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Claudia Foroni

Research

Division

Financial Research

Current Position

Senior Economist

Fields of interest

Macroeconomics and Monetary Economics,Mathematical and Quantitative Methods

Email

Claudia.Foroni@ecb.europa.eu

Education
2008-2012

PhD Economics, European University Institute, Florence, Italy

2005-2007

MA Economic and Social Sciences, Bocconi University, Milan, Italy

2002-2005

BA Economic and Social Sciences, Bocconi University, Milan, Italy

Professional experience
2020-

Senior Economist - Supply Side, Labour and Surveillance Division, Directorate General Economics, European Central Bank

2018-2019

Economist - Supply Side, Labour and Surveillance Division, Directorate General Economics, European Central Bank

2017-2018

Researcher - Reseach Centre, Deutsche Bundesbank

2016-2017

Economist (ESCB/IO) - International Policy Analysis, Directorate General International and European Relations, European Central Bank

2012-2016

Researcher - Research Department, Norges Bank

Teaching experience
2014-2015

Econometrics - University of Oslo, Oslo, Norway

6 January 2021
ECONOMIC BULLETIN - ARTICLE
Economic Bulletin Issue 8, 2020
Details
Abstract
This article analyses labour market developments in the euro area since the onset of the coronavirus (COVID-19) pandemic. Total hours worked declined sharply in the first half of 2020. However, employment and unemployment reacted only weakly to the marked fall in GDP, as many workers remained employed under job retention schemes. These contributed to a fall in compensation per employee and an increase in compensation per hour worked. Participation in the labour force also dropped substantially, more than offsetting the increase observed since mid-2013. An analysis of the decomposition of labour market shocks via a sign-restricted structural vector-autoregressive model shows that both supply and demand shocks contributed to the decline in total hours worked. High-frequency indicators on hiring rates and job postings have declined sharply since April and continue to indicate a depressed level of labour demand. However, employment and hours worked recovered somewhat in the third quarter. Nonetheless, the COVID-19 pandemic is having a heterogeneous impact on employment across euro area countries and there is the risk of a further increase in geographic divergence in euro area labour markets. Temporary employees, the young and workers with low levels of education were the most affected, while teleworking may have played a role in supporting employment and hours worked for some workers in certain sectors. Activity sectors such as trade and transport and recreation activities have been disproportionately affected, with the largest decreases in hours worked. However, it is too early to assess the extent to which the pandemic will affect the need for labour reallocation across sectors, tasks and occupations.
JEL Code
E24 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Employment, Unemployment, Wages, Intergenerational Income Distribution, Aggregate Human Capital
E65 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Studies of Particular Policy Episodes
18 September 2020
WORKING PAPER SERIES - No. 2468
Details
Abstract
We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track by a specific form of intercept correction. Among all these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and following recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment and very persistent negative effects on trend growth. Similar findings emerge also for the other G7 countries.
JEL Code
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
18 June 2020
ECONOMIC BULLETIN - BOX
Economic Bulletin Issue 4, 2020
Details
Abstract
This box examines regional developments in labour input within the euro area since the peak in economic activity before the global financial crisis (GFC). It reveals that the increase in total hours worked during the recovery that followed the GFC was greater than the decline during the recession only for regions at the top of the GDP per capita distribution. Overall, the evolution of total hours worked in the euro area between 2007 and 2018 was quite heterogeneous across regions, with hours worked being more insulated from the fall in GDP in richer regions during the recession period and poorer regions not converging with their richer counterparts during the recovery that followed. The smaller decline in total hours worked in the richer regions during the downturn and the similar growth rates observed during the recovery are the main sources of the regional heterogeneity in the time pattern of total hours worked, and can be attributed to changes in the employment rate, to the decline in average hours worked during the recession period, and to the stability of regional differences in population growth during both periods, with the latter factor being consistent with labour migrating from poorer to richer regions.
JEL Code
E24 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Employment, Unemployment, Wages, Intergenerational Income Distribution, Aggregate Human Capital
17 June 2020
ECONOMIC BULLETIN - BOX
Economic Bulletin Issue 4, 2020
Details
Abstract
This box reviews recent developments in short-time work and temporary lay-off schemes in the five largest euro area countries. It then calculates wage replacement rates and estimates take-up rates. Combining wage replacement rates with the estimated number of participants makes it possible to calculate the impact of short-time work on household disposable income. The box concludes that short-time work and temporary lay-off measures are significantly buffering the impact of COVID-19 on households’ disposable income.
