Curriculum vitæ

Professional experience

:::: {.columns} ::: {.column width=“10%”} 2024- ::: ::: {.column width=“90%”} Head of the Summary, Financial Transactions and Public Debt Tables sector, DGFIP, Paris. :::

2021-2024

Data economist, Insee, Paris.

Summer 2020

Intern at the division Research and Development of the NBB (National Bank of Belgium), Bruxelles, Belgique.

2017 - 2019

Methodologist on Seasonal and Working Day Adjustments (SA-WDA), Insee, Paris.

2019 -

Reviewer for the journals, Statéco Journal of Official Statistics, Statistical Journal of the IAOS and International Journal of Operational Research.

2015 - 2017

Short-term economic analysis officer in the manufacturing industry, INSEE, Paris.

Summer 2013

Intern research officer at the International Relations Department at the INE (Instituto Nacional de Estadística), Madrid, Spain.

Education

2020 -

PhD Student at EDGE, Nantes Atlantique Economics and Management Laboratory (LEMNA).

  • Real-time detection of turning points: contribution of the use of asymmetric filters in business cycle analysis.
  • Under the supervision of Olivier Darné (LEMNA) and Dominique Ladiray (Insee).

2019 - 2021

National School of Statistics and Economic Administration (ENSAE).

2014 - 2015

Master of Science in statistics-econometrics, specialisation in official statistics and statistical studies, ENSAI / Université Rennes 1.

2013 - 2014

Bachelor of Economics in Science of organizations and markets, applied economics, Université Paris-Dauphine.

2012 - 2013

Bachelor of Science in Mathematics equivalent, Université Rennes 1.

2012 - 2014

National School for Statistics and Information Analysis, France (ENSAI).

2010 - 2012

Post-secondary preparatory school in Mathematics, Physics and Computer Science (France’s Grandes écoles), Lycée Carnot, Dijon.

Publications / Interventions

Publications

  • Quartier-la-Tente A. (2024), “Using regression models with time-varying coefficients for short-term economic forecasting”, INSEE working paper (French article).

    This study describes three methods for estimating linear regression models with time-varying coefficients: piecewise regression, local regression, and regression with stochastic coefficients (state-space modeling). It also details their implementation in R using the tvCoef package. Through a comparative analysis of around thirty quarterly forecasting models, we show that the use of these methods, especially thanks to the state-space modeling, reduces forecast errors when breakpoints are present in the coefficients. Moreover, even when traditional tests conclude that the coefficients are stable, regression with stochastic coefficients can still help reduce forecast errors. However, uncertainties related to estimating certain hyperparameters can increase real-time forecast errors, especially for local regression. Thus, an economic analysis of estimated parameters remains essential.

    This study is fully reproducible and all the codes used are available under https://github.com/InseeFrLab/DT-tvcoef.

  • Abbas R., Carnot N., Lequien M., Quartier-la-Tente A., Roux S. (2024), “On the way to net zero. But which way?”, INSEE working paper.

    With an optimal investment – or stranding – choice model in carbon-intensive (brown) capital, which emits greenhouse gases, or in emissions-free (green) capital, we describe the optimal transitions to carbon neutrality that comply with climate constraints such as emission caps for a given date (Fit for 55) or carbon budgets. We show that:

    1. Anticipated stranding cannot occur with targets at specific dates.

    2. To limit warming to a given level, explicitly introducing this constraint in the form of a remaining carbon budget minimizes the associated economic cost, leading to high initial stranding with limited budgets. Emission caps set regularly from the first year, and chosen based on emissions from this optimal trajectory, result in a similar path.

    3. Given a cumulative emission level, delaying the transition increases costs and stranding.

    4. Total investment during and after the transition is lower than that in the initial state.

    All the codes used are available at https://github.com/InseeFrLab/DT-way-to-net-zero.

  • Quartier-la-Tente A. (2024), “Real-time trend-cycle estimation: the contribution of asymmetric moving averages”, INSEE methodological working paper (French article).

