Election forecasting in Lithuania: The case of municipal elections
Mažvydas Jastramskis
International Journal of Forecasting, 2012, vol. 28, issue 4, 822-829
Abstract:
This article explores the possibilities for election forecasting in Lithuania, a post-communist country that has a party system which is characterized by high levels of electoral volatility and fragmentation. The main argument of the article is that despite these unfavorable conditions, election forecasting in Lithuania has potential. Since the sample of national parliamentary elections is too limited for statistical modeling, the possibility of forecasting at the level of municipalities in the municipal council elections is discussed. Four factors (local unemployment change, party’s belonging to the national government, population size and lagged vote share) are integrated into a model that strives to predict the vote share of the party that holds the mayor’s post (the dominant political power in the municipal council). The model presented explains more than 70% of the variance in the dependent variable. The case diagnostics reveal that the model predicts municipal election outcomes very accurately.
Keywords: Municipal elections; Forecasting; Lithuania; Mayor’s party (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207012000519
Full text for ScienceDirect subscribers only
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:eee:intfor:v:28:y:2012:i:4:p:822-829
DOI: 10.1016/j.ijforecast.2012.05.003
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().