MIXED‐FREQUENCY STRUCTURAL MODELS: IDENTIFICATION, ESTIMATION, AND POLICY ANALYSIS

Claudia Foroni · Massimiliano Giuseppe Marcellino

35 citations0/0 files storedJournal of Applied Econometrics

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Monetary Policy and Economic Impact Forecasting Techniques and Applications Economic theories and models

Abstract

In this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identification problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identification issues, and yield more reliable policy conclusions. The problems and possible remedy are illustrated in the context of standard structural monetary policy models.

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References

2000Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory*2007Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach 2010Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach 1995Some Empirical Evidence on the Effects of Shocks to Monetary Policy on Exchange Rates 2015Monetary policy, inflation and the business cycle 1999Some Consequences of Temporal Aggregation in Empirical Analysis 2004Estimating the Euler equation for output 1984Systematic sampling and temporal aggregation in time series models

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