Claudia Foroni
Economics
- Division
-
Supply Side, Labour and Surveillance
- Current Position
-
Senior Economist
- Fields of interest
-
Macroeconomics and Monetary Economics,Mathematical and Quantitative Methods
- Other current responsibilities
- 2021-
Associate Editor, Journal of Business and Economic Statistics
- 2019-
CEPR Research Affiliate, Monetary Economics and Fluctuations programme
- 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
- 5 August 2022
- WORKING PAPER SERIES - No. 2699Details
- Abstract
- Despite its stability over time, as for any statistical relationship, Okun’s law is subject to deviations that can be large at times. In this paper, we provide a mapping between residuals in Okun’s regressions and structural shocks identified with a SVAR model by inspecting how unemployment responds to the state of the economy. We show that deviations from Okun’s law are a natural and expected outcome once one takes a multi-shock perspective, as long as shocks to automation, labour supply and structural factors in the labour market are taken into account. Our simple recipe for policy makers is that, if a positive deviation from Okun’s law arises, it is likely to be generated by either positive labour supply or automation shocks or by negative structural factors shocks.
- 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
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
- 11 February 2022
- WORKING PAPER SERIES - No. 2637Details
- Abstract
- We use mixed-frequency (quarterly-monthly) data to estimate a dynamic stochastic general equilibrium model embedded with the financial accelerator mechanism à la Bernanke et al. (1999). We find that the financial accelerator can work very differently at monthly frequency compared to the quarterly frequency, i.e. we document its inversion. That is because aggregating monthly data into quarterly leads to large biases in the estimated quarterly parameters and, as a consequence, to a deep change in the transmission of shocks.
- JEL Code
- C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 8 October 2021
- WORKING PAPER SERIES - No. 2601Details
- Abstract
- We introduce a Bayesian Mixed-Frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results in terms of forecasting, especially when looking at quarterly variables, such as employment growth and the job finding rate. Furthermore, we look into the shocks that drove the labour market and macroeconomic dynamics from 2002 to early 2020, with a first insight also on the COVID-19 recession. While domestic and foreign demand shocks were the main drivers during the Global Financial Crisis, aggregate supply conditions and labour supply factors reflecting the degree of lockdown-related restrictions have been important drivers of key labour market variables during the pandemic.
- JEL Code
- J6 : Labor and Demographic Economics→Mobility, Unemployment, Vacancies, and Immigrant Workers
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
- 21 September 2021
- OCCASIONAL PAPER SERIES - No. 266Details
- Abstract
- The digitalisation workstream report analyses the degree of digital adoption across the euro area and EU countries and the implications of digitalisation for measurement, productivity, labour markets and inflation, as well as more recent developments during the coronavirus (COVID-19) pandemic and their implications. Analysis of these key issues and variables is aimed at improving our understanding of the implications of digitalisation for monetary policy and its transmission. The degree of digital adoption differs across the euro area/EU, implying heterogeneous impacts, with most EU economies currently lagging behind the United States and Japan. Rising digitalisation has rendered price measurement more challenging, owing to, among other things, faster changes in products and product quality, but also new ways of price setting, e.g. dynamic or customised pricing, and services that were previously payable but are now “free”. Despite the spread of digital technologies, aggregate productivity growth has decreased in most advanced economies since the 1970s. However, it is likely that without the spread of digital technologies the productivity slowdown would have been even more pronounced, and the recent acceleration in digitalisation is likely to boost future productivity gains from digitalisation. Digitalisation has spurred greater automation, with temporary labour market disruptions, albeit unevenly across sectors. The long-run employment effects of digitalisation can be benign, but its effects on wages and labour share depend on the structure of the economy and its labour market institutions. The pandemic has accelerated the use of teleworking: roughly every third job in the euro area/EU is teleworkable, although there are differences across countries. ...
- 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
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
O57 : Economic Development, Technological Change, and Growth→Economywide Country Studies→Comparative Studies of Countries
- 6 January 2021
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 8, 2020Details
- 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. 2468Details
- 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 - BOXEconomic Bulletin Issue 4, 2020Details
- 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 - BOXEconomic Bulletin Issue 4, 2020Details
- 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. 2309Details
- 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. 2250Details
- 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. 2206Details
- 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
- 2022
- Journal of Applied EconometricsThe shale oil revolution and the global supply curve
- 2022
- International Journal of ForecastingForecasting the Covid-19 recession and recovery: Lessons from the financial crisis
- 2019
- Journal of Applied EconometricsMIDAS models and MA components
- 2018
- Journal of International Money and FinanceAssessing the predictive ability of sovereign default risk on exchange rates
- 2018
- International Economic ReviewLabor Supply Factors and Economic Fluctuations
- 2018
- Annals of Applied StatisticsUncertainty through the lenses of a mixed-frequency Bayesian panel Markov switching model
- 2018
- International Journal of ForecastingUsing low frequency information for predicting high frequency variables
- 2017
- International Journal of Computational Economics and EconometricsA daily indicator of economic conditions
- 2017
- Journal of Applied Econometrics,Density forecasts with MIDAS models
- 2017
- Economics LettersTime-varying Effects Of Oil Price Shocks On U.S. Stock Returns
- 2016
- Journal of the Royal Statistical Society – Series AMixed frequency Structural VARs
- 2015
- Journal of the Royal Statistical Society – Series AU-MIDAS: MIDAS regressions with unrestricted lag polynomials
- 2015
- International Journal of ForecastingMarkov-switching Mixed Frequency Vector Autoregression Models
- 2014
- International Journal of ForecastingA Comparison of Mixed Frequency Approaches for Nowcasting Euro Area Macroeconomic aggregates
- 2014
- Journal of Applied EconometricsMixed-Frequency Structural Models: Identification, Estimation, and Policy Analysis
- 2013
- Advances in EconometricsMixed-frequency vector autoregressive models