    This paper focuses on different approaches to build moving averages for real-time trend-cycle estimation and fast turning point detection. We propose a comparison of the main methods, based on a general unifying framework to derive linear filters. We also describe two possible extensions to local polynomial filters: the addition of a timeliness criterion to control the phase shift (delay in the detection of turning points) and a way to locally parameterize these filters. The empirical comparison of the methods shows that: the optimization problems of the filters from the Reproducing Kernel Hilbert Space (RKHS) theory increase the phase shift and the revisions of the trend-cycle estimates; modeling polynomial trends that are too complex introduces more revisions without decreasing the phase shift; for polynomial filters, a local parameterization reduces the phase shift and the revisions.

    This study is fully reproducible and all the codes used are available under https://github.com/InseeFrLab/DT-est-tr-tc.

  • Abbas R., Carnot N., Lequien M., Quartier-la-Tente A., Roux S. (2023),, contribution to the Mahfouz - Pisani-Ferry report on the macro-economic impact of the low-carbon transition, [summary] report(https://www.strategie.gouv.fr/sites/strategie.gouv.fr/files/atoms/files/2023-incidences-economiques-rapport-pisani-5juin.pdf){target=“_blank”} p79-81 and thematic report on the labour market p47-57.

  • Babet D., Lequien M., Quartier-la-Tente A. (2022), “The contribution of macroeconomic models to simulate the effects of higher energy import prices”, Conjoncture in France, March, p. 16-17.

  • Quartier-la-Tente A. (2022), “Real-time detection of turning points: the contribution of asymmetric filters to turning-point detection”, 13e Journées de Méthodologie Statistique (French article).

  • Ladiray D., Quartier-la-Tente A. (2018), “The good use of Reg-ARIMA models in seasonal adjustment”, 13e Journées de Méthodologie Statistique (French article).

  • Pham H., Quartier-la-Tente A. (2018), “Seasonal adjustment of very long time series by sub- period, advantages and choice of the estimate length”, 13e Journées de Méthodologie Statistique (French article).

  • Dortet-Bernadet V., Lenseigne F., Parent C., Quartier-la-Tente A., Stoliaroff-Pépin A-M., Plouhinec C. (2016), “After two years of turbulence, the French aeronautical sector is ready to take off again”, Conjoncture in France, December, p. 19-37.

  • Glotain M., Quartier-la-Tente A. (2015), “New sub-sector business climate indicators to improve economic outlook analysis”, Conjoncture in France, June, p. 35-54.

  • Quartier-la-Tente A. (2015), “To what extent does the integration of sub-sectoral information improve the quality of manufacturing production forecasting?”, Master’s thesis.

Interventions

Trainings

2022-

Trainer on time series analysis with R (12h), CEPE, Paris.

2021-2022

Lecturer in time series analysis, ENSAE, Paris.

2021

Seasonal with JDemetra+ et RJDemetra (24h), RTE, Paris.

2019-

Trainer on seasonal and working days adjustment (SA-WDA) (24h), CEPE, Paris.

2017 - 2019

Trainer on seasonal and working days adjustment (SA-WDA),INSEE, Paris.

2016

Lecturer in introduction to the statistical software , ENSAE, Paris.

Coopérations

Mauritius (2023)

Technical assistance on the production monthly indicators of economic growth (MIEG).

Cameroun (2023)

Technical assistance on the production of seasonally adjusted and working day adjusted of Quarterly National Accounts (QNA).

Togo (2023)

Technical assistance on the production of seasonally adjusted and working day adjusted of Quarterly National Accounts (QNA) and Monthly Indicators of Economic Growth (MIEG).

Mauritania (2022)

Technical assistance on the construction of index of industrial production (IIP) and on seasonal adjustment.

North Macedonia (2022)

Technical assistance on liquidity forecasting to the National Bank of North Macedonia.

Honduras (2022)

Technical assistance on liquidity forecasting to the Central Bank of Honduras.

Senegal (2020)

Trainer on manipulation and forecast of time series with (5 days), BCEAO.

Paris (2018)

Technical assistance on seasonal adjustment methods to a Serbian Delegation.

Europe (2017-)

Member of the Eurostat seasonal adjustment center of Excellence: reflections on seasonal adjustment methods; development of R packages around JDemetra+, maintenance and distribution. Technical assistance to several European countries.

Senegal (2017)

Trainer on forecasting models and construction of synthetic indicators, BCEAO, Dakar, and INSEE, Paris.

Paris (2016)

Technical assistance on the use of business surveys in forecasting National Accounts to a Serbian Delegation.