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Forecasting inflation in Argentina [recurso electrónico] : a probabilistic approach / Tomás Marinozzi


  En: Ensayos Económicos [recurso electrónico]. -- no. 81 (mayo, 2023). -- , . --

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Modo de acceso: World Wide Web. PDF.
Descripción basada en la visualización del recurso el 30/7/2024
Disponible en: https://www.bcra.gob.ar/Institucional/DescargaPDF/DownloadPDF.aspx?Id=1096

  Probability forecasts are gaining popularity in the macroeconomic discipline as point forecasts lack the ability to capture the level of uncertainty in fundamental variables like inflation, growth, exchange rate, or unemployment. This paper explores the use of probability forecasts to predict inflation in Argentina. Scoring rules are used to evaluate several autoregressive models relative to a benchmark. Results show that parsimonious univariate models have a relatively similar performance to that of the multivariate models around central scenarios but fail to capture tail risks, particularly at longer horizons.
  ISSN: 18506046

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Marinozzi, Tomás
Forecasting inflation in Argentina [recurso electrónico] : a probabilistic approach / Tomás Marinozzi
En: Ensayos Económicos [recurso electrónico]. -- no. 81 (mayo, 2023). -- Buenos Aires : Banco Central de la República Argentina, 2016

Incl. ref.
Incl. graf.
Modo de acceso: World Wide Web. PDF.
Descripción basada en la visualización del recurso el 30/7/2024
Disponible en: https://www.bcra.gob.ar/Institucional/DescargaPDF/DownloadPDF.aspx?Id=1096

Probability forecasts are gaining popularity in the macroeconomic discipline as point forecasts lack the ability to capture the level of uncertainty in fundamental variables like inflation, growth, exchange rate, or unemployment. This paper explores the use of probability forecasts to predict inflation in Argentina. Scoring rules are used to evaluate several autoregressive models relative to a benchmark. Results show that parsimonious univariate models have a relatively similar performance to that of the multivariate models around central scenarios but fail to capture tail risks, particularly at longer horizons.
ISSN: 18506046

1. INFLACION
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