Ex post and ex ante prediction of unobserved multivariate time series: a structural‐model based approach

Nieto, Fabio H.

doi:10.1002/for.1017

Cited by 4 publications

(1 citation statement)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nunes (2005) has proposed a procedure which provides estimates of the unobserved common coincident component, of the unobserved monthly series underlying any included quarterly indicator, and of any missing values in the series. Nieto (2007) has presented a methodology for estimating high-frequency values of an unobserved multivariate time series from low-frequency values and related information to it.…”

Section: Introductionmentioning

confidence: 99%

Related‐variables selection in temporal disaggregation

Fukuda

2008

Journal of Forecasting

View full text Add to dashboard Cite

Two related-variables selection methods for temporal disaggregation are proposed. In the first method, the hypothesis tests for a common feature (cointegration or serial correlation) are first performed. If there is a common feature between observed aggregated series and related variables, the conventional Chow-Lin procedure is applied. In the second method, alternative Chow-Lin disaggregating models with and without related variables are first estimated and the corresponding values of the Bayesian information criterion (BIC) are stored. It is determined on the basis of the selected model whether related variables should be included in the Chow-Lin model. The efficacy of these methods is examined via simulations and empirical applications. Copyright © 2008 John Wiley & Sons, Ltd.

show abstract

Section: Introductionmentioning

confidence: 99%

Related‐variables selection in temporal disaggregation

Fukuda

2008

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

Temporal disaggregation and restricted forecasting of multiple population time series

Silva

Guerrero

Peña

2011

Journal of Applied Statistics

View full text Add to dashboard Cite

This article presents some applications of time-series procedures to solve two typical problems that arise when analyzing demographic information in developing countries: (1) unavailability of annual time series of population growth rates (PGRs) and their corresponding population time series and (2) inappropriately defined population growth goals in official population programs. These problems are considered as situations that require combining information of population time series. Firstly, we suggest the use of temporal disaggregation techniques to combine census data with vital statistics information in order to estimate annual PGRs. Secondly, we apply multiple restricted forecasting to combine the official targets on future PGRs with the disaggregated series. Then, we propose a mechanism to evaluate the compatibility of the demographic goals with the annual data. We apply the aforementioned procedures to data of the Mexico City Metropolitan Zone divided by concentric rings and conclude that the targets established in the official program are not feasible. Hence, we derive future PGRs that are both in line with the official targets and with the historical demographic behavior. We conclude that growth population programs should be based on this kind of analysis to be supported empirically. So, through specialized multivariate time-series techniques, we propose to obtain first an optimal estimate of a disaggregate vector of population time series and then, produce restricted forecasts in agreement with some data-based population policies here derived.

show abstract

Dengue prediction model: A systematic review using social network analysis

Wahyuni¹,

Sediyono

Sembiring

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Since 1968, Dengue Harmonic Fever’s incidence in Indonesia has continued to rise and has become a public health issue. Indonesia has the largest number of Dengue Harmonic Fever cases than 30 other epidemic countries worldwide. It is very important to carry out research related to dengue cases’ prediction to prevent the spread of Dengue. This literature review is intended to determine the extent of the dengue prediction approach carried out by previous researchers, and a research gap will be obtained. The algorithm used to cluster articles is a modularity algorithm, using several open-source tools to process data. The online databases used are Google Scholar and Crossref by using keywords: journal, algorithm, prediction, and Dengue. The data are taken from the expansion of 1928-2020. This study’s results are 200 articles that are suitable and divided into four clusters of important articles. Also, several important parameters were obtained in the prediction study of dengue fever, namely humidity, temperature, rainfall, and population density.

show abstract

Ex post and ex ante prediction of unobserved multivariate time series: a structural‐model based approach

Cited by 4 publications

References 13 publications

Related‐variables selection in temporal disaggregation

Related‐variables selection in temporal disaggregation

Temporal disaggregation and restricted forecasting of multiple population time series

Dengue prediction model: A systematic review using social network analysis

Contact Info

Product

Resources

About