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Liudas Giraitis

Liudas Giraitis

Location: GC443
Phone: +44 20 7882 8826

Office hours: Friday 14:00-16:00

Research keywords: Econometrics and Quantitative Methods

Download CV: Liudas Giraitis [83 KB]

Liudas Giraitis  is  a Professor  of  Econometrics  at Queen Mary University of  London. He has completed extensive research on long memory and integrated I(d) models  summarized in  recent monograph “Large Sample Inference for Long memory Processes”.

He has done substantial work on ARCH models, semiparametric inference, and  is  interested in development of comprehensive asymptotic theory for sums and quadratic forms of dependent variables and  their statistical and econometric applications.

In ongoing work, he is exploring time  varying random  coefficient models, their properties and estimation methods, forecasting  under ongoing  change approaches and  heteroscadasticity  and mean-variance  constancy testing  procedures.

His research  bridges the fields of econometrics, statistics and  probability theory, with a substantial emphasis on time  series  analysis. He has published numerous articles in the leading statistical and econometric journals.

Liudas  has received his  PhD in from Vilnius University.  He has gained his  research  experience  working at Heidelberg and  Boston  Universities  and  London School of  Economics.


Giraitis, L., Koul, H. and  Surgailis, D. (2012).  Large Sample Inference for Long memory Processes, pp. 587, Imperial College Press.

Giraitis, L., Kapetanios, G. and Yates, T. (2014). Inference on stochastic time-evolving coefficient models. Journal of  Econometrics,  179,  46–65.

Giraitis, L.,  Kapetanios, G. and  Price, S. (2013). Adaptive forecasting in the presence of recent and ongoing structural change. Journal of  Econometrics,  177, 153–-170.

Giraitis, L. and  Phillips, P.C.B.  (2012). Mean and autocovariance function estimation near the boundary of stationarity. Journal of Econometrics, 169, 166-–178.

Abadir, K.,  Distaso, W., Giraitis, L. (2011). An I(d) model with trend and cycles. Journal of Econometrics, 163, 186-199.

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