This paper develops a structural macroeconometric model of the world economy, disaggregated into forty national economies. This panel dynamic stochastic general equilibrium model features a range of nominal and real rigidities, extensive macrofinancial linkages, and diverse spillover...

This paper develops a structural macroeconometric model of the world economy, disaggregated into thirty five national economies. This panel unobserved components model encompasses an approximate linear panel dynamic stochastic general equilibrium model featuring a monetary transmission...

This paper proposes to exploit data on expectations to identify news shocks in business cycles. News shocks work through changes in expectations, so data on expectations contain important information for identification. We demonstrate this by estimating a DSGE model augmented with news shocks...

This paper tests the ability of popular New Keynesian models, which are traditionally used to study monetary policy and business cycles, to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding...

We use mixed-frequency (quarterly-monthly) data to estimate a dynamic stochastic general equilibrium model embedded with the financial accelerator mechanism a la Bernanke et al. (1999). We find that the financial accelerator can work very differently at monthly frequency compared to the...

We develop a two-country New Keynesian model with sticky local currency pricing,distribution costs and a demand elasticity increasing with the relative price. These features help to reduce the exchange rate pass-through to import price at the border and down the chain towards consumption price,...

This paper identiftes U.S. monetary and ftscal dominance regimes using machine learning techniques. The algorithms are trained and verifted by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017 using actual U.S. data. All machine learning...