This paper develops a global simulation-based solution method to solve large states space macro-finance models using machine learning. We use an artificial neural network (ANN) to approximate the expectations in the optimality conditions in the spirit of the parameterized expectations algorithm...

We provide a brief primer on how the core of usable macroeconomic theory for monetary policymakers has evolved over the past 50 years. Today's policy discussions center on the New Keynesian (NK) synthesis, which builds on the Neoclassical growth model and the AS-AD framework. It incorporates...

Rising inflation in the wake of unprecedented debt financed stimulus packages raises concerns about a looming return of persistent inflation, as governments may be tempted to monetize debt. In this paper, we ask whether governments can use real (TIPS) bonds as part of the government debt...

We revisit the relation between equity returns and financial leverage through the lens of a dynamic trade-off model with costly capital structure rebalancing. The model predicts that expected equity returns depend on whether a firm's leverage is above or below its target leverage. We provide...

We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic...

We develop a model of capital accumulation in an open economy that imports investment goods from large foreign firms with market power. We model investmentgoods producers as a dynamic oligopoly and characterize a Markov Perfect Equilibrium with a Generalized Euler Equation. We use this...