Two-step QCA revisited: the necessity of context conditions

Quality & Quantity - The so-called two-step QCA approach as formulated by Schneider and Wagemann (Eur J Polit Res 45(5):751–786, 2006) proposes a separation of conditions into two...     

Bootstrap Co‐integration Rank Testing: The Effect of Bias‐Correcting Parameter Estimates

Bootstrap‐based methods for bias‐correcting the first‐stage parameter estimates used in some recently developed bootstrap implementations of co‐integration rank tests are investigated. The procedure constructs estimates of the bias in the original parameter estimates by using the average bias in the corresponding parameter estimates taken across a large number of auxiliary bootstrap replications. A number of possible implementations of this procedure are discussed and concrete recommendations made on the basis of finite sample performance evaluated by Monte Carlo simulation methods. The results show that bootstrap‐based bias‐correction methods can significantly improve the small sample performance of the bootstrap co‐integration rank tests.     

Unshrouding for Competitive Advantage

In a market with hidden product details and systematic consumer biases, firms have the possibility to unshroud and thereby to rectify such market obliquities. While the classical view was that firms will have an incentive to unshroud, Gabaix and Laibson (2006) show that there exist constellations in which firms prefer to leave the market shrouded. Building on that model I introduce a more strategic and long‐term dimension of unshrouding which turns out to fundamentally alter the underlying incentives to unshroud. In particular, I show that there exists an incentive to unshroud that stems from differences in add‐on profitability and that it is dependent on parameter constellations whether a more profitable or a less profitable firm will want to unshroud.