We consider estimation of a linear or nonparametric additive model in which a few coefficients or additive components are large and may be objects of substantive interest, whereas others are small but not necessarily zero. The number of small coefficients or additive components may exceed the...

We consider estimation of a linear or nonparametric additive model in which a few coefficients or additive components are "large" and may be objects of substantive interest, whereas others are "small" but not necessarily zero. The number of small coefficients or additive components may exceed...

Data do not always obey the normality assumption, and outliers can have dramatic impacts on the quality of the least squares methods. We use Huber's loss function in developing robust methods for time-course multivariate responses. We use spline basis expansion of the time-varying regression...

In order to better capture non-R&D based processes related to Learning by Doing, Using and Interacting (DUI) as a basis for policy advice, this paper empirically identifies DUI mode drivers of SME innovation. For the first time, a large set of conceptually derived indicators is used in a...

In order to better capture non-R&D based processes related to Learning by Doing, Using and Interacting (DUI) as a basis for policy advice, this paper empirically identifies DUI mode drivers of SME innovation. For the first time, a large set of conceptually derived indicators is used in a...

The use of the multinomial logit model is typically restricted to applications with few predictors, because in high-dimensional settings maximum likelihood estimates tend to deteriorate. A sparsity-inducing penalty is proposed that accounts for the special structure of multinomial models by...

Varying-coefficient models are useful tools for analyzing longitudinal data. They can effectively describe a relationship between predictors and responses which are repeatedly measured. We consider the problem of selecting variables in the varying-coefficient models via adaptive elastic net...

Bayes factors (BFs) play an important role in comparing the fit of statistical models. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using exact BFs. Instead, it is approximated, often using the Bayesian Information Criterion (BIC) or a...

Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these...

This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...