Results 91 to 100 of about 13,851,275 (245)

Sensitivity analysis for unmeasured pretreatment confounders in causal mediation analysis with debiased machine learning

open access: yesBritish Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Sensitivity analysis for unmeasured confounders is essential for assessing the robustness of causal mediation conclusions. Most existing methods rely on parametric assumptions, which are ill‐suited for machine learning‐based estimators that are not tied to specific parametric models.
Xiao Liu, Cameron McCann
wiley   +1 more source

Semiparametric Trending Panel Data Models with Cross-Sectional Dependence [PDF]

open access: yes
A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals.
Jia Chen, Jiti Gao, Degui Li
core  

Demand Shocks, Export Diversification, and Firm Performance During the Great Trade Collapse

open access: yesJournal of Economics &Management Strategy, EarlyView.
ABSTRACT This paper analyzes the impact of external demand shocks from the 2008 to 2009 Great Trade Collapse on the performance of Chinese exporters and examines how export diversification mitigates these effects. Using Chinese Customs data merged with financial data from all listed Chinese firms, we construct firm‐specific demand shocks based on pre ...
Qianlin Hong   +3 more
wiley   +1 more source

Semiparametric Regression Analysis under Imputation for Missing Response Data [PDF]

open access: yes
We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator, a marginal average estimator and a (marginal) propensity score weighted estimator are defined.
Qihua Wang   +2 more
core  

Mixing It Up: Inflation at Risk

open access: yesJournal 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

CONSUMER PRICE INDEX MODELING USING A MIXED TRUNCATED SPLINE AND KERNEL SEMIPARAMETRIC REGRESSION APPROACH

open access: yesBarekeng
Some semiparametric regression model approaches include spline, kernel, Fourier series, and wavelet. Semiparametric regression modelling can involve more than one independent variable (multivariable), a parametric approach is usually combined with one of
Lilik Hidayati   +4 more
doaj   +1 more source

A remark on semiparametric models

open access: yesJournal of Multivariate Analysis, 1986
Consider a semiparametric model (X,A,\({\mathcal P})\) with interesting Euclidean parameter \(\kappa\) : \({\mathcal P}\to R_ k\) to be estimated. Assume that \({\mathcal P}\) is a differentiable family of mutually absolutely continuous probability measures and the parameter \(\kappa\) is a functional on \({\mathcal P}\).
openaire   +3 more sources

Testing Distributional Granger Causality With Entropic Optimal Transport

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley   +1 more source

An Analysis of Housing Expenditure Using Semiparametric Cross-Section Models [PDF]

open access: yes
In this paper we model expenditure on housing for owners and renters by means of endogenous switching regression models using cross-section data. We explain the share of housing in total expenditure from family characteristics and total expenditure ...
Charlier, E.   +2 more
core  

Semiparametric volatility model with varying frequencies

open access: yesCommunications in Statistics - Simulation and Computation
In extracting time series data from various sources, it is inevitable to compile variables measured at varying frequencies as this is often dependent on the source. Modeling from these data can be facilitated by aggregating high frequency data to match the relatively lower frequencies of the rest of the variables.
Jetrei Benedick R. Benito   +2 more
openaire   +2 more sources

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