Forecasting Peruvian Monetary Aggregates in a Nonlinear and Uncertain Environment

Fernando Pérez Forero ()

No 2024-010, Working Papers from Banco Central de Reserva del Perú

Abstract: Making macroeconomic forecasts in a time-varying and uncertain environment is hard, especially for monetary aggregates such as credit, currency, and total deposits. In this paper we employ a Bayesian Autoregressive Vector model with a time-varying mean and stochastic volatility to cover this task for the Peruvian economy. Results for different horizons exhibit a high level of predictive power. In addition, structural shocks are identified through zero and sign restrictions, i.e., supply and demand for credit by currencies together with other macroeconomic disturbances. Credit supply shocks in domestic currency expand credit and deposits in soles, reduce the spread between lending and deposit rates, produce a fall in credit in foreign currency, and an expansion of economic activity. Moreover, credit demand shocks in domestic currency produce an increase in the spread of lending and deposit rates, and a subsequent increase in economic activity.

Keywords: Credit Demand; Credit Supply; Bayesian Vector Autorregressions; Stochastic Volatility (search for similar items in EconPapers)
JEL-codes: C53 E47 E51 (search for similar items in EconPapers)
Date: 2024-12
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.bcrp.gob.pe/docs/Publicaciones/Documen ... trabajo-010-2024.pdf Application/pdf
Our link check indicates that this URL is bad, the error code is: 555 Security Page

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rbp:wpaper:2024-010

Access Statistics for this paper

More papers in Working Papers from Banco Central de Reserva del Perú Contact information at EDIRC.
Bibliographic data for series maintained by Research Unit ().