconomic variables. The used procedure is the multivariate Hodrick-Prescot which depends on smoothing param eters. The choice of these parameters is crucial. Our

Externí odkaz: https://doaj.org/article/83f0fa59a04548478758c1af917c31ea

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