Estimating the direct tax multipliers in Iran's economy: The application of the Time-Varying Parameters Vector Autoregression (TVP-VAR) model

Document Type : Scientific paper

Authors

1 PhD student, Department of Economics, Urmia Branch, Islamic Azad University, Urmia, Iran

2 Assistant Professor, Department of Economics, Urmia Branch, Islamic Azad University, Urmia, Iran

3 Assistant Professor, Department of Economics, Salmas Branch, Islamic Azad University, Salmas, Iran

10.22080/mrl.2024.27054.2074

Abstract

The experimental studies done in the field of estimating the multiplier of demand side policies, have shown that the multiplier of fiscal policy has not been fixed, rather has been changing over time. From a theoretical perspective this coefficient is influenced by different factors such that with change of every factor the mentioned coefficient would change. TVP-VAR models have the ability to estimate the response of a variable due to the shock introduced by other variables over time. In this study, using quarterly data from the period 1990 to 2021 in Iran and employing the Time-Varying Parameter Vector Autoregression (TVP-VAR) model with the factor-augmented approach, first of all, the combined and latent index of monetary policy and impulse respons function was extracted. Then the multipliers of personal taxes, corporate taxes and property tax were calculated in the form of time-varying estimations. In the third stage, using the variance analysis technique, the factors effective on multiplier of direct tax policies were ascertained. The results indicated that from among 3 tools for direct tax policies, the greatest multiplier was related to corporate taxes. Furthermore, the imports to GDP and the savings to GDP ratios have the highest efficiency and explanatory power of multiplier fluctuations of personal taxes.

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Macroeconomics Research Letter

Articles in Press, Accepted Manuscript
Available Online from 08 July 2024

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History

  • Receive Date: 28 April 2024
  • Revise Date: 13 May 2024
  • Accept Date: 29 June 2024

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