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Andrej Sokol

Economics

Division

Prices & Costs

Current Position

Economist

Fields of interest

Macroeconomics and Monetary Economics,International Economics,Mathematical and Quantitative Methods

Email

Andrej.Sokol@ecb.europa.eu

Education
2010-2011

MSc Economics, Queen Mary University of London, United Kingdom

2006-2008

MSc Management, Economics and Industrial Engineering, Politecnico di Milano, Italy

2003-2006

BSc Management and Production Engineering, Politecnico di Milano, Italy

Professional experience
2018-

Economist - Prices and Costs Division, Directorate General Economics, European Central Bank

2017-2018

Senior Research Economist, International Directorate, Bank of England

2015-2017

Research Economist, International Directorate, Bank of England

2011-2015

Economist, Monetary Analysis, Bank of England

2009

Research and Teaching Assistant, Dipartimento di Ingegneria Gestionale, Politecnico di Milano, Italy

Awards
2011

Principal's prize for outstanding academic achievement, Queen Mary University of London

2010

"Amici della Fondazione Luigi Einaudi" scholarship, Fondazione Luigi Einaudi

Teaching experience
2009

Teaching Assistant - Economics (BSc), International Economics (MSc), Economics (MBA), Dipartimento di Ingegneria Gestionale, Politecnico di Milano, Italy

17 June 2019
ECONOMIC BULLETIN - ARTICLE
Economic Bulletin Issue 4, 2019
Details
Abstract
In this article we review the evolution of euro area HICP inflation excluding energy and food since the Great Financial Crisis through the lens of the Phillips curve. This period is particularly interesting, as the euro area experienced two recessions (in 2008-2009 and 2011-2014) and a protracted episode of low inflation from 2013 onwards. We estimate a large set of Phillips curve models for the euro area and review the interpretation of inflation developments that they provide over time. We highlight both the advantages and some of the limitations of this type of analysis. We find that our models can account for much of the weakness in underlying inflation between 2013 and mid-2017, but that they cannot account for the most recent weakness in underlying inflation.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
6 April 2020
WORKING PAPER SERIES - No. 2387
Details
Abstract
We document how the distribution of exchange rate returns responds to changes in global financial conditions. We measure global financial conditions as the common component of country-specific financial condition indices, computed consistently across a large panel of developed and emerging economies. Based on quantile regression results, we provide a characterisation and ranking of the tail behaviour of a large sample of currencies in response to a tightening of global financial conditions, corroborating (and quantifying) some of the prevailing narratives about safe haven and risky currencies. Our approach delivers a more nuanced picture than one based on standard OLS regression. We then carry out a portfolio sorting exercise to identify the macroeconomic fundamentals associated with such different tail behaviour, and find that currency portfolios sorted on the basis of net foreign asset positions, relative interest rates, current account balances and levels of international reserves display a higher likelihood of large losses in response to a tightening of global financial conditions.
JEL Code
F31 : International Economics→International Finance→Foreign Exchange
G15 : Financial Economics→General Financial Markets→International Financial Markets
12 August 2020
WORKING PAPER SERIES - No. 2453
Details
Abstract
Monitoring economic conditions in real time, or nowcasting, is among the key tasks routinely performed by economists. Nowcasting entails some key challenges, which also characterise modern Big Data analytics, often referred to as the three \Vs": the large number of time series continuously released (Volume), the complexity of the data covering various sectors of the economy, published in an asynchronous way and with different frequencies and precision (Variety), and the need to incorporate new information within minutes of their release (Velocity). In this paper, we explore alternative routes to bring Bayesian Vector Autoregressive (BVAR) models up to these challenges. We find that BVARs are able to effectively handle the three Vs and produce, in real time, accurate probabilistic predictions of US economic activity and, in addition, a meaningful narrative by means of scenario analysis.
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C01 : Mathematical and Quantitative Methods→General→Econometrics
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
14 December 2020
WORKING PAPER SERIES - No. 2501
Details
Abstract
We compare direct forecasts of HICP and HICP excluding energy and food in the euro area and five member countries to aggregated forecasts of their main components from large Bayesian VARs with a shared set of predictors. We focus on conditional point and density forecasts, in line with forecasting practices at many policy institutions. Our main findings are that point forecasts perform similarly using both approaches, whereas directly forecasting aggregate indices tends to yield better density forecasts. In the aftermath of the Great Financial Crisis, relative forecasting performance was typically only affected temporarily. Inflation forecasts made by Eurosystem/ECB staff perform similarly or slightly better than those from our models for the euro area.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
23 April 2021
WORKING PAPER SERIES - No. 2538
Details
Abstract
We characterise the probability distributions of various categories of gross capital flows conditional on information contained in financial asset prices in a panel of emerging market economies, with a focus on ‘tail’ events. Our framework, based on the quantile regression methodology, allows for a separate role of push- and pull-type factors, and because it is based on high-frequency data, can quantify the likelihood of different outturns before official capital flows data are released. We find that both push and pull factors have heterogeneous effects across the distributions of gross capital flows, which are most marked in the left tails. We also explore the role of various policies, and find that macroprudential and capital flows management measures are stabilising, leading to lower chances of either large portfolio inflows or out flows.
JEL Code
F32 : International Economics→International Finance→Current Account Adjustment, Short-Term Capital Movements
F34 : International Economics→International Finance→International Lending and Debt Problems
G15 : Financial Economics→General Financial Markets→International Financial Markets
2019
International Journal of Forecasting
  • Domit, S., Monti, F. and Sokol, A.
2011
Annali della Fondazione Luigi Einaudi
Estimating central bank preferences: The case of the Bank of England over 1981-2007
  • Sokol, A.
2021
Bank Underground
  • Kumhof, M., Rungcharoenkitkul, P., and Sokol, A.
2020
Bank Underground
  • Eguren-Martin, F., O'Neill, C., Sokol, A. and Von dem Berge, L.
2020
Bank for International Settlements Working Paper 890
  • Kumhof, M., Rungcharoenkitkul, P. and Sokol, A.
2020
VoxEU
  • Ehrmann, M., Jarocinski, M., Nickel, C., Osbat, C. and Sokol, A.
2019
Bank Underground
  • Eguren-Martin, F. and Sokol, A.
2019
Bank of England Staff Working Paper 817
  • Cesa-Bianchi, A., Kumhof, M., Sokol, A. and Thwaites, G.
2019
Bank Underground
  • Eguren-Martin, F. and Sokol, A.
2018
Bank of England Quarterly Bulletin, 2018Q3
  • Kindberg-Hanlon, G. and Sokol, A.
2017
Bank Underground
  • Cesa-Bianchi, A., Redl, C., Sokol, A. and Thwaites, G.
2017
Bank of England Staff Working Paper 693
  • Cesa-Bianchi, A. and Sokol, A.
2014
Centre for Macroeconomics Discussion Paper 1410
  • Haberis, A. and Sokol, A.