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Claudia Foroni
;
Massimiliano Marcellino

mixed‐frequency structural models: identification, estimation, and policy analysis (replication data)

The mismatch between the timescale of DSGE (dynamic stochastic general equilibrium) models and the data used in their estimation translates into identification problems, estimation bias, and distortions in policy analysis. We propose an estimation strategy based on mixed-frequency data to alleviate these shortcomings. The virtues of our approach are explored for two monetary policy models.

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Foroni, Claudia; Marcellino, Massimiliano (2014): MIXED‐FREQUENCY STRUCTURAL MODELS: IDENTIFICATION, ESTIMATION, AND POLICY ANALYSIS (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022321.0715979697

Related Publication

Foroni, C. and Marcellino, M. (2014), Mixed‐Frequency Structural Models: Identification, Estimation, And Policy Analysis, Journal of Applied Econometrics, 29(7), 1118-1144. https://doi.org/10.1002/jae.2396

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