Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (16 results)

  • Article
  • Peer Reviewed
Recent Work (2016)
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2015)
    I study the asset pricing implications and the efficiency of a tractable dynamic stochastic general equilibrium model with heterogeneous agents and incomplete markets along the lines of Krebs (2003a). Contrary to previous applications of these types of models, I find that generically the distribution of idiosyncratic shocks affects the risk premia of aggregate shocks and that the equilibrium is constrained inefficient in the sense that a planner can Pareto improve the equilibrium outcome by assigning different portfolio choices to agents. The inefficiency is caused by a 'portfolio externality': the average portfolio of the economy affects the portfolio return of each agent. The constrained efficient outcome can be achieved through linear taxes and subsidies that I characterize in closed-form.
      Cover page: Asset prices and efficiency in a Krebs economy
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2014)
      The size distributions of many economic variables seem to obey the double power law, that is, the power law holds in both the upper and the lower tails. I explain this emergence of the double power law-which has important economic, econometric, and social implications-using a tractable dynamic stochastic general equilibrium model with heterogeneous agents subject to aggregate and idiosyncratic investment risks. I establish theoretical properties such as existence, uniqueness, and constrained efficiency of equilibrium, and provide a numerical algorithm that is guaranteed to converge. The model is widely applicable: it allows for arbitrary homothetic CRRA recursive preferences, an arbitrary Markov process governing aggregate shocks, and an arbitrary number of technologies and assets with arbitrary portfolio constraints.
        Cover page: Incomplete market dynamics and cross-sectional distributions
        • Article
        Recent Work (2017)
          Cover page: Wealth Distribution with Random Discount Factors
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2014)
          The maximum entropy principle is a powerful tool for solving underdetermined inverse problems. This paper considers the problem of discretizing a continuous distribution, which arises in various applied fields. We obtain the approximating distribution by minimizing the Kullback-Leibler information (relative entropy) of the unknown discrete distribution relative to an initial discretization based on a quadrature formula subject to some moment constraints. We study the theoretical error bound and the convergence of this approximation method as the number of discrete points increases. We prove that (i) the theoretical error bound of the approximate expectation of any bounded continuous function has at most the same order as the quadrature formula we start with, and (ii) the approximate discrete distribution weakly converges to the given continuous distribution. Moreover, we present some numerical examples that show the advantage of the method and apply to numerically solving an optimal portfolio problem.
            Cover page: Error estimate and convergence analysis of moment-preserving discrete approximations of continuous distributions
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2015)
            The maximum entropy principle is a powerful tool for solving underdetermined inverse problems. This paper considers the problem of discretizing a continuous distribution, which arises in various applied fields. We obtain the approximating distribution by minimizing the Kullback-Leibler information (relative entropy) of the unknown discrete distribution relative to an initial discretization based on a quadrature formula subject to some moment constraints. We study the theoretical error bound and the convergence of this approximation method as the number of discrete points increases. We prove that (i) the theoretical error bound of the approximate expectation of any bounded continuous function has at most the same order as the quadrature formula we start with, and (ii) the approximate discrete distribution weakly converges to the given continuous distribution. Moreover, we present some numerical examples that show the advantage of the method and apply to numerically solving an optimal portfolio problem.
              Cover page: Discretizing Distributions with Exact Moments: Error Estimate and Convergence Analysis
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2015)
              We provide evidence suggesting that the cross-sectional distributions of US consumption and its growth rate obey the power law in both the upper and lower tails, with exponents approximately equal to four. Consequently, high-order moments are unlikely to exist, and the generalized method of moments estimation of Euler equations that employs cross-sectional moments may be inconsistent. Through bootstrap studies, we find that the power law appears to generate spurious nonrejection of heterogeneous-agent asset pricing models in explaining the equity premium. Dividing households into age groups, we propose an estimation approach that appears less susceptible to fat tail issues.
                Cover page: The Double Power Law in Consumption and Implications for Testing Euler Equations
                • Article
                • Peer Reviewed
                Recent Work (2015)
                  Cover page: Discretizing Distributions with Exact Moments: Error Estimate and Convergence Analysis
                  • Article
                  • Peer Reviewed
                  Recent Work (2017)
                    Cover page: Fat tails and spurious estimation of consumption-based asset pricing models
                    • Article
                    • Peer Reviewed
                    UC San Diego Previously Published Works (2013)

                    Background

                    Pre-hospital laryngoscopic endotracheal intubation (ETI) is potentially a life-saving procedure but is a technique difficult to acquire. This study aimed to obtain a recommendation for the number of times ETI should be practiced by constructing the learning curve for endotracheal intubation by paramedics, as well as to report the change in the frequency of complications possibly associated with intubation over the training period.

                    Methods

                    Under training conditions, 32 paramedics performed a total of 1,045 ETIs in an operating room. Trainees performed ETIs until they succeeded in 30 cases. For each patient, the number of laryngoscopic maneuvers and any complications potentially associated with ETI were recorded. We built a generalized logistic model to construct the learning curve for ETI and the frequency of complications.

                    Results

                    During the training on the first 30 patients the rate of ETI success at the first attempt improved from 71% to 87%, but there was little improvement during the first 13 cases. The frequency of complications decreased from 53% to 31%. More laryngoscopic maneuvers and longer operation time increased complications.

                    Conclusions

                    It seems that 30 live experiences of performing an ETI is sufficient for obtaining a 90% ETI success rate, but there seems to be little benefit with fewer than 13 experiences. The frequency of complications remained at a high level even after the training. It is desirable to conduct a more detailed and rigorous evaluation of the benefit of pre-hospital ETI by controlling for the skill level of paramedics.

                      Top