Estimating international risk-sharing in the presence of endogeneity.
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Over six chapters, this thesis explores how to estimate risk-sharing when output is not exogenous. The thesis starts with a survey of the current literature and how it estimates risk-sharing. This survey is then followed by risk-sharing estimations based on a panel of 24 countries over the period of 1970-2007. The estimation approaches applied include the literature's Classical and Level approaches, as well as alternative estimation approaches that provide robust parameter estimates when the literature commonly assumed output exogeneity is dropped. These alternative estimators consist of procedures using instrumental variables ranging from first differenced two-stage least squares, a dynamic generalized method of moments estimation, and an instrumental variables estimation using an instrument derived from a structural vector autoregressive model. Also, a Monte Carlo Simulation is undertaken to show the severity of the bias inherent in the Classical estimation method, as well as to show the performance of the proposed alternative methods. When output is endogenous, the Classical estimation method is found to underestimate risk-sharing, while the best performing alternative approaches are concluded to be the Level approach and the instrumental variables estimation approach using an instrument derived from a structural vector autoregressive model. This thesis contributes to the risk-sharing literature by discussing and quantifying the bias the Classical estimation approach suffers from due to output endogeneity. It also contributes by adapting estimation methods from other fields that allow consistent estimations of risk-sharing parameters in the presence of endogeneity bias, and by analyzing the performance of these asymptotic panel estimators in the specific context of the panel dimensions commonly found in the risk-sharing literature.