# Research

Guillaume Chevillon

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## Work in progress

**We modeled long memory with just one lag!**(pdf) with Luc Bauwens and Sébastien Laurent, R&R to the Journal of Econometrics.**What Does it Take to Control Global Temperatures? Prospective and Counterfactual Carbon Abatement Policies in a Cointegrated Vector Autoregressive Model,**with Takamitsu Kurita (Kyoto Sangyo University), working paper and dataset to appear soon here**The Bias-Variance Trade-off in (Un)Conditional Multistep Forecasting, Predictive Regressions and Local Projections**, working paper to appear soon here

## Publications

**Probabilistic Forecasting of Bubbles and Flash Crashes****,**with Anurag Banerjee and Marie Kratz,**The Econometrics Journal**, 23(2), pp. 297–315, 2020, (doi, pdf, supplement)

We propose a near explosive random coefficient autoregressive model (NERC) to obtain predictive probabilities of the apparition and devolution of bubbles. We study the asymptotic properties of the NERC and provide a procedure for inference on the parameters.**Robust inference in structural VARs with long-run restrictions,**with Sophocles Mavroeidis and Zhaoguo Zhan,**Econometric Theory**, 36(1), pp. 86-121, 2020 (open source, wp pdf, supplement, code & data).

Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identification and strong persistence.**Perpetual Learning and Apparent Long Memory,**with Sophocles Mavroeidis (doi, pdf, supplement)**Journal of Economic Dynamics and Control**90, pp. 343-65, 2018.

This paper studies the low frequency dynamics in forward looking models where expectations are formed using perpetual learning such as constant gain least squares. We show that if the coefficient on expectations is sufficiently close to unity, perpetual learning induces strong persistence that is empirically indistinguishable from long memory. We apply this result to present value models of stock prices and exchange rates and find that perpetual learning can explain the long memory observed in the data.**Generating Univariate Fractional Integration within a Large VAR(1)****Journal of Econometrics**, 204(1), pp. 54-65, 2018. (previously circulated as*Long Memory through Marginalization of Large Systems and Hidden Cross-Section Dependence)*This paper shows that a large dimensional vector autoregressive model (VAR) of finite order can generate fractional integration in the marginalized univariate series. We derive high-level assumptions under which the final equation representation of a VAR(1) leads to univariate fractional white noises and verify the validity of these assumptions for two specific models.**Learning can generate Long Memory**with Sophocles Mavroeidis (doi, pdf, supplement, code and data)**Journal of Econometrics**, 198(1), pp. 1-9, 2017.

We study learning dynamics in a prototypical representative-agent forward-looking model in which agents’ beliefs are updated using linear learning algorithms. We show that learning in this model can generate long memory endogenously, without any persistence in the exogenous shocks, depending on the weights agents place on past observations when they update their beliefs, and on the magnitude of the feedback from expectations to the endogenous variable. This is distinctly different from the case of rational expectations, where the memory of the endogenous variable is determined exogenously.**Robust Cointegration Testing in the Presence of Weak Trends, with an Application to the Human Origin of Global Warming,**(pdf, doi)**Ec****onometric Reviews**, 36(5), pp. 514-45, 2017.

This papers shows how valid inference on cointegration can obtain in the presence of unnoticed (nonlinear) trends. An application is provided to assessing the long run causal nature of radiative forcing of human origin for global temperatures.**Multistep Forecasting in the Presence of Location Shifts**(pdf, supplement, doi)**International Journal of Forecasting**, 32(1), pp. 121-37, 2016.

In this paper, I consider how to improve forecasting techniques when the underlying data generating process is subject to stochastic shifts. I show how the improvement relates to the forecasting horizon.**Multi-step forecast error corrections: A comment on “Evaluating Predictive Densities of U.S. Output Growth and Inflation in a Large Macroeconomic Data Set”**,**International Journal of Forecasting.**30(3), pp. 683-7, 2014.(doi, pdf, code & data)

Here I comment on a paper by B. Rossi and T. Sekhposyan. I assess in particular the conditions under which their methods for evaluating predictive densities can be generalized to multi-step ahead forecasts.**Inference in Models with Adaptive Learning,**(pdf, doi) with Sophocles Mavroeidis and Michael Massmann**.****Journal of Monetary Economics**, 57(3), pp. 341-51, 2010,

This article considers the issue of estimation and inference in models where agents' forward-looking expectations are formed via adaptive learning. We show that nonstandard dynamics result and invalidate standard techniques of inference. We provide valid methods and assess them by means of a three-equation new Keynesian macro model.**Physical Market Determinants of the Price of Crude Oil and the Market Premium**with Christine Rifflart. (pdf, data, doi)**Energy Economics**, 31(4), pp. 527-49, 2009.

