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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...
Persistent link: https://www.econbiz.de/10012814196
We propose a parsimonious semiparametric method for macroeconomic forecasting during episodes of sudden changes. Based …
Persistent link: https://www.econbiz.de/10011708260
Pair-copula constructions are flexible models for the dependence in a random vector that have attracted a lot of interest in recent years. In this paper, we use generalized additive models to extend pair-copula constructions to allow for effects of covariates on the dependence parameters. We let...
Persistent link: https://www.econbiz.de/10012985827
estimator and discuss bias-corrected point and density forecasting by simulation. The methods are applied to stock market data …
Persistent link: https://www.econbiz.de/10010344500
We propose a new methodology to estimate the empirical pricing kernel implied from option data. In contrast to most of the studies in the literature that use an indirect approach, i.e. first estimating the physical and risk-neutral densities and obtaining the pricing kernel in a second step, we...
Persistent link: https://www.econbiz.de/10013108080
of macroeconomic predictors and technical indicators with the goal of forecasting the S&P 500 equity risk premium. To … illustrate the merit of the proposed approach, we extend the mean-based equity premium forecasting into the conditional quantile … systematic forecasting power. Third, different predictors are identified as important when considering lower, central and upper …
Persistent link: https://www.econbiz.de/10012859663
We propose a new identification and estimation method for the extended Roy model, in which the agents maximize their utility rather than just their outcome. We nonparametrically identify the joint distribution of potential outcomes, which is of great importance particularly in treatment effect...
Persistent link: https://www.econbiz.de/10014243797
Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis. This paper presents an example where nonparametric flexibility can be...
Persistent link: https://www.econbiz.de/10001537161
This paper introduces a new framework for quantile estimation. Quantile regression techniques have proven to be extremely valuable in understanding the relationship between explanatory variables and the conditional distribution of the outcome variable. Quantile regression allows the effect of...
Persistent link: https://www.econbiz.de/10014187130
The field of production frontier estimation is divided between the parametric Stochastic Frontier Analysis (SFA) and the deterministic, nonparametric Data Envelopment Analysis (DEA). This paper explores an amalgam of DEA and SFA that melds a nonparametric frontier with a stochastic composite...
Persistent link: https://www.econbiz.de/10014050905