Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

This paper picks up on a model developed by Philipov and Glickman (2006) for modeling multivariate stochastic volatility via Wishart processes. MCMC simulation from the posterior distribution is employed to fit the model. However, erroneous mathematical transformations in the full conditionals...

The forecasting literature has identi ed two important issues: (i) several predictors have substantial and statistically signi cant predictive content, although only sporadically, and it is unclear whether this predictive content can be exploited reliably; (ii) in-sample predictive content does...

This research paper is interested to propose an alternative distributed lag model from a multi-dimensional perspective. It is entitled "The Mega-space Distributed Lag Model". The main objective of the Mega-space Distributed Lag Model is to study different micro and macroeconomic scenarios...

We model a large panel of time series as a VAR where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected...

This paper disaggregates energy consumption and GDP data according to end-use to analyze a broad number of developed and developing countries grouped in panels by similar characteristics. Panel long-run causality is assessed with a relatively under-utilized approach recommend by Canning and...

A data-driven version of a portmanteau test for detecting nonlinear types of statistical dependence is considered. An attractive feature of the proposed test is that it properly controls type I error without depending on the number of lags. In addition, the automatic test is found to have higher...

Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...

This short report deals with the recent rise of programmatic time series methods. This decade has witnessed the proliferation of commercial and open source time-series tooling, which calls for an exposition of what is publicly available. In tandem with this survey, AtsPy, an open source...