This paper presents an approach for assessing the time taken by the well known reverse-shooting and forward-shooting algorithms to solve large-scale macroeconomic models characterized by saddle-path instability. We focus on a range of investment models with multi-dimensional specifications of...

When working with large-scale models or numerous small models, there can be a temptation to rely on default settings in proprietary software to derive solutions to the model. In this paper we show that, for the solution of non-linear dynamic models, this approach can be inappropriate....

The property of saddle-path instability often arises in economic models derived from optimizing behavior by individual agents. In the case when underlying functional forms are nonlinear, it is likely that the stable and unstable arms defining the saddle-path dynamics will also have nonlinear...

In this paper, we consider a macroeconomic model with alternative linear and non-linear specifications. One version of the model, expressed in levels, is highly non-linear and has at least two steady-state equilibria. One of these equilibria has an economically meaningful interpretation, while...

This paper describes two alternative approaches (modified reverse shooting and forward shooting) for solving the time-path of a representative agent model following an exogenous shock. In particular, reverse shooting is demonstrably better for solving part of the model but must be modified...

This paper analyzes the optimal intertemporal tradeoff between inflation and output in an open economy under perfect foresight. The announcement of the optimal plan may, or may not, generate an initial jump in the exchange rate. That depends upon the real adjustment costs, which such...

Dynamic regression equations are estimated for each beef cattle breeding herd and beef cattle inventories at two levels of aggregation, the U.S. and Montana. The analysis for Montana was utilized as a guide for specification of the national equation to reduce the inference problem associated...