Description: Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2018, <doi:10.2139/ssrn.2752354>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.

Imports: Rcpp, graphics, stats, numDeriv, zoo, maxLik

Description: Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.

Packaged: 2021-06-17 14:00:19 UTC; onnokleen

Title: Fast Covariance Estimation for Multivariate Sparse Functional
Author: Cai Li [aut,cre] and Luo Xiao [aut]
Description: Multivariate functional principal component analysis via fast covariance estimation for
multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi:10.1002/sta4.245>.
Imports: stats, splines, Matrix, matrixcalc, mgcv, face

multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.

Title: Fitting and Simulation of a block multifactor model
Description: Implementation of the simulated method of moments (smm) of Oh and Patton for copula-based multivariate models

Title: Mixed-Frequency Bayesian VAR Models
person("Sebastian", "Ankargren", email = "sebastian.ankargren@statistics.uu.se", role = c("cre", "aut"), comment = c(ORCID = "0000-0003-4415-8734")),
person("Yukai", "Yang", email = "yukai.yang@statistics.uu.se", role = c("aut")))
Description: Estimation of mixed-frequency Bayesian vector autoregressive (VAR) models with Minnesota or steady-state priors. The package implements a state space-based VAR model that handles mixed frequencies of the data. The model is estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution, where the prior can be either the Minnesota prior, as used by Schorfheide and Song (2015) <doi:10.1080/07350015.2014.954707>, or the steady-state prior, as advocated by Ankargren, Unosson and Yang (2018) <http://uu.diva-portal.org/smash/get/diva2:1260262/FULLTEXT01.pdf>.
Imports: Rcpp (>= 0.12.7), ggplot2 (>= 2.2.1), methods, pbapply, utils
Packaged: 2018-12-17 07:26:00 UTC; sebastianankargren