Fabio Canova

Canova, Fabio & Sæterhagen Paulsen, Kenneth (2023)

Symbolic stationarization of dynamic equilibrium models

Journal of Economic Dynamics and Control, 154 Doi: 10.1016/j.jedc.2023.104710

Dynamic equilibrium models are specified to track persistent time series. Thus, unit roots are typically introduced as exogenous driving forces and the optimality conditions adjusted to produce a stationary solution. This adjustment step requires tedious algebra and often leads to algebraic mistakes, especially in complicated models. We propose an algorithm employing differentiation rules that simplifies the step of rendering non-stationary models stationary. It is easy to implement and works when trends are stochastic or deterministic, exogenous or endogenously determined. Three examples illustrate the mechanics and the properties of the approach. A comparison with existing methods is provided (97 words).

Canova, Fabio & Ferroni, Filippo (2022)

Mind the gap: Stylized dynamic facts and structural models

American Economic Journal: Macroeconomics, 14(4), s. 104- 135. Doi: 10.1257/mac.20200054

We study what happens to identified shocks and to dynamic responses when the data generating process features q disturbances but q 1 < q variables are used in an empirical model. Identified shocks are linear combinations of current and past values of all structural disturbances and do not necessarily combine disturbances of the same type. Theory- based restrictions may be insufficient to obtain structural dynamics. We revisit the evidence regarding the transmission of house price and of uncertainty shocks. We provide suggestions on how to compare the dynamics of larger scale DSGEs models with smaller scale VARs. (JEL E12, E13, E23, E31, E43, R31)

Canova, Fabio & Matthes, Christian (2021)

A composite likelihood approach for dynamic structural models

Economic Journal, 131(638), s. 2447- 2477. Doi: 10.1093/ej/ueab004

Canova, Fabio & Matthes, Christian (2021)

Dealing with misspecification in structural macroeconometric models

Quantitative Economics, 12(2), s. 313- 350. Doi: 10.3982/QE1413 - Full text in research archive

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 (2017)

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

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