La predicción de la recaudación por el impuesto sobre sociedades: La incidencia de los indicadores adelantados y los indicadores cualitativos en el marco de las series temporales

José Luis Barrón Basterrechea; Camino González Vasco; Raquel Pajares Rojo

doi:10.51302/rcyt.2019.3827

Authors

José Luis Barrón Basterrechea Cuerpo Superior de Sistemas y Tecnologías de la Información de la Administración del Estado. IEF (España)
Camino González Vasco Cuerpo Superior de Estadísticos de la Administración del Estado. IEF (España)
Raquel Pajares Rojo Cuerpo Superior de Técnicos Comerciales y Economistas del Estado. IEF (España)

DOI:

https://doi.org/10.51302/rcyt.2019.3827

Keywords:

ARX, forecast combination, CIT forecasting

Abstract

This paper contributes to the empirical literature on the development and estimation of a forecasting model for Corporate Income Tax (CIT) revenue focusing on the study of macroeconomic indicators. The proposed study uses a set of predictors comprising Central Bank Balance Sheet Data and soft indicators, along with some macroeconomic variables related to domestic demand and the foreign sector, which amounts to a total of 43 initial quarterly variables. To address the problem of multicollinearity, we use a blend of statistical methods such as Principal Components Analysis and ARX (Auto- Regressive with eXogenous input) models. Results of the backtesting exercises show that this combination successfully predicts the evolution of the CIT revenue and illustrate its usefulness as a tool for short-term CIT revenue forecasting.

Downloads

Download data is not yet available.

References

Auerbach, A. J. (1999). On the performance and use of government revenue forecasts. National Tax Journal, 767-782.

Bayer, O. (2015). Relevance of Input Data Time Series for Tax Revenue Forecasting. Procedia Economics and Finance, 25, 518-529.

Bischoff, I. y Gohout, W. (2010). The political economy of tax projections. International Tax and Public Finance, 17(2), 133-150.

Boylan, J. E.; Syntetos, A. A. y Karakostas, G. C. (2008). Classification for forecasting and stock control: a case study. Journal of the operational research society, 59(4), 473-481.

Bretschneider, S. I.; Gorr, W. L.; Grizzle, G. y Klay, E. (1989). Political and organizational influences on the accuracy of forecasting state government revenues. International Journal of Forecasting, 5(3), 307-319.

Buettner, T. y Kauder, B. (2010). Revenue forecasting practices: differences across countries and consequences for forecasting performance. Fiscal Studies, 31(3), 313-340.

Cassidy, G.; Kamlet, M. S. y Nagin, D. S. (1989). An empirical examination of bias in revenue forecasts by state governments. International Journal of Forecasting, 5(3), 321-331.

Chatagny, F. (2015). Incentive effects of fiscal rules on the finance minister’s behavior: Evidence from revenue projections in Swiss Cantons. European Journal of Political Economy, 39, 184-200.

Cooley, W. W. y Lohnes, P. R. (1971). Multivariate data analysis. J. Wiley.

Danninger, M. S. (2005). Revenue Forecasts as Performance Targets (EPub) (No. 5-14). International Monetary Fund.

Feenberg, D. R.; Gentry, W. M.; Gilroy, D. y Rosen, H. S. (1988). Testing the rationality of state revenue forecasts.

Gnanadesikan, R.; Kettenring, J. R. y Landwehr, J. M. (1977). Interpreting and assessing the results of cluster analyses. Bulletin of the International Statistical Institute, 47(2), 451-463.

Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6), 417.

Jochimsen, B. y Lehmann, R. (2017). On the political economy of national tax revenue forecasts: evidence from OECD countries. Public Choice, 170(3-4), 211-230.

Keene, M. y Thomson, P. (2007). An analysis of tax revenue forecast errors (No. 07/02). New Zealand Treasury.

Koester, G. B. y Priesmeier, C. (2017). Revenue elasticities in euro area countries. An analysis of long-run and short-run dynamics. ECB working paper No. 1989.

Kshirsagar, A. (1972). Likelihood ratio tests; optimality of principal components. Multivariate analysis, Marcel Dekker Inc. New-York.

Kyobe, A. y Danninger, M. S. (2005). Revenue forecasting: how is it done? Results from a survey of low-income countries (No. 5-24). International Monetary Fund.

Leal, T.; Pérez, J. J.; Tujula, M. y Vidal, J. P. (2008). Fiscal forecasting: lessons from the literature and challenges. Fiscal Studies, 29(3), 347-386.

Mardia, K. V.; Kent, J. T. y Bibby, J. M. (1979). Multivariate analysis, Probability and Mathematical Statistics: A Series of Monographs and Textbooks. Academic Press, Harcourt Brace Jovanovich Publishers.

Mikesell, J. L. y Ross, J. M. (2014). State revenue forecasts and political acceptance: The value of consensus forecasting in the budget process. Public Administration Review, 74(2), 188-203.

Morrison, D. G. (1976). Measurement problems in cluster analysis. Management science, 13(12), B-775.

Paleologou, S. M. (2005). Political manoeuvrings as sources of measurement errors in forecasts. Journal of Forecasting, 24(5), 311-324.

Pearson, K. (1901). Principal components analysis. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 6(2), 559.

Pike, T. y Savage, D. (1998). Forecasting the public finances in the Treasury. Fiscal Studies, 19(1), 49-62.

Rao, C. R. (1964). The use and interpretation of principal component analysis in applied research. Sankhya: The Indian Journal of Statistics, Series A, 329-358.

Wolswijk, G. (2007). Short-and long-run tax elasticities: the case of the Netherlands. ECB working paper No. 763.