This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...

This note provides a nonparametric test for dynamic discrete random utility models (DDRUM). The methodology can be used to understand the welfare implications of price changes in demand data when there is no uncertainty for consumers that have preferences over product characteristics or discrete...

We use nonparametric methods to compute the environmental inefficiency of 100 countries over the period 1990-2017 on the macro-level. The inefficiency is expressed as the potential reduction of GHG emissions, holding economic output constant. Based on that, we estimate different trend functions...

In this paper, we propose a two-step less volatile value-at-risk (LVaR) estimation using the generalized nearly-isotonic regression (GNIR) model. The first step of our LVaR estimation is to produce a VaR sequence under the generalized autoregressive conditional heteroskedasticity (GARCH)...

Two election forensics methods provide estimates of the magnitude of election fraud: Bayesian version of the finite mixture model developed by Walter Mebane and his students, and nonparametric approach based on vote-turnout histograms proposed by Sergey Shpilkin. While Mebane's approach offers...

We provide nonparametric quantile regressions to test for autocorrelation patterns for weekly and monthly stock returns. We test in four developed markets (North America, Europe, Japan, and Asia without Japan) and five market-size portfolios. We find greater heteroskedasticity for the...

Testing for and measuring habitual brand loyalty (HBL) is one of the classic questions in quantitative marketing. We propose a nonparametric test for HBL using a ``dynamic potential outcomes'' model that resolves the classic identification challenge of decoupling state dependence and unobserved...