Michele Aquaro

Econometric Institute
Erasmus School of Economics
Erasmus University Rotterdam

Research field: Econometrics
PhD: Tilburg University
Full CV: here

Contact:
aquaro [at] ese [dot] eur [dot] nl

Last updated Sep 2026

Field of specialization

Econometrics (theory and applied), with focus on micro-econometrics, panel data methods, and labour. I also have an interest in robust statistics.

Working Papers

• Detecting structural breaks in panel data models of social interactions with unknown networks
  with Ryo Okui and Wendun Wang
  
  [Abstract] [Paper coming soon]

  This paper aims to detect structural break points in latent networks in a panel data setting. We consider panel models where the outcome of a unit depends on the outcomes and characteristics of other units. The latent network structure induces high-dimensional parameters and interactive outcomes generate endogeneity. Our goal is to detect breaks in high-dimensional network parameters associated with endogenous variables. We propose a two-step penalized nonlinear least squares approach to estimate the break points based on reduced forms, and show that the resulting estimator achieves superconsistency. This property allows us to estimate, and make inferences on, network and slope parameters as if the true break points were known.

Work in progress

• Career Dynamics and Labor-Market Entry at the Parent’s Employer
  with Paul Bingley, Lorenzo Cappellari, and Konstantinos Tatsiramos

• Outlier-robust difference-in-differences
  with Mikhail Zhelonkin

Publications

• Innovative mergers and acquisitions and the broker regions of European integration
  with Giacomo Damioli and Balázs Lengyel
  Regional Studies, 57:2, 2023, p.287-299
  [Abstract] [Paper] [Supplementary Appendix]

  Cross-regional mergers and acquisitions (M&A) transfer control and diffuse knowledge across space, which facilitates the integration of business systems. We analyse about 40,000 cross-regional acquisitions in Europe completed between 2003 and 2017 and distinguish innovative and non-innovative M&A. Both types of deals cluster into communities constituted by countries or groups of neighbouring countries. However, an increasing proportion of deals connect different communities, especially for innovative M&A. More populous and richer regions host more acquiring and target companies and thus broker communities. Research and development expenditure and skilled human capital are additional factors favouring brokerage of regions by attracting acquirers.

• Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices
  with Natalia Bailey and M. Hashem Pesaran
  Journal of Applied Econometrics, 36:1, 2021, p.18-44
  The companion package hetsar is available in R, Stata, Python, and MATLAB
  
  [Abstract] [Paper] [Working Paper] [Replication files]

  This paper considers the estimation and inference of spatial panel data models with heterogeneous spatial lag coefficients, with and without weakly exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of the spatial coefficients are derived. The QML estimators of individual spatial coefficients, as well as their mean group estimators, are shown to be consistent and asymptotically normal. Small‐sample properties of the proposed estimators are investigated by Monte Carlo simulations and results are shown to be in line with the paper's key theoretical findings, even for panels with moderate time dimensions and irrespective of the number of cross‐section units. A detailed empirical application to US house price changes during the 1975–2014 period shows a significant degree of heterogeneity in spatiotemporal dynamics over the 338 Metropolitan Statistical Areas considered.

• Robust estimation and moment selection in dynamic fixed-effects panel data models
  with Pavel Cizek
  Computational Statistics, 33:2, 2018, p.675-708
  [Abstract] [Paper]

  Considering linear dynamic panel data models with fixed effects, existing outlier–robust estimators based on the median ratio of two consecutive pairs of first-differenced data are extended to higher-order differencing. The estimation procedure is thus based on many pairwise differences and their ratios and is designed to combine high precision and good robust properties. In particular, the proposed two-step GMM estimator based on the corresponding moment equations relies on an innovative weighting scheme reflecting both the variance and bias of those moment equations, where the bias is assumed to stem from data contamination. To estimate the bias, the influence function is derived and evaluated. The robust properties of the estimator are characterized both under contamination by independent additive outliers and the patches of additive outliers. The proposed estimator is additionally compared with existing methods by means of Monte Carlo simulations.

• Robust estimation of dynamic fixed-effects panel data models
  with Pavel Cizek
  Statistical Papers, 55:1, 2014, p.169-186
  [Abstract] [Paper]

  This paper extends an existing outlier-robust estimator of linear dynamic panel data models with fixed effects, which is based on the median ratio of two consecutive pairs of first-order differenced data. To improve its precision and robustness properties, a general procedure based on higher-order pairwise differences and their ratios is designed. The asymptotic distribution of this class of estimators is derived. Further, the breakdown point properties are obtained under contamination by independent additive outliers and by the patches of additive outliers, and are used to select the pairwise differences that do not compromise the robustness properties of the procedure. The proposed estimator is additionally compared with existing methods by means of Monte Carlo simulations.

• One-step robust estimation of fixed-effects panel data models
  with Pavel Cizek
  Computational Statistics & Data Analysis, 57:1, 2013, p.536-548
  [Abstract] [Paper]

  The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods available for fixed-effects panel data. A new estimation approach based on two different data transformations is therefore proposed. Considering several robust estimation methods applied to the transformed data, the robust and asymptotic properties of the proposed estimators are derived, including their breakdown points and asymptotic distributions. The finite-sample performance of the existing and proposed methods is compared by means of Monte Carlo simulations.

Permanent working papers

• Estimation and inference for spatial models with heterogeneous coefficients in MATLAB, Python, R, and Stata
  with Federico Belotti and Ida Johnsson
  The package hetsar is available in R, Stata, Python, and MATLAB

Policy reports

• Innovative mergers and acquisitions and the integration of European regions
  with Giacomo Damioli and Balazs Lengyel
  Scientific and Technical Research Reports, 2020, JRC122436
  
  [Abstract] [Policy report]

  Mergers and acquisitions (M&A) entail the substantial reallocation of economic activities. When they involve distant acquiring and target companies, they transfer control and diffuse knowledge across locations, which in turn facilitates the process of the integration of business systems. This study aims to understand how cross-regional European M&A facilitate the process of European integration. We applied social network analysis and regression techniques to a sample of cross-regional acquisitions between 2003 and 2017. The data allow us to identify whether or not a target company had an active patent portfolio at the time of deal completion. Both types of deals are highly concentrated in economically more developed regions and cluster into communities constituted by countries or groups of neighbouring countries. However, a large and increasingly non-trivial proportion of deals connect different communities, and to a larger extent for innovative than for non-innovative M&A. More populous and richer regions host a disproportionally larger number of acquiring and target companies and thus connect fragmented communities. The intensity of R&D-related expenditures provides an additional factor favouring the connection of fragmented groups of regions by attracting technology-seeking acquirers.