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Abstract: Colluding firms often exchange private information and make transfers within the cartels based on the information. Estimating the impact of such collusive practices— known as the “lysine strategy profile (LSP)”— on cartel duration is difficult because of endogeneity and omitted variable bias. I use firms’ linguistic differences as an instrumental variable for the LSP in 135 cartels discovered by the European Commission since 1980. The incidence of the LSP is not significantly related to cartel duration. After correction for selectivity in the decision to use the LSP, statistical tests are consistent with a theoretic prediction that the LSP increases cartel duration. Journal of Economic Literature

In this paper we consider several modified wild bootstrap methods that, additionally to heteroskedasticity, can take dependence into account. The modified wild bootstrap methods are shown to correctly replicate an invariance principle for multivariate time series that are characterized by general forms of unconditional heteroskedasticity, or nonstationary volatility, as well as dependence within and between different elements of the time series. The invariance principle is then applied to derive the asymptotic validity of the wild bootstrap methods for unit root testing in a multivariate setting. The resulting tests, which can also be interpreted as panel unit root tests, are valid under more general assumptions than most current tests used in the literature. A simulation study is performed to evaluate the small sample properties of the bootstrap unit root tests.

The Sign Covariance Matrix is an orthogonal equivariant estimator of multivariate scale. It is often used as an easy-to-compute and highly robust estimator. In this paper we propose a k-step version of the Sign Covariance Matrix, which improves its efficiency while keeping the maximal breakdown point. If k tends to infinity, Tyler's M-estimator is obtained. It turns out that even for very low values of k, one gets almost the same efficiency as Tyler's M-estimator. © 2010 The Author(s). info:eu-repo/semantics/published SCOPUS: ar.j

A Direct Monte Carlo (DMC) approach is introduced for posterior simulation in theInstrumental Variables (IV) model with one possibly endogenous regressor, multipleinstruments and Gaussian errors under a flat prior. This DMC method can also beapplied in an IV model (with one or multiple instruments) under an informativeprior for the endogenous regressor's effect. This DMC approach can not be appliedto more complex IV models or Simultaneous Equations Models with multiple endogenous regressors. An Approximate DMC (ADMC) approach is introduced thatmakes use of the proposed Hybrid Mixture Sampling (HMS) method, which facilitates Metropolis-Hastings (MH) or Importance Sampling from a proper marginalposterior density with highly non-elliptical shapes that tend to infinity for a pointof singularity. After one has simulated from the irregularly shaped marginal distri-bution using the HMS method, one easily samples the other parameters from theirconditional Student-t and Inverse-Wishart posteriors. An example illustrates theclose approximation and high MH acceptance rate. While using a simple candidatedistribution such as the Student-t may lead to an infinite variance of ImportanceSampling weights. The choice between the IV model and a simple linear model un-der the restriction of exogeneity may be based on predictive likelihoods, for whichthe efficient simulation of all model parameters may be quite useful. In future workthe ADMC approach may be extended to more extensive IV models such as IV withnon-Gaussian errors, panel IV, or probit/logit IV.

In his classic model of trust, Coleman (1990) argues that the decision of an actor to trust or not is a function of the expected gain and loss involved. In this paper we show how actors who attempt to recruit others into network marketing employ narrative in manipulating the recruit's expectations. For example, we show how the costs involved in recruiting friends and family into network marketing are transformed into benefit through narrative strategies. Through the strategic use of narrative, recruiters change the recruit's perception of the parameters of the Coleman model and make perfectly rational behavior that was not considered rational before. In addition, we argue that recruiters use similar narrative on themselves in reconfirming the validity of their own previous decision to trust.