Software related to our research projects
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- 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.
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- 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.
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- Maximum likelihood estimation of a mixture autoregressive model based on Student's t-distribution.
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- Data-driven identification methods for structural vector autoregressive (SVAR) models.
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- 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.
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- Bayesian analysis of statistically identified structural vector autoregression.
- Anttonen, J., M. Lanne, and J. Luoto (2023), Bayesian Inference on Fully and Partially Identified Potentially Non-Gaussian Structural Vector Autoregressions, Available at SSRN: or
- Lanne, M., K. Liu, and J. Luoto, Identifying Structural Vector Autoregressions Via Non-Gaussianity of Potentially Dependent Structural Shocks, Available at SSRN: or