Browsing BI Research Centre's Series by Author "Canova, Fabio"

Now showing items 1-5 of 5

    • A composite likelihood approach for dynamic structural models 

      Canova, Fabio; Matthes, Christian (CAMP Working Paper Series Paper No. 10/2018;, Working paper, 2018-10-08)
      We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology ...

    • Approximating time varying structural models with time invariant structures 

      Canova, Fabio; Ferroni, Filippo; Matthes, Christian (CAMP Working Paper Series;1/2016, Working paper, 2016)
      The paper studies how parameter variation affects the decision rules of a DSGE model and structural inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. ...

    • Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Non-Fundamentalness 

      Canova, Fabio; Sahneh, Mehdi Hamidi (CAMP Working Paper Series;2/2016, Working paper, 2016)
      Non-fundamentalness arises when observables do not contain enough information to recover the vector of structural shocks. Using Granger causality tests, the literature suggested that many small scale VAR models are ...

    • Mending the broken link: heterogeneous bank lending and monetary policy pass-through 

      Altavilla, Carlo; Canova, Fabio; Ciccarelli, Matteo (CAMP Working Paper Series;9/2016, Working paper, 2016)
      We analyze the pass-through of monetary policy measures to lending rates to fi rms and households in the euro area using a novel bank-level dataset. Banks characteristics such as the capital ratio, the exposure to sovereign ...

    • Mind the gap! Stylized dynamic facts and structural models. 

      Canova, Fabio; Ferroni, Filippo (CAMP Working Paper Series;13/2018, Working paper, 2018-12-11)
      We study what happens to identified shocks and to dynamic responses when the structural model features q disturbances and m endogenous variables, q ≤ m, but only m1 < q variables are used in the empirical model. Identified ...