Showing 9 of total 9 results (show query)
matthieustigler
tsDyn:Nonlinear Time Series Models with Regime Switching
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Maintained by Matthieu Stigler. Last updated 5 months ago.
18.6 match 34 stars 10.53 score 684 scripts 3 dependents
svazzole
sparsevar:Sparse VAR/VECM Models Estimation
A wrapper for sparse VAR/VECM time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Sumanta Basu and George Michailidis <doi:10.1214/15-AOS1315>.
Maintained by Simone Vazzoler. Last updated 4 years ago.
econometricslassomcpscadsparsestatisticstime-seriesvarvecm
23.5 match 11 stars 5.69 score 30 scripts 1 dependents
tidyverts
fable:Forecasting Models for Tidy Time Series
Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.
Maintained by Mitchell OHara-Wild. Last updated 4 months ago.
7.6 match 569 stars 13.54 score 2.1k scripts 6 dependents
bpfaff
urca:Unit Root and Cointegration Tests for Time Series Data
Unit root and cointegration tests encountered in applied econometric analysis are implemented.
Maintained by Bernhard Pfaff. Last updated 10 months ago.
7.7 match 6 stars 8.95 score 1.4k scripts 270 dependents
rjdverse
rjd3revisions:Revision analysis with 'JDemetra+ 3.x'
Revision analysis tool part of 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It performs a battery of tests on revisions and submit a report with the results. The various tests enable the users to detect potential bias and sources of inefficiency in preliminary estimates.
Maintained by Corentin Lemasson. Last updated 11 days ago.
5.1 match 3 stars 5.01 score 4 scriptsbpfaff
vars:VAR Modelling
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Maintained by Bernhard Pfaff. Last updated 1 years ago.
1.9 match 7 stars 8.84 score 2.8k scripts 45 dependents
cran
GVARX:Perform Global Vector Autoregression Estimation and Inference
Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.
Maintained by Ho Tsung-wu. Last updated 2 years ago.
6.2 match 5 stars 1.70 score
gianmarco-v
bootCT:Bootstrapping the ARDL Tests for Cointegration
The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - <doi:10.1002/jae.616>) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.
Maintained by Gianmarco Vacca. Last updated 1 years ago.
3.0 match 2.00 score 3 scriptscran
cif:Cointegrated ICU Forecasting
Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, <https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf>.
Maintained by Paolo Paruolo. Last updated 3 years ago.
3.4 match 1.00 score