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Research Seminar Series in Statistics and Mathematics

Wirtschaftsuniversität Wien, Departments 4 D4.4.00812:30 - 14:00

Type Lecture / discussion
SpeakerEric Eisenstat (School of Economics, The University of Queensland, Brisbane, Australia)
Organizer Institut für Statistik und Mathematik

Eric Ei­s­en­stat (School of Eco­nom­ics, The Uni­versity of Queensland, Bris­bane, Aus­tralia) about “Ef­fi­cient Es­tim­a­tion of Struc­tural VARMAs with Stochastic Volat­il­ity”

The In­sti­tute for Stat­ist­ics and Mathem­at­ics (De­part­ment of Fin­ance, Ac­count­ing and Stat­ist­ics) cor­di­ally in­vites every­one in­ter­ested to at­tend the talks in our Re­search Sem­inar Ser­ies, where in­ter­na­tion­ally renowned schol­ars from lead­ing uni­versit­ies present and dis­cuss their (work­ing) pa­pers.

The list of talks for the sum­mer term 2018 is avail­able via the fol­low­ing link:
Sum­mer Term 2018


This pa­per devel­ops Markov chain Monte Carlo al­gorithms for struc­tural vector autore­gress­ive mov­ing aver­age (VARMA) mod­els with fix coef­fi­cients and time-vary­ing er­ror co­v­ari­ances, modeled as a mul­tivari­ate stochastic volat­il­ity pro­cess. A par­tic­u­lar be­ne­fit of al­low­ing for time vari­ation in the co­v­ari­ances in this set­ting is that it in­duces unique­ness in terms of fun­da­mental and vari­ous non-­fun­da­mental VARMA rep­res­ent­a­tions. Hence, it re­solves an im­port­ant is­sue in ap­ply­ing mul­tivari­ate time ser­ies mod­els to struc­tural mac­roe­co­nomic prob­lems. Al­though com­pu­ta­tion in this set­ting is more chal­len­ging, the con­di­tion­ally Gaus­sian nature of the model renders ef­fi­cient sampling al­gorithms feas­ible. The al­gorithm presen­ted in this pa­per uses two in­nov­at­ive ap­proaches to achieve sampling ef­fi­ciency: (i) the time-vary­ing co­v­ari­ances are sampled jointly us­ing particle Gibbs with ances­try sampling, and (ii) the mov­ing aver­age coef­fi­cients are sampled jointly us­ing an ex­ten­sion of the Whittle like­li­hood ap­prox­im­a­tion. We provide Monte Carlo evid­ence that the al­gorithm per­forms well in prac­tice. We fur­ther em­ploy the al­gorithm to assess the ex­tent to which com­monly used SVAR mod­els sat­isfy their un­derly­ing fun­da­ment­al­ness as­sump­tion and the ef­fect that this as­sump­tion has on struc­tural in­fer­ence.

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