We estimate the effects of student loan access on educational attainment and labor market returns in New Zealand. We exploit the introduction of a national policy mandating a 50% pass rate for student loan renewals using a regression discontinuity design. Retaining loan access increases reenrollment for students around the threshold, and a majority eventually graduate with a bachelor's degree within seven years. We find that retaining student loan access leads to large labor market returns for struggling students. The additional debt from further borrowing is small relative to the earnings returns and declines quickly due to faster repayment.

The problem of demand inversion—a crucial step in the estimation of random utility discrete-choice models—is equivalent to the determination of stable outcomes in two-sided matching models. This equivalence applies to random utility models that are not necessarily additive, smooth, nor even invertible. Based on this equivalence, algorithms for the determination of stable matchings provide effective computational methods for estimating these models. For non-invertible models, the identified set of utility vectors is a lattice, and the matching algorithms recover sharp upper and lower bounds on the utilities. Our matching approach facilitates estimation of models that were previously difficult to estimate, such as the pure characteristics model. An empirical application to voting data from the 1999 European Parliament elections illustrates the good performance of our matching-based demand inversion algorithms in practice.

In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting.