Forecasting Corporate Income Tax Revenues: the incidence of leading and soft indicators in a time series framework

• MARIA DEL CAMINO GONZÁLEZ VASCO ; RAQUEL PAJARES ROJO ^[1]
  1. [1] Institute for Fiscal Studies (Spain)
• Localización: Papeles de trabajo del Instituto de Estudios Fiscales. Serie economía, ISSN 1578-0252, Nº 2, 2019, págs. 1-18
• Idioma: inglés
• Enlaces
  • Texto completo (pdf)
• Resumen

  • 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.