This papers evaluates the risk premium associated with the price of crude oil. The risk premium is defined as the difference between actual pricing and pricing based on historical demand and supply conditions**Multi-step Forecasting in Emerging Economies: an Investigation of the South African GDP.**(pdf, doi, supplement**)****International Journal of Forecasting**, 25(3), pp 602–28, 2009.

This article considers techniques for forecasting in a multivariate framework when the forecast horizon of interest varies and the economy is subject to breaks or change. This is mostly an empirical paper that assess how best to forecast the GDP of South Africa.

**Direct Multi-Step Estimation and Forecasting,**(earlier version in pdf, doi)**Journal of Economic Surveys**, 21(4), pp. 746-85, 2007.

This paper reviews existing results and provides a framework for understanding the interplay between the forecast horizon and the forecasting techniques.

**Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes****International Journal of Forecasting**, 21(2), pp 201-18, 2005.

We consider the optimal methods for forecasting as a function of the horizon at which it is wished to obtain a forecast.

## Discussions

Incentive-Driven Inattention, by W.P. Gaglianone, R. Giacomini, J.V. Issler & V. Skreta,

Bank of Finland Workshop on Empirical Macroeconomics, March 8, 2019.Short-Term Macroeconomic Forecasting and Turning-Point Detection after the Great Recession, by C. Doz, L. Ferrara & P.-A. Pionnier,

10th French Econometrics Conference, November 29, 2018Mixed Non-Causal AR Processes and the Modelling of Explosive Bubbles, by Sébastien Fries and Jean-Michel Zakoïan

9th French Econometrics Conference, November 30, 2017Why are inflation forecasts sticky? by F. Bec, R. Boucekkine & C. Jardet

Banque de France, September 29, 2017Information-driven business cycles, by R. Chahrour and R. Ulbricht

Barcelona GSE Summer Forum, June 8, 2016Econométrie des Marchés des Matières Premières, by Joëts, Mignon & Razafindrabe

3e JEAM – Labex MME-DII 16 Septembre, 2015Disentangling Economic Recessions and Depressions, by B. Candelon, N. Metiu & S. Straetmans,

Journée d’Econométrie Economix, Université Paris-Ouest Nanterre, December 11, 2013

## Research Articles in French

**Stratégies de Vote en AG Face aux Résolutions Externes***(*pdf*)*with P. Charléty, and M. Messaoudi,**Revue Française de Gestion**, 198-199 (2009), pp 277‑96**L’impact du taux de change sur le tourisme en France**with Xavier Timbeau (OFCE), (pdf)

Revue de l’OFCE 98 (2006), pp 167-182.**Analyse économétrique et compréhension des erreurs de prévision**(pdf)

Revue de l’OFCE, 95 (2005), pp 327-56.

## Empirical Macro Forecasts (at OFCE, 2003-6)

**Brouillard autour des puits de pétrole,**2004 (The determinants of oil prices) with Christine Rifflart (OFCE) Lettre de l’OFCE, No 253.**Les tribulations de la parité euro/dollar,**2004 (Recent developments in the euro/dollar exchange rate) Lettre de l’OFCE No 252.**Perspectives 2003-2004, 2003-2004, 2004-2005, 2005-2006.**World Economic Outlooks (in charge of French Foreign Trade, Central & Eastern European countries (from 2006)) April 2003, October 2003, April 2004, October 2004, April 2005, October 2005, April 2006;

Revue de l’OFCE No 85, 87, 89, 91, 93, 95 & 97.**Pétrole : marée noire sur la Croissance,**OFCE World Economic Outlook October 2004, Revue de l’OFCE No 91.**Euro/Dollar: l’épreuve des faits**,

OFCE World Economic Outlook April 2004, Revue de l’OFCE No 89.**Combien nous coûte l’appréciation de l’euro ?**

OFCE World Economic Outlook April 2004, Revue de l’OFCE No 89.

## Reports & Chapters

**The impact of the recent euro appreciation on tourism in France ,**with Xavier Timbeau (OFCE).

OFCE working paper No 2005-17. In: Artus, P & L Fontagné,

Conseil d'Analyse Economique: Evolutions récentes du commerce extérieur français. Paris (France): La Documentation Française, 2006, p. 99-108.**Les perspectives économiques 2005-2009 : les voies d'une croissance autonome et****soutenue,**with Eric Heyer and Matthieu Lemoine, Rapport pour le Sénat, n°70, novembre 2004.**Perspectives économiques 2004-2008 pour un bon équilibre entre croissance et****assainissement structurel des finances publiques,**with Valérie Chauvin and Eric Heyer, Rapport pour le Sénat, n°69, novembre 2003.