Access Type

Open Access Dissertation

Date of Award

January 2014

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Economics

First Advisor

Tatsuma Wada

Abstract

This dissertation decomposes, discusses, and ventures to solve an international macroeconomic anomaly known as the "Forward Premium Puzzle" into three main chapters. Chapter 1 explores the state of research pertaining to the Forward Premium Puzzle, which derives from a failure in the Uncovered Interest Parity (UIP). The body of literature is split into three branches i. Those works advocating the presence of an anomaly due to assumption of Rational Expectations, ii. Works preserving the assumption of Rational Expectations and instead discuss a bias due to Risk Premia, and iii. Research focused on econometric implementation of the forward premium estimator. Furthermore, a tour of Animal Spirits is given and how applications out of control theory, or Robust Control, serves as a vehicle of implementing Animal Spirits within modern macroeconomics as a potential resolution to the anomaly.

In Chapter 2, a dynamic stochastic general equilibrium (DSGE) two-country two-money model is fitted with Robust Control, inducing fear of model misspecification, or pessimism, in international households. The forward premium bias, produced from a collapse in uncovered interest parity (UIP), is a direct function of pessimism. Under various regimes of pessimism, the forward premium estimator emulates both features of international data and unbias UIP.

In Chapter 3 pessimism, via a parameter, is implemented in a two-country, two-money dynamic stochastic general equilibrium (DSGE) model fitted with robust control (RC). Using detection error probability methodology, pessimism is calibrated using international data and simulations from the RC-DSGE model. Data-driven pessimism and its simulation-based counterpart are compared to determine how the animal spirit, produced from the model, performs against pessimism implied by the data.

Included in

Economics Commons

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