Fabio Canova

Journal of Economic Dynamics and Control 154 Doi: https://doi.org/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).

American Economic Journal: Macroeconomics 14(4) s. 104-135 Doi: https://doi.org/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)

Economic Journal 131(638) s. 2447-2477 Doi: https://doi.org/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.

Quantitative Economics 12(2) s. 313-350 Doi: https://doi.org/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.

International Economic Review 61(1) s. 105-125 Doi: https://doi.org/10.1111/iere.12418

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.

Journal of Monetary Economics 110(April) s. 1-18 Doi: https://doi.org/10.1016/j.jmoneco.2019.01.001

We analyse the pass-through of monetary policy measures to lending rates to households and firms in the euro area using novel bank-level datasets. Banks’ characteristics such as the capital ratio, exposure to domestic sovereign debt, percentage of non-performing loans and stability of funding structure are responsible for the heterogeneity in the pass-through of conventional monetary policy changes. The location of a bank is irrelevant. Non-standard measures reduce lending rate heterogeneities. Banks located in financially stressed countries and with weak balance sheets are most affected. Banks’ lending margins have fallen considerably.

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

The International Journal of Central Banking 12(3) s. 69-120