Software related to our research projects
- uGMAR (R package)
- Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR), Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes.
- References:
- Kalliovirta, L. (2012), Misspecification Tests Based on Quantile Residuals, Econometrics Journal 15, 358-393
- Kalliovirta, L., M. Meitz, and P. Saikkonen (2015), A Gaussian Mixture Autoregressive Model for Univariate Time Series, Journal of Time Series Analysis 36, 247 - 266
- Meitz, M., D. Preve, and P. Saikkonen (2023), A Mixture Autoregressive Model Based on Student’s t–distribution, Communications in Statistics - Theory and Methods, 53, 498 - 514
- Virolainen, S. (2022), A Mixture Autoregressive Model Based on Gaussian and Student’s t-distributions, Studies in Nonlinear Dynamics & Econometrics, 26, 559 - 580
- gmvarkit (R package)
- Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition.
- References:
- StMAR Toolbox (Matlab toolbox)
- Maximum likelihood estimation of a mixture autoregressive model based on Student's t-distribution.
- Reference:
- svars (R package)
- Data-driven identification methods for structural vector autoregressive (SVAR) models.
- References:
- Lanne, M., and H. Lütkepohl (2008), Identifying Monetary Policy Shocks via Changes in Volatility, Journal of Money, Credit and Banking 40, 1131 - 1149
- Lanne, M., M. Meitz, and P. Saikkonen (2017), Identification and Estimation of Non-Gaussian Structural Vector Autoregressions, Journal of Econometrics 196, 288 - 304
- Maxand, S. (2020), Identification of Independent Structural Shocks in the Presence of Multiple Gaussian Components. Econometrics and Statistics, 16, 55 - 68
- sstvars (R package)
- Maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions.
- rbsvar (R package)
- Bayesian analysis of statistically identified structural vector autoregression.
- Anttonen, J., M. Lanne and J. Luoto (2024), Statistically Identified Structural VAR Model with Potentially Skewed and Fat-Tailed Errors. Journal of Applied Econometrics, 39, 422 – 437
- Anttonen, J., M. Lanne, and J. Luoto (2023), Bayesian Inference on Fully and Partially Identified Potentially Non-Gaussian Structural Vector Autoregressions, Available at SSRN: https://ssrn.com/abstract=4358059 or http://dx.doi.org/10.2139/ssrn.4358059
- Lanne, M., K. Liu, and J. Luoto, Identifying Structural Vector Autoregressions Via Non-Gaussianity of Potentially Dependent Structural Shocks, Available at SSRN: https://ssrn.com/abstract=4564713 or http://dx.doi.org/10.2139/ssrn.4564713