Simple Formulas for Standard Errors that Cluster by Both Firm and Time

By S. B. Thompson. 2011

1,461 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4fbe3dbe41a60d80ab9773ce12d7e7d8fdce3371

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In Economics and Econometrics

Citations in the corpus

High Frequency Trading and Price Discovery (2014, methodology): “Statistical inference is conducted on the average stock-day estimates by calculating standard errors controlling for contemporaneous correlation across stocks and time series correlation within stocks using the clustering techniques in Petersen (2009) and Thompson (2011).”
Tournament Incentives and Corporate Fraud (2015, methodology): “12 Following Gow, Ormazabal, and Taylor (2010) and Thompson (2011), we use robust standard errors clustered at the firm and year level.”
The Role of State and Foreign Owners in Corporate Risk-Taking: Evidence from Privatization (2013, positive): “9 We estimate the regressions using OLS and calculate robust standard errors that cluster by both firms and country (Thompson, 2011).”
Liquidity Measurement Problems in Fast, Competitive Markets: Expensive and Cheap Solutions (2013, positive): “T-tests are calculated using standard errors that are robust to clustering along both the firm and the day dimension as described in Thompson (2011).18 The key comparison is between column (1), which reports liquidity measures based on MTAQ in seconds with no adjustments (i.e., the standard way…”
t-Statistic Based Correlation and Heterogeneity Robust Inference (2010, background): “The DGPs correspond to Panels B and C of Thompson (2006), where under ‘Individual Persistence’, ui,t = ξt + ηi,t, ηi,t = ρηi,t−1 + εi,t, ηi,0 = 0, ξt and εi,t are mutually independent and distributed i.”
The dynamics of behavior change: Evidence from energy conservation (2016, methodology): “In supplemental analyses, we implemented multi-way clustering using procedures described in Cameron et al. 2011) and Thompson (2011) to account for dependence in group and time dimensions.”
Subsidizing Liquidity: The Impact of Make/Take Fees on Market Quality (2014, positive): “Since the fees change for all stocks, we cannot address changes in the composition. However, we do find support for Colliard and Foucault’s theoretical prediction that an increase in the total fee decreases taker welfare. Furthermore, our findings support their prediction that the bid-ask spread dec”
Boardroom Centrality and Firm Performance (2013, positive): “The t-statistics are reported in parentheses and are based on two-way (firm and year) cluster robust standard errors to account for both cross-sectional and time-series dependence in the residuals (Petersen, 2009; Gow et al., 2010; Thompson, 2011).”
Shock-Based Causal Inference in Corporate Finance and Accounting Research (2015, methodology): “On two-way clustering on both firm and time, see Kezdi (2004) (simulations suggest that two-way clustering can be appropriate with as few as 10 observations in the shorter dimension); Thompson (2010) (formulas); Cameron et al. (2011) (Stata code cgmreg.ado available on Colin Cameron’s website).”
Algorithmic Trading and the Market for Liquidity (2013, positive): “Statistical inference is conducted across the 390 stock-day observations, controlling for contemporaneous cross-sectional correlation and within-stock correlation by double clustering standard errors on stock and day as suggested by Petersen (2009) and Thompson (2011).”