Prediction Bias Correction for Dynamic Term Structure Models

Dynamic polynomial models for the term structure of interest rates

In this paper we discuss a problem quite debated in the literature, namely how to obtain dynamic versions of cross-sectional models for the term structure of interest rates which satisfies some desirable requirements: (a) providing a good reconstruction of market data; (b) having theoretical control of the dynamic no-arbitrage conditions. These requirements are often conflicting: many empirical...

Combining Global and Local Grid-Based Bias Correction for Mesoscale Numerical Weather Prediction Models

Two methods for objective grid-based bias removal in mesoscale numerical weather prediction models are proposed, one global and one local. The global method is an elaboration of model output statistics (MOS), combining several modern methods for multiple regression: alternating conditional expectation (ACE), regression trees, and Bayesian model selection. This allows the representation of nonli...

Bias correction methods for dynamic panel data models with fixed effects

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although con...

Linear models for structure prediction

Parametric bootstrap methods for bias correction in linear mixed models

The empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation, and the estimation of the mean squared error (MSE) of EBLUP is important as a measure of uncertainty of EBLUP. To obtain a second-order unbiased estimator of the MSE, the second-order bias correction has been derived mainly based on Taylor series expansions. However, thi...