Vasilis Sarafidis

Professor - Institutt for samfunnsøkonomi


For further information, please see my personal webpage.

Vasilis Sarafidis' educational background is in the areas of economics and econometrics. He received his BA in Economics with Computing and Quantitative Methods at the University of Sussex, and his MPhil in Economics and Econometrics at the University of Cambridge. He completed his PhD in Econometrics in 2006 at the University of Cambridge. Prior to joining BI, he was Associate Professor at the Department of Econometrics and Business Statistics, Monash University, Melbourne.

Vasilis' main area of research lies in the econometric analysis of panel data. His focus lies on model specification and testing, dynamic panels, common factor structures, models for spatial interactions and networks, multi-dimensional panels, and random coefficient models; with applications to the analysis of crime, banking, water-usage demand, among others.

Before studying for his PhD, Vasilis worked as an economist for a private consultancy in the City of London. During that time he led projects involving econometric analysis and modelling for the electricity, water and telecommunications sectors. As a consultant he advised, among others, the Office for Water Services (U.K.), the National Telecom Agency in Denmark, the Jersey Competition Regulatory Authority and the Electricity Association (U.K.) During his academic career, he has also acted as an advisor to the Productivity Commission (Australia), the Essential Services Commission in Victoria, the Commonwealth Environmental Office, the NSW Independent Pricing and Regulatory Tribunal, the Bureau of Airline Representatives of Australia, and the NSW Corrective Services.

Research areas
Panel data analysis; model specification and testing; high-dimensional data; models for spatial interactions and networks.

Teaching areas
GRA 6039 Econometrics with Programming


Sarafidis, Vasilis & Wansbeek, Tom (2021)

Celebrating 40 years of panel data analysis: Past, present and future

Journal of Econometrics, 220(2), s. 215- 226. Doi: 10.1016/j.jeconom.2020.06.001

The present special issue features a collection of papers presented at the 2017 International Panel Data Conference, hosted by the University of Macedonia in Thessaloniki, Greece. The conference marked the 40th anniversary of the inaugural International Panel Data Conference, which was held in 1977 at INSEE in Paris, under the auspices of the French National Centre for Scientific Research. As a collection, the papers appearing in this special issue of the Journal of Econometrics continue to advance the analysis of panel data, and paint a state-of-the-art picture of the field.

Kripfganz, Sebastian & Sarafidis, Vasilis (2021)

Instrumental-variable estimation of large-T panel-data models with common factors

The Stata Journal, 21(3), s. 1- 28. Doi: 10.1177/1536867X211045558

In this article, we introduce the xtivdfreg command, which implements a general instrumental-variables (IV) approach for fitting panel-data models with many time-series observations, T, and unobserved common factors or interactive effects, as developed by Norkute et al. (2021, Journal of Econometrics 220: 416–446) and Cui et al. (2020a, ISER Discussion Paper 1101). The underlying idea of this approach is to project out the common factors from exogenous covariates using principal-components analysis and to run IV regression in both of two stages, using defactored covariates as instruments. The resulting two-stage IV estimator is valid for models with homogeneous or heterogeneous slope coefficients and has several advantages relative to existing popular approaches. In addition, the xtivdfreg command extends the two-stage IV approach in two major ways. First, the algorithm accommodates estimation of unbalanced panels. Second, the algorithm permits a flexible specification of instruments. We show that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular Stata ivregress command. Notably, unlike ivregress, xtivdfreg permits estimation of the two-way error-components paneldata model with heterogeneous slope coefficients.

Juodis, Artūras & Sarafidis, Vasilis (2021)

An incidental parameters free inference approach for panels with common shocks

Journal of Econometrics Doi: 10.1016/j.jeconom.2021.03.011

Juodis, Artūras; Karavias, Yiannis & Sarafidis, Vasilis (2021)

A homogeneous approach to testing for Granger non-causality in heterogeneous panels

Empirical Economics, 60, s. 93- 112. Doi: 10.1007/s00181-020-01970-9

This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross sections guarantees that the estimator has a NT−−−√ convergence rate. In order to account for the well-known “Nickell bias”, the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks’ profitability and cost efficiency.

Juodis, Artūras & Sarafidis, Vasilis (2021)

A Linear Estimator for Factor-Augmented Fixed-T Panels With Endogenous Regressors

Journal of business & economic statistics, 00, s. 1- 15. Doi: 10.1080/07350015.2020.1766469

A novel method-of-moments approach is proposed for the estimation of factor-augmented panel data models with endogenous regressors when T is fixed. The underlying methodology involves approximating the unobserved common factors using observed factor proxies. The resulting moment conditions are linear in the parameters. The proposed approach addresses several issues which arise with existing nonlinear estimators that are available in fixed T panels, such as local minima-related problems, a sensitivity to particular normalization schemes, and a potential lack of global identification. We apply our approach to a large panel of households and estimate the price elasticity of urban water demand. A simulation study confirms that our approach performs well in finite samples.

Norkutė, Milda; Sarafidis, Vasilis, Yamagata, Takashi & Cui, Guowei (2021)

Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure

Journal of Econometrics Doi: 10.1016/j.jeconom.2020.04.008

Li, Qi; Sarafidis, Vasilis & Westerlund, Joakim (1)

Essays in honor of Professor Badi H Baltagi

Empirical Economics [Kronikk]

Akademisk grad
År Akademisk institusjon Grad
2006 University of Cambridge PhD
2001 Cambridge University Master M.Phil.
2000 University of Sussex B.A. in Economics
År Arbeidsgiver Tittel
2021 - Present BI Norwegian Business School Professor
2017 - 2020 University of Monash Associate Professor
2012 - 2016 University of Monash Senior lecturer
2011 - 2011 University of Sydney Senior lecturer
2006 - 2011 University of Sydney Lecturer
2001 - 2002 Europe Economics Ltd, London. Consultant