No packages match

MSTest - Hypothesis Testing for Markov Switching Models

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez-Rondon & Dufour (2026a) <doi:10.34989/swp-2026-23> that can be used to identify the number of regimes in Markov switching models. For a detailed description of the package, see Rodriguez-Rondon & Dufour (2026b) <doi:10.34989/swp-2026-7>.

Last updated

autoregressivebootstraphypothesis-testinglikelihood-ratio-testmarkov-chainmomentsmonte-carlonon-linearregime-switchingtime-seriesopenblascpp

5.60 score 6 stars 12 scripts 347 downloads

wARMASVp - Winsorized ARMA Estimation for Higher-Order Stochastic Volatility Models

Estimation, simulation, hypothesis testing, AR-order selection, and forecasting for univariate higher-order stochastic volatility SV(p) models. Supports Gaussian, Student-t, and Generalized Error Distribution (GED) innovations, with optional leverage effects. Estimation uses closed-form Winsorized ARMA-SV (W-ARMA-SV) moment-based methods that avoid numerical optimization. Hypothesis testing includes Local Monte Carlo (LMC) and Maximized Monte Carlo (MMC) procedures for leverage effects, heavy tails, and autoregressive order. AR-order selection is also available via information criteria (BIC/AIC) using the Kalman-filter quasi-likelihood and the Hannan-Rissanen ARMA residual variance. The Gaussian-mixture Kalman filter uses precomputed optimal mixture tables (deterministic and fast), with the sampled EM fit and the Kim, Shephard and Chib (1998) table retained as options. Forecasting is based on Kalman filtering and smoothing. See Ahsan and Dufour (2021) <doi:10.1016/j.jeconom.2021.03.008>, Ahsan, Dufour, and Rodriguez-Rondon (2025) <doi:10.1111/jtsa.12851>, and Ahsan, Dufour, and Rodriguez-Rondon (2026) <doi:10.34989/swp-2026-8> for details.

Last updated

openblascpp

4.70 score 1 stars 356 downloads