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The paper is a sequel to Won (2016) which attempts to characterize full-rank GEI equilibrium as pre-GEI equilibrium. The pre-GEI equilibrium approach for computing full-rank GEI equilibrium is useful because pre-GEI equilibrium always exist under standard conditions. The goal of the current paper is to develop algorithms which can be implemented to compute pre-GEI equilibrium. The algorithms are built on a prototype system of equations which consist of the first-order conditions for utility maximization and market clearing conditions. The prototype system can be directly encoded into an algorithm or can be transformed into implementable forms for algorithms such as homotopy path-following algorithms. Two examples are presented where the algorithms are implemented to compute pre-GEI equilibrium and their performance are comparatively discussed.

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In this paper we estimate the impact of the statutory labor hours cut in Korea on monthly labor earnings by the regression discontinuity design (RDD) method.
The implementation of the statutory labor hours cut policy was sequentially extended based on the number of corporation's employees.
The estimation results show that the statutory labor hours cut did not make the workers receiving the treatment better off on average throughout the entire period of 2004-2008 in the sense that it raised monthly labor earnings. However, the policy intervention is found to substantially improve the welfare of workers in the treatment group in 2007 and 2008.
Those results imply that the effect of the policy implementation on a worker's welfare depends on the size of the worker's corporation. The policy intervention is more likely to affect workers at large firms favorably than those at small firms.
In particular, workers at corporations having at least 300 employees are the main beneficiaries of the statutory labor hours cut policy in Korea.

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This study tries to build the financial early warning system (EWS) of the individual financial sector such as banks, securities and savings-loans banks by applying the non-parametric signal approach and to establish a new composite EWS. The empirical results show that the financial sector’s EWSs appeared to identify the financial sector’s crisis timely and the new composite EWS seemed to be very similar with the existing EWS. This study suggests that the financial sector EWS is useful for conducting the microprudential policy based on the financial sector’s characteristics and relating to the implementation of the macroprudential policy for financial stability.

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We develop a new Bayesian Markov Chain Monte Carlo algorithm for Euler-discretized Feller square-root stochastic volatility models and demonstrate the performance of our algorithm through simulations and empirical analyses. Specifically, our algorithm use the Laplace approximation of the posterior density of conditional variance, which is the probability kernel of the generalized inverse gaussian distribution, derived from the joint density of return and conditional variance so that it can be easily applied to the extended stochastic volatility models with such as fat-tailed distributions or Levy jump processes. In addition, we conduct the simulation experiment investigating and comparing the size and power of the parametric specification tests checking certain finite-dimensional moment conditions without correction for parameter estimation uncertainty with that of the nonparametric Hong and Li (2005)’s omnibus test which is not affected by parameter estimation uncertainty. The parametric and nonparametric tests are based on the probability integral transform of the prediction densities of returns obtained using auxiliary particle filter algorithms. Our experiment result shows that the classical parametric specification test may have no worse size distortion and better power than Hong and Li (2005)’s test.