We present the first necessary and sufficient conditions for the existence of a unique perfect-foresight solution, returning to a given steady-state, in an otherwise linear model with occasionally binding constraints. We derive further conditions on the existence of a solution in such models,...

This paper shows that a modified real business cycle (RBC) model, one that includes home production and fiscal spending shocks, can solve one of the RBC puzzles and generates zero correlation between wages and hours. In addition, the micro-founded model presented here provides a sound...

There is growing literature in macroeconomics, especially on business cycle synchronization, employing different methods of time series clustering. However, even as an unsupervised learning method, this technique requires making choices that are nontrivially influenced by the nature of the data...

We construct the first algorithm for the perfect foresight solution of otherwise linear models with occasionally binding constraints, with fixed terminal conditions, that is guaranteed to return a solution in finite time, if one exists. We also provide a proof of the inescapability of the...

While a basic theoretical principle in public economics assumes that individuals' behaviour is fully-optimizer with respect to the introduction of a tax, an increasing body of research is presenting evidence that agents decision making is often affected by non-negligible cognitive biases, which...

We analyze a n-country, two-period Nash tax competition game to evaluate Sinn's proposal to use capital income taxation as a means to decelerate fossil fuel ex- traction (Sinn, 2008). The interest and discount rate is determined on a perfectly competitive consumer loan market on which the...

The international discussion of Trump's dispute over import tariffs for steel, aluminum and even cars is so far focused on the big global players. However, African countries in particular could suffer too from the planned punitive tariffs, analogous to the famous African proverb, "When elephants...