 Predicting quarterly aggregates with monthly indicatorsNo 2012-023, Working Papers from Banco Central de Reserva del Perú Abstract: Many important macroeconomic variables measuring the state of the economy are sampled quarterly and with publication lags, although potentially useful predictors are observed at a higher frequency almost in real time. This situation poses the challenge of how to best use the available data to infer the state of the economy. This paper explores the merits of the so-called Mixed Data Sampling (MIDAS) approach that directly exploits the information content of monthly indicators to predict quarterly Peruvian macroeconomic aggregates. To this end, we propose a simple extension, based on the notion of smoothness priors in a distributed lag model, that weakens the restrictions the traditional MIDAS approach imposes on the data to achieve parsimony. We also discuss the workings of an averaging scheme that combines predictions coming from non-nested specifications. It is found that the MIDAS approach is able to timely identify, from monthly information, important signals of the dynamics of the quarterly aggregates. Thus, it can deliver significant gains in prediction accuracy, compared to the performance of competing models that use exclusively quarterly information. Keywords: Mixed-frequency data; MIDAS; model averaging; nowcasting; backcasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:rbp:wpaper:2012-023 Access Statistics for this paper More papers in Working Papers from Banco Central de Reserva del Perú Contact information at EDIRC. |
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