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, 2001 Much empirical research is concerned with estimating conditional mean, median, or hazard functions. For example, labor economists are interested in estimating the mean wages of employed individuals conditional on characteristics such as years of work ...
Horowitz, Joel L.
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Semiparametric Fractional Cointegration Analysis [PDF]
Journal of Econometrics, 2001 Fractional cointegration is viewed from a semiparametric viewpoint as a narrow-band phenomenon at frequency zero. We study a narrow-band frequency domain least squares estimate of the cointegrating vector, and related semiparametric methods of inference ...
D Marinucci, Peter M Robinson
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Semiparametric multivariate volatility models [PDF]
Econometric Theory, 2007 Estimation of multivariate volatility models is usually carried out by quasi maximum likelihood (QMLE), for which consistency and asymptotic normality have been proven under quite general conditions. However, there may be a substantial efficiency loss of
Hafner, Christian M. +1 more
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Semiparametric Bayesian networks
Information Sciences, 2022 We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones.
Atienza González, David +2 more
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Semiparametric Vector MEM [PDF]
SSRN Electronic Journal, 2008 SUMMARYFinancial time series are often non‐negative‐valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector multiplicative error model (vMEM; the element‐by‐element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d.
CIPOLLINI, FABRIZIO +2 more
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Semiparametric Duration Models [PDF]
Journal of Business & Economic Statistics, 2004 In this article we consider semiparametric duration models and efficient estimation of the parameters in a non-iid environment. In contrast to classical time series models where innovations are assumed to be iid we show that in, for example, the often-used autoregressive conditional duration (ACD) model, the assumption of independent innovations is too
Drost, F.C., Werker, B.J.M.
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Semiparametric Regression Pursuit [PDF]
Statistica Sinica, 2012 The semiparametric partially linear model allows flexible modeling of covariate effects on the response variable in regression. It combines the flexibility of nonparametric regression and parsimony of linear regression. The most important assumption in the existing methods for the estimation in this model is to assume a priori that it is known which ...
Jian, Huang, Fengrong, Wei, Shuangge, Ma
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Bayesian Semiparametric Multiple Shrinkage [PDF]
Biometrics, 2010 SummaryHigh‐dimensional and highly correlated data leading to non‐ or weakly identified effects are commonplace. Maximum likelihood will typically fail in such situations and a variety of shrinkage methods have been proposed. Standard techniques, such as ridge regression or the lasso, shrink estimates toward zero, with some approaches allowing ...
MacLehose, Richard F., Dunson, David B.
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Nonparametric and semiparametric estimation with discrete regressors [PDF]
, 1994 This paper presents and discusses procedures for estimating regression curves when regressors are discrete and applies them to semiparametric inference problems.
Delgado, Miguel A., Mora, Juan
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Semiparametric Proximal Causal Inference
Journal of the American Statistical Association, 2023 Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchangeability hinges on an investigator’s ability to accurately measure covariates that capture all potential sources of confounding.
Yifan Cui +4 more
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