Multiple filtering devices for the estimation of cyclical DSGE models

We propose a method to estimate time invariant cyclical DSGE models using the information provided by a variety of filters. We treat data filtered with alternative procedures as contaminated proxies of the relevant model-based quantities and estimate structural and non-structural parameters jointly using a signal extraction approach. We employ simulated data to illustrate the properties of the procedure and compare our conclusions with those obtained when just one filter is used. We revisit the role of money in the transmission of monetary business cycles.DSGE models, Filters, Structural estimation, Business cycles

The dynamics of US inflation: Can monetary policy explain the changes?

We investigate the relationship between monetary policy and inflation dynamics in the US using a medium scale structural model. The specification is estimated with Bayesian techniques and fits the data reasonably well. Policy shocks account for a part of the decline in inflation volatility; they have been less effective in triggering inflation responses over time and qualitatively account for the rise and fall in the level of inflation. A number of structural parameter variations contribute to these patterns.New Keynesian model, Bayesian methods, Monetary policy, Inflation dynamics.

Trend agnostic one step estimation of DSGE models

DSGE models are currently estimated with a two step approach: data is first filtered and then DSGE structural parameters are estimated. Two step procedures have problems, ranging from trend misspecification to wrong assumption about the correlation between trend and cycles. In this paper, I present a one step method, where DSGE structural parameters are jointly estimated with filtering parameters. I show that different data transformations imply different structural estimates; the two step approach lacks a statistical-based criterion to select among them. The one step approach allows to test hypothesis about the most likely trend specification for individual series and/or use the resulting information to construct robust estimates by Bayesian averaging. The role of investment shock as source of GDP volatility is reconsidered.DSGE models, Filters, Structural estimation, Business Cycles

Trend agnostic one step estimation of DSGE models

DSGE models are currently estimated with a two step approach: data is first filtered and then DSGE structural parameters are estimated. Two step procedures have problems, ranging from trend misspecification to wrong assumption about the correlation between trend and cycles. In this paper, I present a one step method, where DSGE structural parameters are jointly estimated with filtering parameters. I show that different data transformations imply different structural estimates; the two step approach lacks a statistical-based criterion to select among them. The one step approach allows to test hypothesis about the most likely trend specification for individual series and/or use the resulting information to construct robust estimates by Bayesian averaging. The role of investment shock as source of GDP volatility is reconsidered

Trend agnostic one step estimation of DSGE models

DSGE models are currently estimated with a two step approach: data is first filtered and then DSGE structural parameters are estimated. Two step procedures have problems, ranging from trend misspecification to wrong assumption about the correlation between trend and cycles. In this paper, I present a one step method, where DSGE structural parameters are jointly estimated with filtering parameters. I show that different data transformations imply different structural estimates; the two step approach lacks a statistical-based criterion to select among them. The one step approach allows to test hypothesis about the most likely trend specification for individual series and/or use the resulting information to construct robust estimates by Bayesian averaging. The role of investment shock as source of GDP volatility is reconsidered

The dynamics of hours worked and technology

We study the relationship between hours worked and technology during the postwar period in the US. We show that the responses of hours to technological improvements have increased over time, and that the patterns captured by the SVAR are consistent with those obtained from an RBC model with a less than unitary elasticity of substitution between capital and labor. Data supports the hypothesis that the observed changes in the response of hours to a technology shock are attributable to changes in the magnitude of the degree of capital-labor substitution ..En este artículo estudiamos la relación entre horas trabajadas y progreso técnico durante el período de postguerra en los Estados Unidos. Mostramos que la respuesta de las horas a mejoras tecnológicas ha aumentado a lo largo del tiempo y que los patrones capturados por un modelo SVAR son coherentes con los obtenidos por un modelo de ciclos reales con una elasticidad de sustitución entre capital y trabajo menor que 1. Los datos apoyan la hipótesis de que el cambio observado en la respuesta de las horas a mejoras tecnológicas es debido a cambios en la magnitud de la elasticidad de sustitución $\sigma$. Nuestro argumento es que la variación de $\sigma$ puede haber surgido por cambios en la composición estructural de los sectores de actividad económica o, de modo equivalente, la composición de los factores de producción en una función de producción con inputs heterogéneos, o bien por sesgos en la dirección del cambio técnic

Fundamental shock selection in DSGE models

DSGE models are typically estimated assuming the existence of certain structural shocks that drive macroeconomic fluctuations. We analyze the consequences of introducing nonfundamental shocks for the estimation of DSGE model parameters and propose a method to select the structural shocks driving uncertainty. We show that forcing the existence of non-fundamental structural shocks produces a downward bias in the estimated internal persistence of the model. We then show how these distortions can be reduced by allowing the covariance matrix of the structural shocks to be rank deficient using priors for standard deviations whose support includes zero. The method allows us to accurately select fundamental shocks and estimate model parameters with precision. Finally, we revisit the empirical evidence on an industry standard medium-scale DSGE model and find that government, price, and wage markup shocks are non-fundamental

Selecting structural innovations in DSGE models

Dynamic stochastic general equilibrium (DSGE) models are typically estimated assuming the existence of certain structural shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are “nonexistent” and propose a method to select the economic shocks driving macroeconomic uncertainty. Forcing these nonexisting shocks in estimation produces a downward bias in the estimated internal persistence of the model. We show how these distortions can be reduced by using priors for standard deviations whose support includes zero. The method allows us to accurately select shocks and estimate model parameters with high precision. We revisit the empirical evidence on an industry standard medium‐scale DSGE model and find that government and price markup shocks are innovations that do not generate statistically significant dynamics