Dynamic Stochastic General Equilibrium (DSGE) and Vector Autoregressive (VAR)
models allow for probabilistic estimations to formulate macroeconomic policies and monitor
them. One of the objectives of creating these models is to explain and understand financial fluc tuations through a consistent theoretical framework. In the tourism sector, stock price and sys temic risk are key financial variables in the international transmission of business cycles. Ad vances in Bayesian theory are providing an increasing range of tools that researchers can employ
to estimate and evaluate DSGE and VAR models. One area of interest in previous literature has
been to design a Bayesian robust filter, that performs well concerning an uncertainty class of
possible models compatible with prior knowledge. In this study, we propose to apply the Bayes ian Kalman Filter with Prior Update (BKPU) in a tourism field to increase the robustness of
DSGE and VAR models built for small samples and with irregular data. Our results indicate that
BKPU improves the estimation of these models in two aspects. Firstly, the accuracy levels of the
computing of the Markov Chain Monte Carlo model are increased, and secondly, the cost of the
resources used is reduced due to the need for a shorter run time. Our model can play an essential
role in the monetary policy process, as central bankers could use it to investigate the relative
importance of different macroeconomic shocks and the effects of tourism stock prices and
achieve a country´s international competitiveness and trade balance for this sector
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