Subscribe

Subscribe to our Newsletter and get informed about new publication regulary and special discounts for subscribers!

ILSHS > Volume 34 > Tax-Spend, Spend-Tax or Fiscal Synchronization...
Tax-Spend, Spend-Tax or Fiscal Synchronization Hypothesis: Evidence from Iran
< Back to Volume

Tax-Spend, Spend-Tax or Fiscal Synchronization Hypothesis: Evidence from Iran

Full Text PDF

Abstract:

The relationship between revenue and government expenditure is an important subject in public economics especially for Iran country, which is suffering from persistent budget deficits. From point of view of theoretical studies, there are essentially four schools of thought on the direction of causation between government expenditure and revenue. The main purpose of this study is to investigate the Long and short Run relationship between government revenue and government expenditure in Iran Country covering data 1978- 2012 with using An Auto Regressive Distributive Lag (ARDL) Approach. The Iranian economy has been subject to a multitude of structural changes and regime shifts during the sample period. First, time series properties of the data are first analysed by Augmented Dickey-Fuller (ADF), Zivot-Andrews and Lee – Strazicich (2003, 2004) model. The results of the ADF and Lee – Strazicich models indicate that all series under investigation are non-stationary at level. However, it is evident from the results of Augmented Dickey-Fuller and Lee – Strazicich tests that revenue and government expenditure are stationary at first difference because null hypotheses of unit roots for all the variables are rejected at 1 percent significance level then, we investigated causality between revenue and government expenditure by using an application of Toda-Yamamoto approach. Their evidence generally found unidirectional causality running from government revenue to government expenditure. So, these results consistent with the revenue-spend hypothesis. In the three stage, Autoregressive Distributeded Lag (ARDL) technique is used to describe both long run relationships and short run dynamic adjustments between government revenue and expenditure variables. The results of this paper support the Freidman (1978) hypothesis that government revenues cause expenditure and revenues have a positive causal impact on government expenditure.

Info:

Periodical:
International Letters of Social and Humanistic Sciences (Volume 34)
Pages:
1-20
DOI:
https://doi.org/10.18052/www.scipress.com/ILSHS.34.1
Citation:
A. A. Rezaei "Tax-Spend, Spend-Tax or Fiscal Synchronization Hypothesis: Evidence from Iran", International Letters of Social and Humanistic Sciences, Vol. 34, pp. 1-20, 2014
Online since:
July 2014
Authors:
Abbas Ali Rezaei
Keywords:
ARDL Technique, Iranian Economy, Structural Break, Toda-Yamamoto Approach
Export:
RIS, BibTeX, Text
Distribution:
Open Access

Creative Commons License
This work is licensed under a
Creative Commons Attribution 4.0 International License

References:

[1] Al-Yousif Y., Indian Economic Journal 48(2) (2000).

[2] Anderson W., M.S. Wallace J.T. Warner, Southern Economic Journal 52 (1986) 630-639.

[3] Baghestani H., R. McNown, Southern Economic Journal 60 (1994) 311-322.

[4] Cheng Benjamin S., Latic American Countries 23(2) (1999) 184-192.

[5] Furstenberg George M., von R. Jaffery Green, Jin Ho Jeong, The Review of Economics and Statistics 2 (1986) 179-188.

[6] Johansen S., Journal of Economic Dynamics and Control 12(1) (1988) 231-54.

[7] Johansen S., Juselius K., Oxford Bulletin of Economics and Statistics 52(2) (1990) 169-210.

[8] Joulfaian D., R. Mookerjee, Public Finance 45 (1990) 109-117.

[9] Kunitomo N., Japanese Economic Review 47(1) (1996) 79-109.

[10] Lee J., Strazicich M. C., Appalachian State University Working Papers, (2004) 04-17.

[11] Leybourne S. J., Newbold P., Applied Economics, 35(9) (2003) 1117-1121.

[12] Lütkepohl H., Wolters J., Dynamics 7 (2003) 711-733.

[13] Lutkepohl H. (2004). Vector Autoregressive and Vector Error Correction Model, in Lutkepohl, H. and M. Kratzig (ed. ), Applied Time Series econometrics, Cambridge University Press.

DOI: https://doi.org/10.1017/cbo9780511606885.004

[14] Owoye O., Applied Economic Letters 2 (1995) 19-22.

[15] Pahlavani M., International Journal of Applied Econometrics and Quantitative Studies 2-3 (2005).

[16] Pahlavani M., Wilson E. J., A. Valadkhani, International Journal of applied Business and Economic Research 4(1) (2006) 23-44.

[17] Peacock S. M., J. Wiseman, Public Finance Quarterly 7 (1979) 3-23.

[18] Perron P., Journal of Econometrics 80(2) (1997) 355-385.

[19] Ram R., Southern Economic Journal 54(3) (1988) 763-69.

[20] Saikkonen P., Lütkepohl H. (a), Econometric Theory 16(3) (2000) 373-406.

[21] Saikkonen P., Lütkepohl H. (b), Journal of Business and Economic Statistics 18(4) (2000) 451-464.

[22] Saikkonen P., Lütkepohl H. (c), Journal of Time Series Analysis 21 (2000) 435-456.

[23] Zivot E., Andrews D., Journal of Business and Economic Statistics 10 (1992) 251-70.

[23] Schwarz G., Annals of Statistics 6 (1978) 461-64.

[24] Zivot E., Andrews D. W. K., Journal of Business and Economic Statistics 10(3) (1992) 251-270. ( Received 19 June 2014; accepted 01 July 2014 ).

Show More Hide