Results 101 to 110 of about 13,851,275 (245)

Parametric Time‐Variation in the Unconditional Volatility: Estimation and Inference

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose modeling time‐variation in the unconditional volatility by augmenting the standard GARCH model by a deterministic time‐varying intercept. The model, called the additive time‐varying (ATV‐)GARCH model, can be interpreted as a reduced form of a model including covariates and can be derived from a multiplicative decomposition of ...
Niklas Ahlgren, Alexander Back, Timo Teräsvirta   +2 more
wiley   +1 more source

Semiparametric Estimation in Simultaneous Equations of Time Series Models [PDF]

A system of vector semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components.
Jiti Gao, Peter C. B. Phillips
core  

THE PARAMETRIC AND NONPARAMETRIC ESTIMATOR IN SEMIPARAMETRIC REGRESSION FOR LONGITUDINAL DATA WITH SPLINE APPROACH

Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi, 2023
Regression analysis aims to determine the relationship between response variables and predictor variables. There are three approaches to estimate regression curves, there are parametric, nonparametric, and semiparametric regression.
Tony Yulianto, Kuzairi Kuzairi, Noer Azizah, M. Fariz Fadillah Mardianto, Ira Yuditira, Faisol Faisol, Rica Amalia   +6 more
doaj  

Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint. [PDF]

Comput Manag Sci, 2023
Staino A, Russo E, Costabile M, Leccadito A.   +3 more
europepmc   +1 more source

Partial Time‐Varying Regression Modelling Under General Heterogeneity

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper studies a semiparametric time‐varying regression model in which a subset of regressors is associated with fixed parameters, while the remaining regressors have parameters that evolve smoothly over time. We propose a closed‐form semiparametric Frisch‐Waugh‐Lovell estimator for the fixed parameters, and a non‐parametric kernel type ...
Liudas Giraitis, George Kapetanios, Yufei Li, Tien Chuong Nguyen   +3 more
wiley   +1 more source

Model Averaging with AIC Weights for Hypothesis Testing of Hormesis at Low Doses

Dose-Response, 2017
For many dose–response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we
Steven B. Kim, Nathan Sanders
doaj   +1 more source

A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data. [PDF]

Biometrics, 2022
Liu J, Zhang X, Chen T, Wu T, Lin T, Jiang L, Lang S, Liu L, Natarajan L, Tu JX, Kosciolek T, Morton J, Nguyen TT, Schnabl B, Knight R, Feng C, Zhong Y, Tu XM.   +17 more
europepmc   +1 more source

Semiparametric Bayesian measurement error modeling

Journal of Multivariate Analysis, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
María Paz Casanova, Pilar Loreto Iglesias, Heleno Bolfarine, Victor H. Salinas, Alexis Peña   +4 more
openaire   +4 more sources

Exploring Spatial Patterns in Inefficiency Using a Stationary Gaussian Random Field

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT This article presents a semiparametric stochastic frontier model that accounts for unobserved spatial heterogeneity in inefficiency by incorporating a stationary Gaussian random field based on geographic coordinates into the inefficiency scaling function. This approach captures spatial patterns in inefficiency at a fine scale.
Ioannis Skevas
wiley   +1 more source

Quantile estimation of semiparametric model with time-varying coefficients for panel count data. [PDF]

PLoS One, 2021
Wang Y, Wang W.
europepmc   +1 more source