The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation

We examine the conditional logit estimator for binary panel data models with unobserved heterogeneity. A key assumption used to derive the conditional logit estimator is conditional serial independence (CI), which is problematic when the underlying innovations are serially correlated. A Monte Carlo experiment suggests that the conditional logit estimator is not robust to violation of the CI assumption. We find that higher persistence and smaller time dimension both increase the magnitude of the bias in slope parameter estimates. We also compare conditional logit to unconditional logit, bias corrected unconditional logit, and pooled correlated random effects logit.

Equity liquidity and tax aggressiveness in the Brazilian capital market

This study has investigated the potential relationship between equity liquidity and tax aggressiveness in the Brazilian capital market. Using a database of publicly traded Brazilian companies from 2010 to 2019 – not including the year 2020 due to the atypical effects of the COVID-19 pandemic – panel data models have been developed, the goal synthesis of which consisted in evaluating the longitudinal effects of equity liquidity, independent variable, on the book tax difference, dependent variable, and proxy of tax aggressiveness. Results have shown a statistically significant and economically positive relationship between the tax aggressiveness proxy and stockholding liquidity. Results suggests that companies with less volatile stocks, with larger relative stocks in B3 [(in full, B3 – Brasil Bolsa Balcão S.A.), formerly BMFBOVESPA, a stock exchange located at São Paulo, Brazil] businesses and lower trading costs tend to adopt a more aggressive tax planning. This article helps to demonstrate that in an emerging capital market such as the Brazilian one investor tend to belittle occasional increases in profits sparingly through more aggressive tax practices, however, which may result in future losses. Furthermore, this study helps to demonstrate the importance of disclosures about tax planning so that market agents can properly price financial assets.