The influence function of penalized regression estimators

To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers. Therefore, alternatives as penalized M-estimation or the sparse least trimmed squares (LTS) estimator have been proposed. The robustness of these regression methods can be measured with the influence function. It quantifies the effect of infinitesimal perturbations in the data. Furthermore, it can be used to compute the asymptotic variance and the mean-squared error (MSE). In this paper we compute the influence function, the asymptotic variance and the MSE for penalized M-estimators and the sparse LTS estimator. The asymptotic biasedness of the estimators make the calculations non-standard. We show that only M-estimators with a loss function with a bounded derivative are robust against regression outliers. In particular, the lasso has an unbounded influence function.     

SOCIAL DRINKING VERSUS ADMINISTERING ALCOHOL

Alcohol consumption tends to make some people (unwillingly) tell the truth, hence social drinking can serve as a signal in social contact games. We provide empirical evidence which shows that social drinking can serve as a trust facilitating mechanism. Liver cirrhosis mortality and the rate of abstainers are used to construct a novel index of moderate alcohol consumption, which correlates with trust levels in a cross‐country analysis.(JEL L14, D82, D71)     

Sind die Regeln der externen Unternehmensrechnung auf die kommunale Rechnungslegung übertragbar?

The replacement of public sector accounting methods in municipalities by double-entry bookkeeping has been discussed as well as implemented for some time now. This paper examines whether municipal accounting comprises accounting purposes which are comparable to the purposes of external financial reporting and whether the recognition and valuation rules used in firms can also be applied to municipal accounting. We argue that municipalities and businesses have similar accounting purposes. However, the application of financial reporting, including the corresponding recognition and valuation rules, to municipal accounting requires a critical look for several reasons. First, the concept of accounting income is not suitable in terms of decision-facilitating and decision-influencing in municipalities. Furthermore, it is doubtful how meaningful the valuations of municipal assets in annual financial statements are. The equivalent of providing dividend restrictions based on accounting income is often thought to consist in ensuring intergenerational fairness by asset preservation in municipalities. However, this element of municipal accounting requires rules that are not based on an asymmetrical recording of risks and chances according to the principle of prudence and that take into account the assessment of future generations regarding the benefits of municipal projects.     

Influence measures in affine combination type regression

The detection of outliers and influential observations has received a great deal of attention in the statistical literature in the context of least-squares (LS) regression. However, the explanatory variables can be correlated with each other and alternatives to LS come out to address outliers/influential observations and multicollinearity, simultaneously. This paper proposes new influence measures based on the affine combination type regression for the detection of influential observations in the linear regression model when multicollinearity exists. Approximate influence measures are also proposed for the affine combination type regression. Since the affine combination type regression includes the ridge, the Liu and the shrunken regressions as special cases, influence measures under the ridge, the Liu and the shrunken regressions are also examined to see the possible effect that multicollinearity can have on the influence of an observation. The Longley data set is given illustrating the influence measures in affine combination type regression and also in ridge, Liu and shrunken regressions so that the performance of different biased regressions on detecting and assessing the influential observations is examined.     

WITHIN U.S. TRADE AND THE LONG SHADOW OF THE AMERICAN SECESSION

Using data from U.S. commodity flow survey, we show that the historical Union–Confederacy border lowers contemporaneous trade between U.S. states by about 13%. The finding is robust over econometric models, survey waves, or aggregation levels. Including contemporaneous controls, such as network or institutional variables, lowers the estimate only slightly. Historical variables, such as slavery, do not explain the effect. Adding U.S. states unaffected by the Civil War, we argue that the friction is not merely reflecting unmeasured North–South differences. Finally, the border effect is larger for differentiated than for homogeneous goods, stressing the potential role for cultural factors and trust. (JEL F15, N72, N92, Z10)     

The Role of Similarity Cues in the Development of Trustin Sources of Information About GM Food

In evaluating complex new technologies, people are usually dependent on information provided by others, for example, experts or journalists, and have to determine whether they can trust these information sources. This article focuses on similarity as the basis for trust. The first experiment ( N  = 261) confirmed that a journalist writing about genetically modified (GM) food was trusted more when his attitude was congruent with that of his readers. In addition, the experiment showed that this effect was mediated by the perceived similarity of the journalist. The second experiment ( N  = 172) revealed that trust in a journalist writing about the focal domain of GM food was even influenced by him expressing a congruent attitude in an unrelated domain. This result supports a general similarity account of the congruence effect on trust, as opposed to a confirmatory bias account.     

Survival functions for the frailty models based on the discrete compound Poisson process

Frailty models are often used to model heterogeneity in survival analysis. The distribution of the frailty is generally assumed to be continuous. In some circumstances, it is appropriate to consider discrete frailty distributions. Having zero frailty can be interpreted as being immune, and population heterogeneity may be analysed using discrete frailty models. In this paper, survival functions are derived for the frailty models based on the discrete compound Poisson process. Maximum likelihood estimation procedures for the parameters are studied. We examine the fit of the models to earthquake and the traffic accidents’ data sets from Turkey.     

Robust ridge and robust Liu estimator for regression based on the LTS estimator

In the multiple linear regression analysis, the ridge regression estimator and the Liu estimator are often used to address multicollinearity. Besides multicollinearity, outliers are also a problem in the multiple linear regression analysis. We propose new biased estimators based on the least trimmed squares (LTS) ridge estimator and the LTS Liu estimator in the case of the presence of both outliers and multicollinearity. For this purpose, a simulation study is conducted in order to see the difference between the robust ridge estimator and the robust Liu estimator in terms of their effectiveness; the mean square error. In our simulations, the behavior of the new biased estimators is examined for types of outliers: X-space outlier, Y-space outlier, and X-and Y-space outlier. The results for a number of different illustrative cases are presented. This paper also provides the results for the robust ridge regression and robust Liu estimators based on a real-life data set combining the problem of multicollinearity and outliers.