EconPapers    
Economics at your fingertips  
 

Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview

Daniel Kosiorowski, Dominik Mielczarek and Jerzy. P. Rydlewski

Papers from arXiv.org

Abstract: In economics we often face a system, which intrinsically imposes a structure of hierarchy of its components, i.e., in modelling trade accounts related to foreign exchange or in optimization of regional air protection policy. A problem of reconciliation of forecasts obtained on different levels of hierarchy has been addressed in the statistical and econometric literature for many times and concerns bringing together forecasts obtained independently at different levels of hierarchy. This paper deals with this issue in case of a hierarchical functional time series. We present and critically discuss a state of art and indicate opportunities of an application of these methods to a certain environment protection problem. We critically compare the best predictor known from the literature with our own original proposal. Within the paper we study a macromodel describing a day and night air pollution in Silesia region divided into five subregions.

New Economics Papers: this item is included in nep-ene, nep-env and nep-for
Date: 2017-11
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Published in Central European Journal of Economic Modelling and Econometrics, 10: 53-73 (2018)

Downloads: (external link)
http://arxiv.org/pdf/1712.03797 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1712.03797

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2018-04-04
Handle: RePEc:arx:papers:1712.03797