An illustration of the causality relation between government spending and revenue using wavelet analysis on Finnish data
(2003) In Journal of Applied Statistics 30(5). p.571-584- Abstract
- Quarterly data for the period 1960: 1 to 1997: 2, conventional tests, a bootstrap simulation approach and a multivariate Rao's F-test have been used to investigate if the causality between government spending and revenue in Finland was changed at the beginning of 1990 due to future plans to create the European Monetary Union (EMU). The results indicate that during the period before 1990, the government revenue Granger-caused spending, while the opposite happened after 1990, which agrees better with Barro's tax smoothing hypothesis. However, when using monthly data instead of quarterly data for almost the same sample period, totally different results have been noted. The general conclusion is that the relationship between spending and... (More)
- Quarterly data for the period 1960: 1 to 1997: 2, conventional tests, a bootstrap simulation approach and a multivariate Rao's F-test have been used to investigate if the causality between government spending and revenue in Finland was changed at the beginning of 1990 due to future plans to create the European Monetary Union (EMU). The results indicate that during the period before 1990, the government revenue Granger-caused spending, while the opposite happened after 1990, which agrees better with Barro's tax smoothing hypothesis. However, when using monthly data instead of quarterly data for almost the same sample period, totally different results have been noted. The general conclusion is that the relationship between spending and revenue in Finland is still not completely understood. The ambiguity of these results may well be due to the fact that there are several time scales involved in the relationship, and that the conventional analyses may be inadequate to separate out the time scale structured relationships between these variables. Therefore, to investigate empirically the relation between these variables we attempt to use the wavelets analysis that enables us to separate out different time scales of variation in the data. We find that time scale decomposition is important for analysing these economic variables. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/315181
- author
- Almasri, Abdullah LU and Shukur, Ghazi
- organization
- publishing date
- 2003
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Applied Statistics
- volume
- 30
- issue
- 5
- pages
- 571 - 584
- publisher
- Carfax Publishing
- external identifiers
-
- wos:000181842100007
- scopus:0037409940
- ISSN
- 0266-4763
- DOI
- 10.1080/0266476032000053682
- language
- English
- LU publication?
- yes
- id
- bbaaeb15-edaf-4d1c-9903-8f723468dbe3 (old id 315181)
- date added to LUP
- 2016-04-01 16:45:19
- date last changed
- 2022-01-28 21:55:51
@article{bbaaeb15-edaf-4d1c-9903-8f723468dbe3,
abstract = {{Quarterly data for the period 1960: 1 to 1997: 2, conventional tests, a bootstrap simulation approach and a multivariate Rao's F-test have been used to investigate if the causality between government spending and revenue in Finland was changed at the beginning of 1990 due to future plans to create the European Monetary Union (EMU). The results indicate that during the period before 1990, the government revenue Granger-caused spending, while the opposite happened after 1990, which agrees better with Barro's tax smoothing hypothesis. However, when using monthly data instead of quarterly data for almost the same sample period, totally different results have been noted. The general conclusion is that the relationship between spending and revenue in Finland is still not completely understood. The ambiguity of these results may well be due to the fact that there are several time scales involved in the relationship, and that the conventional analyses may be inadequate to separate out the time scale structured relationships between these variables. Therefore, to investigate empirically the relation between these variables we attempt to use the wavelets analysis that enables us to separate out different time scales of variation in the data. We find that time scale decomposition is important for analysing these economic variables.}},
author = {{Almasri, Abdullah and Shukur, Ghazi}},
issn = {{0266-4763}},
language = {{eng}},
number = {{5}},
pages = {{571--584}},
publisher = {{Carfax Publishing}},
series = {{Journal of Applied Statistics}},
title = {{An illustration of the causality relation between government spending and revenue using wavelet analysis on Finnish data}},
url = {{http://dx.doi.org/10.1080/0266476032000053682}},
doi = {{10.1080/0266476032000053682}},
volume = {{30}},
year = {{2003}},
}