Abstract Parametric (polynomial) models are popular in research employing regression discontinuity designs and are required when data are discrete. However, researchers often choose a parametric model based on data inspection or pretesting. These approaches lead to standard errors and confidence...

In this paper, we propose a model-selection approach to testing the expectations theory of the term structure of interest rates. Our method is based on the posterior information criterion (PIC) developed and analyzed by Phillips and Ploberger (1994, 1996) and extended to provide order estimation...

This paper presents the asymptotic and finite sample properties of the efficient method of moments and indirect inference, when applied to estimating stationary ARMA models. Issues such as identification, model selection, and testing are also discussed. The properties of these estimators are...

Abstract We consider a Lévy driven stochastic convolution, also called continuous time Lévy driven moving average model X(t)=\int_{0}^{t}a(t-s)\,dZ(s) , where 𝑍 is a Lévy martingale and the kernel a(\,{.}\,) a deterministic function square integrable on \mathbb{R}^{+} . Given 𝑁 i.i.d....

Summary A common assumption in supervised learning is that the training and test input points follow the same probability distribution. However, this assumption is not fulfilled, e.g., in interpolation, extrapolation, active learning, or classification with imbalanced data. The violation of this...

Abstract We are interested in the problem of estimating a regression function φ observed with a correlated noise Y  =  φ ( X )+ U . Contrary to the usual regression model, U is not centered conditionaly on X but rather on an observed variable W . Hence this model turns to be a difficult...

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each...

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance...

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate...

Florax et al. [Florax, R.J.G.M., Folmer, H., Rey, S.J., 2003. Specification searches in spatial econometrics: the relevance of Hendry's methodology. Regional Science and Urban Economics, 33, 557–579] undertook a simulation study designed to assess the properties of various selection strategies...