Quarterly Fiscal Policy Experiments with a Multiplier-Accelerator Model

David Kendrick and George Shoukry ()

Computational Economics, 2014, vol. 44, issue 3, 269-293

Abstract: In an earlier paper, i.e. Kendrick and Amman (A Taylor Rule for fiscal policy?, 2010 ), we raised the question of whether adjusting fiscal policy more frequently than its current pace of once a year could be used to improve stabilization. Also, we proposed a method for shedding light on that question by using a small macroeconometric model in a quadratic linear tracking stochastic control framework with an implicit feedback rule to compare a scenario in which fiscal policy was changed quarterly to a scenario in which it was only changed once a year. In this paper we first report on the use of counterfactual experiments in the 2007–2010 period of a major downturn in the economy. We find in one experiment that quarterly changes in policy stabilize output levels in the economy better than annual changes with a slightly larger increase in debt over the counterfactual period. In a second experiment we find that when weight changes are used to get roughly equal stabilization results, the increase in the debt level over the counterfactual periods is substantially less with quarterly than with annual policy changes. In the second part of the paper we repeat the two experiments but do so in a Monte Carlo framework. The results in this more general framework also point the way to a finding that a relatively simple shift from annual to quarterly fiscal policy could provide either better stabilization results with a slightly larger increase in the debt level or similar stabilization results but with a smaller increase in the debt level. Copyright Springer Science+Business Media New York 2014

Keywords: Fiscal policy; Stochastic control; Economic stochastic control models; Feedback rules; Macroeconomics; Stabilization policy (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s10614-013-9389-4

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