DSGE Models in a Data-Rich Environment

By Marc P. Giannoni, Jean Boivin. 2006

321 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7764a479b204aad6abb5bd9a78fd58f6a9c82e9d

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In Economics and Econometrics

Citations in the corpus

DYNAMIC FACTOR MODELS (2018, methodology): “By introducing multiple indicators of these latent processes, Boivin and Giannoni (2006) bring additional information to bear on the difficult task of estimation of DSGE parameters. In principle this estimation could be done by MLE, however Boivin and Giannoni (2006) use the Bayesian/Markov Chain Mo”
Selecting Instrumental Variables in a Data Rich Environment (2009, background): “This interpretation is motivated by co-movement of economic variables, and Ft is a vector of unobservable common shocks that drive the economic variables.1 A theoretical motivation for this interpretation is given by Boivin and Giannoni (2006), although they use different notations from ours.”
Can Structural Small Open Economy Models Account for the Influence of Foreign Disturbances (2010, background): “Variance decompositions are 3 In a closed-economy setting, Boivin and Giannoni (2006) establish a formal link between DSGE and dynamic factor models.”
Non-Stationary Dynamic Factor Models for Large Datasets (2016, methodology): “First, our estimation approach could also be used for estimating and validating Dynamic Stochastic General Equilibrium models in a data-rich environment (see Boivin and Gi- annoni, 2006, for the stationary case).”
Fitting observed inflation expectations (2011, unknown): “4 Following the FAVAR methodology (Bernanke et al., 2005) there are some attempts to combine factor and DSGE models with the goal of incorporating as much of the available data as possible (Boivin and Giannoni, 2006; Giannone et al., 2008).”
Methods to Estimate Dynamic Stochastic General Equilibrium Models (2007, unknown): “[5]”
INSTRUMENTAL VARIABLE ESTIMATION IN A DATA RICH ENVIRONMENT (2010, background): “The analysis of Boivin and Giannoni (2006) shows how a factor model can emerge from a DSGE model in a data rich environment.”
What's Up with the Phillips Curve? (2020, unknown): “…of the variables are measured with error, that is, the observed value equals the model implied value plus an AR(1) exogenous process e∗t that can be thought of either measurement errors or some other unmodeled source of discrepancy between the model and the data, as in Boivin and Giannoni (2006).”
Monetary Policy, Credit Spreads, and Business Cycle Fluctuations (2015, background): “2See Hanson (2004), Boivin and Giannoni (2006), Boivin et al. (2010), Castelnuovo and Surico (2010) 3Dynamic stochastic general equilibrium models with financial frictions have been pioneered by Bernanke et al. (1999), and Gertler and Karadi (2011) provide a recent application to study the…”
The Natural Rate of Interest and Its Usefulness for Monetary Policy (2014, methodology): “This approach diminishes the importance of cost-push shocks for cyclical fluctuations, as shown by Boivin and Giannoni (2006) for the case of goods prices, and by Justiniano, Primiceri, and Tambalotti (2013) for wages.”
TESTS FOR PARAMETER INSTABILITY IN DYNAMIC FACTOR MODELS (2013, methodology): “For example, they have been successfully used in forecasting (Stock and Watson, 2002a), factor augmented vector autoregressive (FAVAR) models (Bernanke, Boivin, and Eliasz, 2005; Stock and Watson, 2005), dynamic stochastic general equilibrium (DSGE) models (Boivin and Giannoni, 2006), and handling a”
Working Paper Series (2019, unknown): “Boivin and Giannoni (2006) used 91 variables in their analysis which directly connects estimation of DSGE models with large-dimensional factor models because data with measurement errors can also be represented by factor models.”