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A mean-reverting stochastic model for the political business cycle

Basak, Gopal K and Ghosh, Mrinal K and Mukherjee, Diganta (2016) A mean-reverting stochastic model for the political business cycle. In: STOCHASTIC ANALYSIS AND APPLICATIONS, 34 (1). pp. 96-116.

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Official URL: http://dx.doi.org/10.1080/07362994.2015.1106321 ...

Abstract

In this article, we look at the political business cycle problem through the lens of uncertainty. The feedback control used by us is the famous NKPC with stochasticity and wage rigidities. We extend the New Keynesian Phillips Curve model to the continuous time stochastic set up with an Ornstein-Uhlenbeck process. We minimize relevant expected quadratic cost by solving the corresponding Hamilton-Jacobi-Bellman equation. The basic intuition of the classical model is qualitatively carried forward in our set up but uncertainty also plays an important role in determining the optimal trajectory of the voter support function. The internal variability of the system acts as a base shifter for the support function in the risk neutral case. The role of uncertainty is even more prominent in the risk averse case where all the shape parameters are directly dependent on variability. Thus, in this case variability controls both the rates of change as well as the base shift parameters. To gain more insight we have also studied the model when the coefficients are time invariant and studied numerical solutions. The close relationship between the unemployment rate and the support function for the incumbent party is highlighted. The role of uncertainty in creating sampling fluctuation in this set up, possibly towards apparently anomalous results, is also explored.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the TAYLOR & FRANCIS INC, 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
Keywords: Political business cycle; new Keynesian Phillips curve; mean-reverting control; risk aversion; stochastic optimal control; 93E20; 93C95; 60H30
Department/Centre: Division of Physical & Mathematical Sciences > Mathematics
Depositing User: Id for Latest eprints
Date Deposited: 20 Jan 2016 05:37
Last Modified: 20 Jan 2016 05:37
URI: http://eprints.iisc.ac.in/id/eprint/53133

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