Stefano Grassi

University of Kent - Canterbury Campus

Keynes College

Canterbury, Kent CT2 7NP

United Kingdom

SCHOLARLY PAPERS

6

DOWNLOADS

542

SSRN CITATIONS
Rank 36,875

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Top 36,875

in Total Papers Citations

2

CROSSREF CITATIONS

13

Scholarly Papers (6)

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

Norges Bank Working Paper 11 | 2014
Number of pages: 27 Posted: 04 May 2015
University Ca' Foscari of Venice - Department of Economics, University of Kent - Canterbury Campus, Free University of Bolzano and Tinbergen Institute
Downloads 25 (508,976)
Citation 2

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Density forecast combination, sequential Monte Carlo, parallel computing, GPU, MATLAB

2.

Time-Varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies

Tinbergen Institute Discussion Paper 16-099/III
Number of pages: 44 Posted: 22 Nov 2016
Maastricht University - Department of Quantitative Economics, University of Kent - Canterbury Campus, VU University Amsterdam and Tinbergen Institute
Downloads 201 (151,194)

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nonlinear, non-gaussian state space, filters, density combinations, bayesian modeling, equity momentum

Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance

Tinbergen Institute Discussion Paper 15-084/III
Number of pages: 73 Posted: 21 Jul 2015 Last Revised: 06 Mar 2017
University Ca' Foscari of Venice - Department of Economics, University of Kent - Canterbury Campus, Free University of Bolzano and Tinbergen Institute
Downloads 157 (188,622)
Citation 4

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Density Combination, Large Set of Predictive Densities, Compositional Factor Models, Nonlinear State Space, Bayesian Inference, GPU Computing

Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance

Norges Bank Working Paper No. 12/2015
Number of pages: 58 Posted: 08 Aug 2015
University Ca' Foscari of Venice - Department of Economics, University of Kent - Canterbury Campus, Free University of Bolzano and Tinbergen Institute
Downloads 24 (515,224)
Citation 5

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Density Combination, Large Set of Predictive Densities, Compositional Factor Models, Nonlinear State Space, Bayesian Inference, GPU Computing

The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference

Tinbergen Institute Discussion Paper 15-042/III
Number of pages: 43 Posted: 31 Mar 2015 Last Revised: 08 Jul 2017
Maastricht University - Department of Quantitative Economics, University of Kent - Canterbury Campus, VU University Amsterdam, Vrije Universiteit Amsterdam and Tinbergen Institute
Downloads 51 (391,919)

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finite mixtures, Student-t densities, importance sampling, MCMC, Metropolis-Hastings algorithm, expectation maximization, Bayesian inference, R-software

The R Package Mitisem: Efficient and Robust Simulation Procedures for Bayesian Inference

Norges Bank Working Paper 10/2017
Number of pages: 43 Posted: 19 Jul 2017
Maastricht University - Department of Quantitative Economics, University of Kent - Canterbury Campus, VU University Amsterdam, Vrije Universiteit Amsterdam and Tinbergen Institute
Downloads 17 (560,385)

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finite mixtures, Student-t densities, importance sampling, MCMC, MetropolisHastings algorithm, expectation maximization, Bayesian inference, R software

5.

A Data-Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models

CEIS Working Paper No. 374
Number of pages: 30 Posted: 31 Mar 2016
Martyna Marczak, Tommaso Proietti and Stefano Grassi
University of Hohenheim, University of Rome II - Department of Economics and Finance and University of Kent - Canterbury Campus
Downloads 48 (395,674)

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robust filtering, augmented Kalman filter, structural time series model, additive outlier, innovation outlier

6.

Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM

Tinbergen Institute Discussion Paper 16-005/III
Number of pages: 26 Posted: 25 Jan 2016
Maastricht University - Department of Quantitative Economics, University of Kent - Canterbury Campus, VU University Amsterdam and Tinbergen Institute
Downloads 19 (528,674)

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finite mixtures, Student-t distributions, Importance Sampling, MCMC, Metropolis-Hastings algorithm, Expectation Maximization, Bayesian inference