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We re-analyze the effects of a Danish active labour market program social experiment that included a range of sub-treatments, including monitoring, job search assistance and training. Previous studies have shown that the overall effect of the experiment is positive. We apply newly developed non-parametric methods to determine which of the individual policies that explains the positive effect. The use of non-parametric methods to separate sub-treatment effects is important from a methodological point of view, since the alternative, namely parametric/distributional assumptions, is in conflict with the concept of experimental evidence. Our results are highly relevant in a policy perspective, as optimal labour market policy design requires knowledge on the effectiveness of specific policy measures.

We reanalyze the effects of a Danish active labour market program social experiment, that included a range of sub-treatments, including monitoring, job search assistance and training. Previous studies have shown that the overall effect of the experiment is positive. We apply newly developed non-parametric methods to determine which of the individual policies that explains the positive effect. The use of non-parametric methods to separate sub-treatment effects is important from a methodological point of view, since the alternative, namely parametric/distributional assumptions, is in conflict with the concept of experimental evidence. Our results are highly relevant in a policy perspective, as optimal labour market policy design requires knowledge on the effectiveness of specific policy measures.

This research paper presents statistical comparisons between two methods that are commonly used to estimate option implied Risk-Neutral Densities (RND). These are: 1) mixture of lognormals (MXL); and, 2) volatility function technique (VFT). The former is a parametric method whilst the latter is a non-parametric approach. The RNDs are extracted from over-thecounter European-style options on the Mexican Peso–US Dollar exchange rate. The non-parametric method was the superior one for out-of-sample evaluations. The implied mean, median and mode were, in general, statistically different between the competing approaches. It is recommended to apply the VFT instead of the MXL given that the former has superior accuracy and it can be estimated when there is a relatively short crosssection of option exercise price range. The results have implications for financial investors and policy makers given that they could use the information content in options to analyze market's perceptions about the future expected variability of the financial asset under study. Clasificación JEL: C14, C52, F31, G13.

AbstractThis article examines the spatial variability of extreme precipitation trends in northwestern Algeria (Macta) and compares the results obtained from the four recent and old non‐parametric methods. A dataset of annual maximum precipitation consisting of 41 observation years (1970–2010) and 41 rain gauges was used. The results of the four old and new methods used to detect trends, Mann–Kendall (MK), Bravais–Pearson (BP), Spearman (SR) and innovative trend analysis (ITA), show good agreement. They revealed that a decrease in the trend of annual maximum precipitation was detected during the first period (1970–1992) with −44% (MK), −61% (BP), −68% (SR) and −76% (ITA). On the other hand, in the second period (1993–2010), a total shift occurred in which a significant increase in annual maximum precipitation trends was observed with +63% (BP), +34% (MK and SR) and +93% (ITA). These results show the ability of ITA to detect partial trends that the other three tests do not allow. Our results allow decision‐makers to properly design adaptation strategies in the face of the intensification of these extreme events.

This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.

We employ a wide range of parametric and non–parametric cost frontiers' efficiency estimation methods to estimate economic efficiency and economies of scale, using the same panel data of 22 Taiwanese commercial banks over the period 1982–97. According to our empirical implementation, the two methodologies yield similar average efficiency estimates, yet they come to very dissimilar results pertaining to the efficiency rankings, the stability of measured efficiency over time, the consistency between frontier efficiency and conventional performance measures, and the estimates of scale economies. Thus, the choice of an estimation approach can result in very different conclusions and policy implications regarding cost efficiencies and cost economies. These findings suggest that making policy decisions and evaluations relies on multiple techniques and specifications.

This paper analyses the effect of corruption on Multinational Enterprises' (MNEs) incentives to undertake FDI in a particular country. We contribute to the existing literature by modelling the relationship between corruption and FDI using both parametric and non-parametric methods. We report that the impact of corruption on FDI stock is different for the different quantiles of the FDI stock distribution. This is a characteristic that could not be captured in previous studies which used only parametric methods. After controlling for the location selection process of MNEs and other host country characteristics, the result from both parametric and non-parametric analyses offer some support for the 'helping-hand' role of corruption. [Copyright Elsevier B.V.]

Estonia has the highest gender wage gap in the European Union and the highest degree of gender segregation by occupation and industry. Previous studies have found that most of the gap remains unexplained by personal and job characteristics. However, key job characteristics, occupation and industry, are usually imprecisely measured, leading to the question of whether all relevant characteristics have been properly taken into account in the decompositions. In this paper, we perform the decomposition of the wage gap to see how much of the wage gap remains unexplained after using more detailed data than has been common in previous studies (the Estonian Structure of Earnings Study data set), and discuss the related methodological challenges. Using a non-parametric method that takes into account differences in supports of distributions of male and female workers' characteristics, we find that using more detailed data does not eliminate the unexplained wage gap: about half of the wage gap remains unexplained even using the full set of available variables.