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McMillan, David G. - 2018

We forecast quarterly US stock returns using eighteen predictor variables both individually and in multivariate … regressions, with the former also used in forecast combinations. Using rolling and recursive approaches, we consider a range of … statistical and economic evaluation measures. We consider linear and non-linear regressions as well as forecast evaluations over …

Persistent link: https://www.econbiz.de/10012909692

Shrinking the Term Structure

Filipović, Damir; Pelger, Markus; Ye, Ye - 2022

We develop a conditional factor model for the term structure of treasury bonds, which unifies non parametric curve estimation with cross-sectional asset pricing. Our factors correspond to the optimal non-parametric basis functions spanning the discount curve. They are investable portfolios...

Persistent link: https://www.econbiz.de/10013403311

Stripping the discount curve : a robust machine learning approach

Filipović, Damir; Pelger, Markus; Ye, Ye - 2022

We introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form...

Persistent link: https://www.econbiz.de/10013169176

Analysis of latent Gaussian models with spatial dependence

Vogler, Jan - 2016

Persistent link: https://www.econbiz.de/10011618511

The role of current account balance in forecasting the US equity premium : evidence from a quantile predictive regression approach

Gupta, Rangan; Majumdar, Anandamayee; Wohar, Mark E. - In: Open economies review 28 (2017) 1, pp. 47-59

Persistent link: https://www.econbiz.de/10011804281

Nelson-Siegel, affine and quadratic yield curve specifications : which one is better at forecasting?

Nyholm, Ken; Vidova-Koleva, Rositsa - 2010

yield curve models ; forecast performance …

Persistent link: https://www.econbiz.de/10003973519

Parametrizing Nonparametric Translog Models : A Goal Programming/Constrained Regression Study of U.S. Manufacturing

Ziari, Houshmand; Azzam, Azzeddine - 1999

This paper demonstrates how Goal Programming/Constrained Regression can be used for cross-checking results from standard econometric models as well as a stand alone methodology in empirical production analysis. For illustration, we re-examine Berndt and Wood's (BW) seminal study of the U.S....

Persistent link: https://www.econbiz.de/10014193098

Distinguishing between long-range dependence and deterministic trends

Sibbertsen, Philipp; Venetis, Ioannis - 2003

We provide a method for distinguishing long-range dependence from deterministic trends such as structural breaks. The method is based on the comparison of standard log-periodogram regression estimation of the memory parameter with its tapered counterpart. The difference of these estimators...

Persistent link: https://www.econbiz.de/10010509839

Stock return autocorrelations revisited : a quantile regression approach

Baur, Dirk G.; Dimpfl, Thomas; Jung, Robert - 2012

The aim of this study is to provide a comprehensive description of the dependence pattern of stock returns by studying a range of quantiles of the conditional return distribution using quantile autoregression. This enables us in particular to study the behavior of extreme quantiles associated...

Persistent link: https://www.econbiz.de/10009411861

Forecasting comparison between two nonlinear models : fuzzy regression versus SETAR

Feng, Hui - In: Applied economics letters 18 (2011) 16/18, pp. 1623-1627

Persistent link: https://www.econbiz.de/10009383409