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筛选: 创造者 Wolpin, Kenneth I. 删除限定条件 创造者: Wolpin, Kenneth I.

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  • Kp78gg41b?file=thumbnail
    Creator: Keane, Michael P. and Wolpin, Kenneth I.
    Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department)
    Number: 559
    Abstract:

    This paper provides structural estimates of a dynamic model of schooling, work, and occupational choice decisions based on 11 years of observations on a sample of young men from the 1979 youth cohort of the National Longitudinal Surveys of Labor Market Experience (NLSY). The structural estimation framework that we adopt fully imposes the restrictions of the theory and permits an investigation of whether such a theoretically restricted model can succeed in quantitatively fitting the observed data patterns. We find that a suitably extended human capital investment model can in fact do an excellent job of fitting observed data on school attendance, work, occupational choices, and wages in the NLSY data on young men and also produces reasonable forecasts of future work decisions and wage patterns.

  • Xp68kg31v?file=thumbnail
    Creator: Keane, Michael P. and Wolpin, Kenneth I.
    Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department)
    Number: 181
    Abstract:

    Over the past decade, a substantial literature on the estimation of discrete choice dynamic programming (DC-DP) models of behavior has developed. However, this literature now faces major computational barriers. Specifically, in order to solve the dynamic programming (DP) problems that generate agents' decision rules in DC-DP models, high dimensional integrations must be performed at each point in the state space of the DP problem. In this paper we explore the performance of approximate solutions to DP problems. Our approximation method consists of: 1) using Monte Carlo integration to simulate the required multiple integrals at a subset of the state points, and 2) interpolating the non-simulated values using a regression function. The overall performance of this approximation method appears to be excellent, both in terms of the degree to which it mimics the exact solution, and in terms of the parameter estimates it generates when embedded in an estimation algorithm.