JEL Code
E24 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Employment, Unemployment, Wages, Intergenerational Income Distribution, Aggregate Human Capital
E65 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Studies of Particular Policy Episodes
16 August 2019
WORKING PAPER SERIES - No. 2309
Details
Abstract
We focus on the implications of the shale oil boom for the global supply of oil. We begin with a stylized model with two producers, one facing low production costs and one higher production costs but potentially lower adjustment costs, competing à la Stackelberg. We find that the supply function is flatter for the high cost producer, and that the supply function for shale oil producers becomes more responsive to demand shocks when adjustment costs decline. On the empirical side, we apply an instrumental variable approach using estimates of demand-driven oil price changes derived from a standard structural VAR of the oil market. A main finding is that global oil supply is rather vertical, practically all the time. Moreover, for the global oil market as a whole, we do not find evidence of a major shift to a more price elastic supply as a result of the shale oil boom.
JEL Code
Q33 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Nonrenewable Resources and Conservation→Resource Booms
Q41 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Demand and Supply, Prices
Q43 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy and the Macroeconomy
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
20 March 2019
WORKING PAPER SERIES - No. 2250
Details
Abstract
We analyse the importance of macroeconomic information, such as industrial production index and oil price, for forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). We study the forecasting accuracy for different horizons (from 1 day ahead to 28 days ahead) and by considering different specifications of the models. We find gains around 20% at short horizons and around 10% at long horizons. Therefore, it turns out that the macroeconomic low frequency variables are more important for short horizons than for longer horizons. The benchmark is almost never included in the model confidence set.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
Q43 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy and the Macroeconomy
Q47 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy Forecasting
22 November 2018
WORKING PAPER SERIES - No. 2206
Details
Abstract
Temporal aggregation in general introduces a moving average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed frequency model. The MA component is generally neglected, likely to preserve the possibility of OLS estimation, but the consequences have never been properly studied in the mixed frequency context. In this paper, we show, analytically, in Monte Carlo simulations and in a forecasting application on U.S. macroeconomic variables, the relevance of considering the MA component in mixed-frequency MIDAS and Unrestricted-MIDAS models (MIDAS-ARMA and UMIDAS-ARMA). Specifically, the simulation results indicate that the short-term forecasting performance of MIDAS-ARMA and UMIDAS-ARMA is better than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting U.S. GDP growth, investment growth and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS-ARMA is better than UMIDAS-ARMA.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
2020
International Journal of Forecasting
Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis
  • Foroni, C., Marcellino, M. and Stevanovic, S.
2019
Journal of Applied Econometrics
MIDAS models and MA components
  • Foroni, C., Marcellino, M. and Stevanovic, S.
2018
Journal of International Money and Finance
Assessing the predictive ability of sovereign default risk on exchange rates
  • Foroni, C., Ravazzolo, F. and Sadaba, B.
2018
International Economic Review
Labor Supply Factors and Economic Fluctuations
  • Foroni, C., Furlanetto, F. and Lepetit, A.
2018
Annals of Applied Statistics
Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov switching model
  • Casarin, R., Foroni, C., Marcellino, M. and Ravazzolo, F.
2018
International Journal of Forecasting
Using low frequency information for predicting high frequency variables
  • Foroni, C., Guerin, P. and Marcellino, M.
2017
International Journal of Computational Economics and Econometrics
A daily indicator of economic conditions
  • Aprigliano, V., Mazzi, G., Foroni, C., Marcellino, M. and Venditti, F.
2017
Journal of Applied Econometrics,
Density forecasts with MIDAS models
  • Aastveit, K.A., Foroni, C. and Ravazzolo, F.
2017
Economics Letters
Time-varying Effects Of Oil Price Shocks On U.S. Stock Returns
  • Foroni, C., Guerin, P. and Marcellino, M.
2016
Journal of the Royal Statistical Society – Series A
Mixed frequency Structural VARs
  • Foroni, C. and Marcellino, M.
2015
Journal of the Royal Statistical Society – Series A
U-MIDAS: MIDAS regressions with unrestricted lag polynomials
  • Foroni, C., Marcellino, M. and Schumacher, C.
2015
International Journal of Forecasting
Markov-switching Mixed Frequency Vector Autoregression Models
  • Foroni, C., Guerin, P. and Marcellino, M.
2014
International Journal of Forecasting
A Comparison of Mixed Frequency Approaches for Nowcasting Euro Area Macroeconomic aggregates
  • Foroni, C. and Marcellino, M.
2014
Journal of Applied Econometrics
Mixed-Frequency Structural Models: Identification, Estimation, and Policy Analysis
  • Foroni, C. and Marcellino, M.
2013
Advances in Econometrics
Mixed-frequency vector autoregressive models
  • Foroni, C., Ghysels, E. and Marcellino, M.