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Modeling and estimation of financial and bioeconomic settings in a dynamic environment


This dissertation consists of 4 papers that propose methods for modeling and estimation in a dynamic or nonstationary environment. An underlying theme throughout the thesis the belief that bioeconomic equilibriums are moving over time and that steady state models are often inappropriate in dynamic setting. Two empirical dynamic settings are explored. The first showing a regime change in the biological characteristics with consideration of the economic impact to the fisheries. The second proposes estimation methods in a nonstationary environment. Policy implications are explored and suggestions are made where appropriate. Chapter 1 models common-pool industries which experience exogenous technology shocks. Shocks are modeled as a compound Poisson process for total factor productivity and induce off-equilibrium dynamics that lower the equilibrium resource stock while causing capital buildup. The steady state changes from a stable node to a shifting focus with boom and bust cycles, even if only technology is uncertain. A fisheries application is developed, but the results apply to many settings with discontinuous changes in value and open access with costly exit. Chapter 2 provides a fisheries relevant empirical analysis of the biological dynamics of a fisheries that has undergone a regime shift. Key stock assessment statistics are computed for the Northern anchovy over 1981 -2009. Spatial and temporal variation of the eggs, larvae is characterized and modeled. We find that recent low recruitment productivity that can be attributed largely to poor environmental conditions. An economic appendix explores the determinants of catch in the anchovy fishery. Chapter 3 further explores the modeling and estimation of jump diffusion processes as they apply to financial time series. A framework is proposed for estimating and forecasting realized volatility in the presence of jumps. An optimal method for threshold selection is proposed that minimizes the out-of-sample forecasting loss. We find that large truncation thresholds may not be optimal from a forecasting perspective. An extensive simulation study and an empirical application to S&P 500 futures demonstrate the effectiveness of the proposed method. Chapter 4 models a dynamic environment as regime shifts in a spatial bioeconomic framework. Regime shifts are induced through a cyclic time varying parameter meant to mimic environmental oscillations. The optimal economic resource exploitation policy is derived, explicitly showing the impact of spatial connectedness in this dynamic setting. Through simulation alternative management policies are explored that ignore the spatial connectedness and the corresponding impacts on economic variables and resources stock

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