Results 41 to 50 of about 167,561,462 (173)
Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors [PDF]
We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design.
Toshio Honda
core
Market Uncertainty, Investor Sentiment, and the Beta Premium
Financial Review, EarlyView.ABSTRACT This paper provides new insights into how time‐variation in the beta premium is related to popular measures of market uncertainty and sentiment; specifically, the CBOE's VIX and the sentiment index of Baker‐Wurgler (2006). We show that the beta premium: (1) increases nonlinearly with uncertainty, rising significantly only when VIX exceeds a ...
Naresh Bansal, Chris Stivers
wiley +1 more source
Nonparametric regression for dependent data in the errors-in-variables problem [PDF]
We consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression ...
Toshio Honda
core
Bias Adjustment for Mean Squared Error Estimation in M‐Quantile Models for Small Area Estimation
International Statistical Review, EarlyView.Summary M‐quantile (MQ) regression provides a robust and flexible alternative to mixed models for small area estimation. However, several theoretical aspects remain underexplored. In this paper, a parametric bootstrap method is proposed to approximate the distributions of area‐specific MQ coefficients and applied to adjust the bias in the mean squared ...
María Bugallo +3 more
wiley +1 more source
Estimating a convex function in nonparametric regression [PDF]
A new nonparametric estimate of a convex regression function is proposed and its stochastic properties are studied. The method starts with an unconstrained estimate of the derivative of the regression function, which is firstly isotonized and then ...
Dette, Holger, Birke, Melanie
core
Goodness‐of‐Fit Tests for Positive Quadrant Dependence
International Statistical Review, EarlyView.Summary When two random variables are positive quadrant dependent (PQD), they are more likely to assume small (or large) values simultaneously compared with when the random variables are independent. This dependence structure is of interest in many areas, including finance, actuarial science and engineering.
Chuan‐Fa Tang, Joshua M. Tebbs
wiley +1 more source
Missing Values in Time Series: A Brief Review and a New Versatile Imputation Method
International Statistical Review, EarlyView.Summary Missing data can significantly hamper standard time series analysis, yet they occur frequently in applications. In this paper, we briefly review some available methods for handling missing values and introduce the temporal Wasserstein imputation, a novel method for imputing missing data in time series.
Shuo‐Chieh Huang +2 more
wiley +1 more source
Mixing It Up: Inflation at Risk
Journal of Money, Credit and Banking, EarlyView.Abstract Understanding how risk factors shape the economic outlook is essential for guiding policy decisions. This paper develops a flexible framework that decomposes distributional risk forecasts of macro‐economic variables into underlying contributions and supports the construction of interpretable risk measures.
MAXIMILIAN SCHRÖDER
wiley +1 more source
Supplement to "Quantile-Based Nonparametric Inference for First-Price Auctions" [PDF]
This paper contains supplemental materials for Marmer and Shneyerov (2010). We discuss here how the approach developed in the aforementioned paper can be applied to conducting inference on the optimal reserve price in first-price auctions, report ...
Shneyerov, Artyom, Marmer, Vadim
core
Network Effects in Corporate Emissions: Evidence from a Data‐Dependent Spatial Panel Model
Journal of Money, Credit and Banking, EarlyView.Abstract We study spillover effects in corporate toxic emissions using a heterogeneous panel of U.S. industrial facilities from 2000 to 2023. Rather than imposing a network structure a priori, we uncover an unobserved web of influence directly from the data using recent advances in high‐dimensional network econometrics.
STYLIANOS ASIMAKOPOULOS +3 more
wiley +1 more source