Fabio Canova

Canova, Fabio & Matthes, Christian (2021)

A Composite Likelihood Approach for Dynamic Structural Models

Economic Journal, 131, s. 2447- 2474. Doi: 10.1093/ej/ueab004

We explain how to use the composite likelihood function to ameliorate estimation, computational and inferential problems in dynamic stochastic general equilibrium models. We combine the information present in different models or data sets to estimate the parameters common across models. We provide intuition for why the methodology works and alternative interpretations of the estimators we construct and of the statistics we employ. We present a number of situations where the methodology has the potential to resolve well-known problems and to provide a justification for existing practices that pool different estimates. In each case, we provide an example to illustrate how the approach works and its properties in practice.

Canova, Fabio & Matthes, Christian (2021)

Dealing with misspecification in structural macroeconometric models

Quantitative Economics, 12(2), s. 313- 350. Doi: 10.3982/QE1413

We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood‐based estimators in mean squared error and composite models are superior to individual models in the Kullback–Leibler sense. We describe Bayesian quasi‐posterior computations and compare our approach to Bayesian model averaging, finite mixture, and robust control procedures. We robustify inference using the composite posterior distribution of the parameters and the pool of models. We provide estimates of the marginal propensity to consume and evaluate the role of technology shocks for output fluctuations.

Canova, Fabio; Ferroni, Filippo & Matthes, Christian (2020)

DETECTING AND ANALYZING THE EFFECTS OF TIME‐VARYING PARAMETERS IN DSGE MODELS

International Economic Review, 61(1), s. 105- 125. Doi: 10.1111/iere.12418 - Full text in research archive

We study how structural parameter variations affect the decision rules and economic inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. A constant parameter model poorly approximates a time‐varying data generating process (DGP), except in a handful of relevant cases. Linear approximations do not produce time‐varying decision rules; higher‐order approximations can do this only if parameter disturbances are treated as decision rule coefficients. Structural responses are time invariant regardless of order of approximation. Adding endogenous variations to the parameter controlling leverage in Gertler and Karadi's model substantially improves the fit of the model.

Altavilla, Carlo; Canova, Fabio & Ciccarelli, Matteo (2019)

Mending the broken link: Heterogeneous bank lending rates and monetary policy pass-through

Journal of Monetary Economics, s. 1- 18. Doi: 10.1016/j.jmoneco.2019.01.001 - Full text in research archive

Canova, Fabio & Sahneh, Mehdi Hamidi (2018)

Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Nonfundamentalness

Journal of the European Economic Association, 16(4), s. 1069- 1093. Doi: 10.1093/jeea/jvx032

Altavilla, Carlo; Canova, Fabio & Ciccarelli, Matteo (2017)

Mending the Broken Link: Heterogeneous Bank Lending and Monetary Policy Pass-through

[Academic lecture]. Recent Developments in Monetary Policy Research.