Identifying High-Frequency Shocks with Bayesian Mixed-Frequency VARs

We contribute to research on mixed-frequency regressions by introducing an innovative Bayesian approach. We impose a Normal-inverse Wishart prior by adding a set of auxiliary dummies in estimating a Mixed-Frequency VAR. We identify a high frequency shock in a Monte Carlo experiment and in an illustrative example with uncertainty shock for the U.S. economy. As the main findings, we document a "temporal aggregation bias" when we adopt a common low-frequency model instead of estimating a mixed-frequency framework. The bias is amplified in case of a large mismatching between the high-frequency shock and low-frequency business cycle variables

DSGE Model Validation in a Bayesian Framework: an Assessment

This paper presents the concept of Model Validation applied to a Dynamic Stochastic General equilibrium Model (DSGE). The main problem discussed is the approximation of the statistical representation for a DSGE model when not all endogenous variables are observable. MonteCarlo experiments in artificial world are implemented to assess this problem by using the DSGE-VAR. Two Data Generating Processes are compared: a forward-looking and a backward-looking model. These experiments are followed by an empirical analysis with real world data for the US economy

Dealing with Misspecification in DSGE Models: A Survey

Dynamic Stochastic General Equilibrium (DSGE) models are the main tool used in Academia and in Central Banks to evaluate the business cycle for policy and forecasting analyses. Despite the recent advances in improving the fit of DSGE models to the data, the misspecification issue still remains. The aim of this survey is to shed light on the different forms of misspecification in DSGE modeling and how the researcher can identify the sources. In addition, some remedies to face with misspecification are discussed

DSGE Model Evaluation in a Bayesian Framework: an Assessment

This paper presents the concept of Model Validation applied to a Dynamic Stochastic General equilibrium Model (DSGE). The main problem discussed is the approximation of the statistical representation for a DSGE model when not all endogenous variables are observable. MonteCarlo experiments in artificial world are implemented to assess this problem by using the DSGE-VAR. Two Data Generating Processes are compared: a forward-looking and a backward-looking model. These experiments are followed by an empirical analysis with real world data for the US economy

Identifying noise shocks: a VAR with data revisions

We propose a new VAR identification strategy to study the impact of noise shocks on aggregate activity. We do so exploiting the informational advantage the econometrician has, relative to the economic agent. The latter, who is uncertain about the underlying state of the economy, responds to the noisy early data releases. The former, with the benefit of hindsight, has access to data revisions as well, which can be used to identify noise shocks. By using a VAR we can avoid making very specific assumptions on the process driving data revisions. We rather remain agnostic about it but make our identification strategy robust to whether data revisions are driven by noise or news. Our analysis shows that a surprising report of output growth numbers delivers a persistent and hump-shaped response of real output and unemployment. The responses are qualitatively similar but an order of magnitude smaller than those to a demand shock. Finally, our counterfactual analysis supports the view that it would not be possible to identify noise shocks unless different vintages of data are used

DSGE Model Validation in a Bayesian Framework: an Assessment

This paper presents the concept of Model Validation applied to a Dynamic Stochastic General equilibrium Model (DSGE). The main problem discussed is the approximation of the statistical representation for a DSGE model when not all endogenous variables are observable. MonteCarlo experiments in artificial world are implemented to assess this problem by using the DSGE-VAR. Two Data Generating Processes are compared: a forward-looking and a backward-looking model. These experiments are followed by an empirical analysis with real world data for the US economy

Dealing with Misspecification in DSGE Models: A Survey

Dynamic Stochastic General Equilibrium (DSGE) models are the main tool used in Academia and in Central Banks to evaluate the business cycle for policy and forecasting analyses. Despite the recent advances in improving the fit of DSGE models to the data, the misspecification issue still remains. The aim of this survey is to shed light on the different forms of misspecification in DSGE modeling and how the researcher can identify the sources. In addition, some remedies to face with misspecification are discussed

On the statistical identification of DSGE models

Dynamic Stochastic General Equilibrium (DSGE) models are now considered attractive by the profession not only from the theoretical perspective but also from an empirical standpoint. As a consequence of this development, methods for diagnosing the fit of these models are being proposed and implemented. In this article we illustrate how the concept of statistical identification, that was introduced and used by Spanos [Spanos, Aris, 1990. The simultaneous-equations model revisited: Statistical adequacy and identification. Journal of Econometrics 44, 87–105] to criticize traditional evaluation methods of Cowles Commission models, could be relevant for DSGE models. We conclude that the recently proposed model evaluation method, based on the DSGE−VAR(λ), might not satisfy the condition for statistical identification. However, our application also shows that the adoption of a FAVAR as a statistically identified benchmark leaves unaltered the support of the data for the DSGE model and that a DSGE-FAVAR can be an optimal forecasting model

How Top Management Team Social Status Impacts Innovation

Scholars have investigated the effect that top management team (TMT) status has on several organizational dimensions, including strategic decision, risk propensity, and, ultimately, performance. However, the existing literature is relatively silent on the effect of TMT status on innovation. Our scope is to cover that research gap. Grounding our reasoning on two different yet intertwined literature streams – one on the TMT status and the other on innovation – we predict that TMT status should be positively correlated with innovation and its market value, but not with its scientific value. Relying on a unique, hand-crafted dataset composed of 833 firm-years' observations for the period 2005–2010, we can validate our hypotheses. Our study contributes to a better understanding of the relationship between TMT status and innovation generated by the respective firm. Finally, the study discusses limitations and recommendations for further research