We present a comprehensive simulation study to assess and compare the performance of popular machine learning algorithms for time series prediction tasks. Specifically, we consider the following algorithms: multilayer perceptron (MLP), logistic regression, naïve Bayes, knearest neighbors,...

Partial cointegration is a weakening of cointegration that allows for the "cointegrating" process to contain a random walk and a mean-reverting component. We derive its representation in state space, provide a maximum likelihood based estimation routine, and a suitable likelihood ratio test....

Partial cointegration is a weakening of cointegration, allowing for the residual series to contain a mean-reverting and a random walk component. Analytically, the residual series is described by a partially autoregressive process. The partialCI package provides estimation, testing, and...

Partial cointegration is a weakening of cointegration, allowing for the residual series to contain a mean-reverting and a random walk component. Analytically, the residual series is described by a partially autoregressive process. The partialCI package provides estimation, testing, and...

This paper establishes a selection of stylized facts for high-frequency cointegrated processes, based on one-minute-binned transaction data. A methodology is introduced to simulate cointegrated stock pairs, following none, some or all of these stylized facts. AR(1)-GARCH(1,1) and...

Machine learning is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts based on traditional time series models. For independent and identically distributed observations, cross-validation is the prevalent scheme for estimating out-of-sample...