Conditional forecasting of Peruvian inflation
A Bayesian approach
DOI:
https://doi.org/10.15381/pc.v27i1.23282Keywords:
Bayesian VAR, Forecasts, Monetary Policy, InflationAbstract
This article shows the application of a conditional VAR model in order to estimate the future path of Peruvian inflation conditioned to US inflation based on 3 possible scenarios: optimistic, average and pessimistic. For the Peruvian case, the methodology developed by Waggoner and Zha (1999) is incorporated, under Bayesian estimations and using the Gibss Sampling algorithm to estimate and simulate the forecast distributions. The results show that for the year 2022 and in a pessimistic scenario, Peruvian inflation would reach its highest level in June. In an average scenario, the highest level of inflation would be reached in April, while in an optimistic scenario, it would reach a maximum level in March. Additionally, it is observed that the difference in average Peruvian inflation from one scenario to another is around 0.2% per month.
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Copyright (c) 2022 Kevin Antonio Álvarez García, Raúl Velita Zorrilla
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