This paper employs a large BVAR model with common stochastic volatility to examine the effects of oil supply shocks, global oil demand shocks and precautionary oil shocks on 17 U.S. macroeconomic and financial market variables from 1986Q1 to 2019Q2. Generalized impulse response functions...

We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a...

This study first investigates the short and long-run effectsof exchange rate, output gap and output gap volatility on inflationvolatility in Turkey by using the ARDL bounds testing approach.Second, we also examine the causal relationship among these vari-ables by using Toda-Yamamoto and...

In this paper we study 2-state Markov switching VAR models of monthly unemployment and inflation for three countries: Sweden, United Kingdom, and the United States. The primary purpose is to examine if periods of low inflation are associated with high or low unemployment volatility. We find that...

We examine the effect of introducing a specific type of price stickiness into a stochastic growth model, subject to a cash in advance constraint. As in previous studies, we find the introduction of price rigidities provides a substantial source of monetary non-neutrality which contributes...

The US and other advanced countries suffered bursts of severe inflation in 2021 and the first half of 2022, followed by declines of inflation later in 2022, in some countries. In times of high volatility of price determinants--cost and productivity--inflation can jump upward and fall downward at...

The New Keynesian Phillips Curve plays a central role in modern macroeconomic theory. A vast empirical literature has estimated this structural relationship over various post-war full samples. While it is well known that in a standard sticky price model a 'weak' central bank response to...

In this paper we develop a general framework to analyze state space models with time-varying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...