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Working Paper
Modeling Time-Variation Over the Business Cycle (1960-2017): An International Perspective
In this paper, I explore the changes in international business cycles with quarterly data for the eight largest advanced economies (U.S., U.K., Germany, France, Italy, Spain, Japan, and Canada) since the 1960s. Using a time-varying parameter model with stochastic volatility for real GDP growth and inflation allows their dynamics to change over time, approximating nonlinearities in the data that otherwise would not be adequately accounted for with linear models (Granger et al. (1991), Granger (2008)). With that empirical model, I document a period of declining macro volatility since the 1980s, ...
Working Paper
Some International Evidence for Keynesian Economics Without the Phillips Curve
Farmer and Nicol (2018) show that the Farmer Monetary (FM)-model outperforms the three-equation New-Keynesian (NK)-model in post war U.S. data. In this paper, we compare the marginal data density of the FM-model with marginal data densities for determinate and indeterminate versions of the NK-model for three separate samples using U.S., U.K. and Canadian data. We estimate versions of both models that restrict the parameters of the private sector equations to be the same for all three countries. Our preferred specification is the constrained version of the FM-model which has a marginal data ...
Working Paper
Optimal Unemployment Insurance and International Risk Sharing
We discuss how cross-country unemployment insurance can be used to improve international risk sharing. We use a two-country business cycle model with incomplete financial markets and frictional labor markets where the unemployment insurance scheme operates across both countries. Cross-country insurance through the unemployment insurance system can be achieved without affecting unemployment outcomes. The Ramsey-optimal policy however prescribes a more countercyclical replacement rate when international risk sharing concerns enter the unemployment insurance trade-off. We calibrate our model to ...