This thesis first investigates various issues related with model averaging, and then evaluates two policies, i.e. West Development Drive in China and fiscal decentralization in U.S, using econometric tools. Chapter 2 proposes a hierarchical weighted least squares (HWALS) method to address multiple sources of uncertainty generated from model specification, estimation, and measurement choices. It examines the effects of different growth theories taking into account the measurement problem in the growth regression. Chapter 3 addresses the issue of prediction under model uncertainty, and proposes a weighted average least squares (WALS) prediction procedure that is not conditional on the selected model. Taking both model and error uncertainty into account, it also proposes an appropriate estimate of the variance of the WALS predictor. Chapter 4 focuses on the interplay among resource abundance, institutional quality, and economic growth in China, using two different measures of resource abundance. It employs a functional-coefficient model to capture the nonlinear interaction effect of institutional quality, and panel-data time-varying coefficient model to describe the dynamic effect of natural resources. Chapter 5 considers a dark side of fiscal decentralization. It models and empirically tests a dress-up contest caused by fiscal decentralization, and shows that the dress-up contest can lead to a social welfare loss.
|Qualification||Doctor of Philosophy|
|Award date||8 Nov 2013|
|Place of Publication||Tilburg|
|Publication status||Published - 2013|