Bayesian Approach to Forecasting Aggregate Taxes of the Republic of Armenia

This paper is devoted to the application of the Bayesian approach to the forecasting of aggregate taxes on the example of the Republic of Armenia. Typically, this approach is used in large-scale BVARs to forecast macroeconomic variables. The objective of this study is to estimate the efficiency of the Bayesian approach to constricting relatively low-scale fiscal VARs. Another objective is to build a specific BVAR model for forecasting tax revenues in the context of actual forecasting rounds. The study is based on seasonally adjusted quarterly aggregate tax data and the corresponding proxy bases. A hierarchical approach to the selection of BVAR’s priors is implemented. It assumes the random nature of variances in the prior values of the coefficients. The hierarchical approach is also characterized by a high level of variability of hyperparameters. To determine the optimal structure of the BVAR model in terms of out-of-sample prediction accuracy, a special algorithm was developed. This algorithm involves a specific procedure for the selection of priors and model parameters, which allows to significantly minimize the prediction error. The Geweke and Gelman-Rubin tests were used/considered to check the convergence of the parameters, and the acceptance rate of the Metropolis-Hastings algorithm was taken into account. It Additional priors, such as the sum-of-coefficients prior and the dummy-initialobservation prior (single-unit-root), are shown to improve the quality of out-of-sample forecasts. These priors allow for the possibility of the existence of a single root and cointegration between variables. The main finding of this study is that the proposed algorithm for selecting parameters in BVAR significantly improves out-of-sample performance compared to traditional frequency VAR.