Similar Search Results

Machine-learning-based semiparametric time series conditional variance : estimation and forecasting

Dang, Justin; Ullah, Aman - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-12

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

Chapter 73 Nonparametric identification

Matzkin, Rosa L. - In: Handbook of econometrics : volume 6B, (pp. 5307-5368). 2007

When one wants to estimate a model without specifying the functions and distributions parametrically, or when one wants to analyze the identification of a model independently of any particular parametric specification, it is useful to perform a nonparametric analysis of identification. This...

Persistent link: https://www.econbiz.de/10014024942

Nonparametric Realized Volatility Estimation in the International Equity Markets

Vortelinos, Dimitrios I. - 2015

Using high-frequency intraday data, we construct, test and model seven new realized volatility estimators for six international equity indices. We detect jumps in these estimators, construct the jump components of volatility and perform various tests on their properties. Then we use the class of...

Persistent link: https://www.econbiz.de/10013029279

Nonparametric Density Estimation by B-spline Duality

Cui, Zhenyu - 2019

In this paper, we propose a new non-parametric density estimator derived from the theory of frames and Riesz bases. In particular, we propose the so-called bi-orthogonal density estimator based on the class of B-splines, and derive its theoretical properties including the asymptotically optimal...

Persistent link: https://www.econbiz.de/10012890658

Confidence corridors for multivariate generalized quantile regression

Chao, Shih-Kang; Proksch, Katharina; Dette, Holger; … - 2014

We focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression … obtain confidence corridors for the regression function in the classical mean regression. In order to deal with the problem …

Persistent link: https://www.econbiz.de/10010354164

Finite Sample Nonparametric Tests for Linear Regressions

Gossner, Olivier; Schlag, Karl H. - 2013

We introduce tests for finite-sample linear regressions with heteroskedastic errors. The tests are exact, i.e., they have guaranteed type I error probabilities when bounds are known on the range of the dependent variable, without any assumptions about the noise structure. We provide upper bounds...

Persistent link: https://www.econbiz.de/10014197050

Identification and Estimation of Regression Models with Misclassification

Mahajan, Aprajit - 2010

This paper studies the problem of identification and estimation in nonparametric regression models with a misclassified … binary regressor where the measurement error may be correlated with the regressors. We show that the regression function is … underlying variable but unrelated to the measurement error. Identification for semi-parametric and parametric regression …

Persistent link: https://www.econbiz.de/10014199229

Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model (SUR)

Billio, Monica - 2017

Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a …

Persistent link: https://www.econbiz.de/10012968298

Correcting Endogeneity via Instrument-Free Two-Stage Nonparametric Copula Control Functions

Hu, Xixi; Qian, Yi; Xie, Hui - National Bureau of Economic Research - 2025

Given the ubiquitous presence of endogenous regressors and the challenges in finding good instruments to overcome the endogeneity problem, a forefront of recent research is the development and application of endogeneity correction methods without requiring instruments. In this article, we...

Persistent link: https://www.econbiz.de/10015361483

The asymptotic variance of semiparametric estimators with generated regressors

Hahn, Jinyong; Ridder, Geert - 2010

first step estimator plays in the second step non-parametric regression, i.e., that of conditioning variable and that of … argument. We consider three examples in more detail: the partial linear regression model estimator with a generated regressor …

Persistent link: https://www.econbiz.de/10008657324