Detecting and Analyzing the Effects of Time‐varying Parameters in Dsge Models

Abstract

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.

Economic theories and macroeconomic reality

“…While our approach would not allow use to update the probabilities for a given model, the resulting posteriors would take into account the uncertainty across these identification schemes for each model. 27 The time variation in the discount factor β t is modelled following Canova et al (2020):…”

“…Schmitt-Grohe and Uribe (2012) study news shocks, but for simplicity we turn off these shocks in our exercises (so they are not present in neither the DGP nor the models we want to attach weights to).Our DGP adds one friction to the standard Schmitt-Grohe and Uribe (2012) model: A time varying discount factor, where time variation can be due to time variation in the capital stock or an exogenous shock. The specification we use for this time variation is due toCanova et al, 2020. Time variation in the discount factor has become a common tool to model, for example, sudden shifts in real interest rates -see for exampleBianchi and Melosi (2017).…”

Dynamics of globalization effect in India

“…The model was enriched by Primiceri (2005), allowing all the parameters to differ over time. The model is used by Canova, Ferroni, and Matthes (2018) to analyze the dynamics of inflation and production growth in the United States (2018). Using the TVP‐VAR model, Nakajima (2011) analyzed Japan's macroeconomic data.…”

Likelihood Evaluation of Models with Occasionally Binding Constraints

“…Fernandez-Villaverde and Rubio-Ramirez (2005) simulate data with measurement error from a nonlinear real business cycle model and compare the estimation performance of using a Kalman filter with a linearized model versus a particle filter with a nonlinear model. Canova, Ferroni, and Matthes (2018) analyze the effects of estimating a constant parameter model when there is time-variation in the structural parameters. the model.…”