Matteo Barigozzi

Economics and Econometrics · 2008–2019

A researcher in the Science corpus: 9 papers; trusts 77 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Economics and Econometrics — 242 citations into it; #3,949 of 23,481 by trust here, #6,370 by citations
• Finance — 122 citations into it; #2,096 of 10,518 by trust here, #2,779 by citations
• Statistics and Probability — 86 citations into it; #2,640 of 12,932 by trust here, #4,517 by citations
• Management Science and Operations Research — 21 citations into it
• Artificial Intelligence — 20 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Statistical Learning Theory — 11 citations into it
• Control and Systems Engineering — 10 citations into it

Whom Matteo Barigozzi trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jushan Bai
2. M. Forni
3. Serena Ng
4. A. Onatski
5. James H. Stock
6. Marc Hallin
7. Hui Zou
8. Marco Lippi
9. Mark W. Watson
10. Jianqing Fan

Who trusts Matteo Barigozzi overall

1. Markus Pelger
2. Jianqing Fan
3. M. Forni
4. Lorenzo Trapani
5. S. Freyaldenhoven
6. Marc Hallin
7. Marco Lippi
8. Jushan Bai
9. Yuan Liao
10. Yacine Aït-Sahalia

Papers from Matteo Barigozzi's perspective

Ordered by how strongly the researchers Matteo Barigozzi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Shrinkage and Selection via the Lasso
2. Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
3. Determining the Number of Factors in Approximate Factor Models
4. Are more data always better for factor analysis
5. Macroeconomic Forecasting Using Diffusion Indexes
6. Inferential Theory for Factor Models of Large Dimensions
7. The Adaptive Lasso and Its Oracle Properties
8. Determining the Number of Factors from Empirical Distribution of Eigenvalues (Review of Economics and Statistics)
9. Monetary Policy in a Data-Rich Environment
10. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

Their papers

• Improved penalization for determining the number of factors in approximate factor models (2010) — 354 citations
• Simultaneous multiple change-point and factor analysis for high-dimensional time series (2016) — 106 citations
• Non-Stationary Dynamic Factor Models for Large Datasets (2016) — 51 citations
• NETS: Network estimation for time series (2019) — 189 citations
• A network analysis of the volatility of high dimensional financial series (2017) — 81 citations
• Generalized dynamic factor models and volatilities: estimation and forecasting (2017) — 49 citations
• Generalized dynamic factor models and volatilities: recovering the market volatility shocks (2016) — 66 citations
• Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy (2014) — 185 citations
• A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models (2008) — 72 citations