Rank − 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents

By Xavier Gabaix, Rustam Ibragimov. 2007

625 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/b20bf8b75fd701b2bd11222bd00e808ca22760e2

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Citations in the corpus

Suffrage, Schooling, and Sorting in the Post-Bellum U.S. South (2012, positive): “Using the Gabaix and Ibragimov (2009) estimator, which regresses the log of the capital stock rank of a farm minus 1 2 on the log of the capital, gives a Pareto shape coefficient of b = 2.”
Portfolio diversification and value at risk under thick-tailedness (2009, positive): “zIn addition, as follows from the results in Ibragimov and Walden (2007), the value at risk and expected utility comparisons are closely linked: in particular, non-diversification results for truncations of extremely heavy-tailed distributions implied by the results in this paper also continue to ho”
Firm Sorting and Agglomeration (2018, methodology): “26The shape parameter ζj is estimated by running the following regression, following Gabaix and Ibragimov (2011): log ( rankij − 12 ) = αj − ζj log(value addedij) + ij , where j indexes industries, i indexes firms and rankji is the rank of firm i in industry j in terms of value added. to 1999-2006.”
Power Laws in Firm Size and Openness to Trade: Measurement and Implications (2010, positive): “This is the estimator suggested by Gabaix and Ibragimov (2011). The power law coefficient ζ̂LR has a standard error of |ζ̂LR|(N/2), where N is the sample size. This standard error is reported throughout for this estimator. As we show below, in practice the three estimators deliver remarkably similar”
Macroeconomic Crises since 1870 (2008, positive): “In joint work with Rustam Ibragimov, we will use the method of Gabaix and Ibragimov (2007) to estimate the distribution of disaster sizes, b, within a power-law context.”
Zipf's Law for Cities in the Regions and the Country (2011, positive): “…regressions yield R2-levels beyond 0.98.9 For the estimation we have considered two alternative methods that both draw on the recent contribution by Gabaix and Ibragimov (2008) who show that standard OLS yields biased results in rank-size regressions: (i) a normal log rank-log size regression with…”
Firm entry, trade, and welfare in Zipf's world (2013, positive): “This is the estimator suggested by Gabaix and Ibragimov (2009), which delivers very similar results.”
What is Different About Urbanization in Rich and Poor Countries? Cities in Brazil, China, India and the United States (2017, neutral): “The results for the U.S. show a coefficient of -.91, which is lower in magnitude than the -1.05 estimate discussed by Gabaix and Ibragimov (2011).”
The city size distribution debate: Resolution for US urban regions and megalopolitan areas (2012, background): “Gabaix and Ibragimov (2007) offer a simple solution to correcting for the small sample bias; also see Terra (2009). ize distribution debate: Resolution for US urban regions and megalopolitan (i.e. Gibrat’s Law operates), leading to a puzzle: Gibrat’s Law gives rise to a lognormal rather than a…”
Productive Cities: Sorting, Selection, and Agglomeration (2014, neutral): “Following Gabaix and Ibragimov ð2011Þ, we use logðrank 2 1=2Þ as the dependent variable in that case.”
Search Based Peer Firms: Aggregating Investor Perceptions through Internet Co-Searches (2015, result): “Earlier work, such as that of Guenther and Rosman (1994) and Kahle and Walkling (1996), compared two-digit SIC codes between Compustat and Center for Research in Security Prices (CRSP) sources and found that they disagreed, on average, 38% of the time; however, using Compustat SIC codes yielded high”