The Problem of Time Dependent Explanatory Variables at the Context-Level in Discrete Time Multilevel Event History Analysis: A Comparison of Models Considering Mobility Between Local Labour Markets as an Example

The paper deals with the question of how to include time dependent explanatory variables at the context-level in multilevel event history models. In general, context-level explanatory variables in multilevel models are assumed to be time constant. Only time constant context-level explanatory variables perform the task of reducing context-level error variance. Thus, it will be suggested that the analysis should be extended to a three-level model. In this model, time periods of persons constitute level 1 units, time periods of contexts constitute level 2 units and the contexts themselves constitute level 3 units – in which in turn level 2 units are clustered. Considering mobility between local labour markets as an example, four different ways of modelling time varying context-level variables are compared. The result is that the proposed three-level model leads to the most conservative results.     

The Disciplinary Effect of the Single Market on Swedish Firms

In the 1980s it became increasingly clear that the European Community market was still segmented by national borders because of non-tariff barriers to trade. A major objective of the Single Market Program in 1992 was to remove these barriers, thereby enforcing intra-union competition. In this paper, a panel of Swedish firm-level data is used to evaluate whether domestic market power has been curtailed as a consequence of the SMP and of the Swedish membership in the European Union in 1995. Evidence of increased competition emerges, as price-cost margins have declined in industries with high non-tariff barriers prior to 1992. JEL Classification Numbers: F15, L11, C33     

On Social Statement Spaces

In this fundamental draft we shall introduce the concept of social propositional spaces as a general notion of social science and in particular language, logic and methodology. This should help us bridge an old gap between extension and cognition, or said in more specific words, between space as conceived in mathematics or physics and space as is brought in by social location, by the meaning of predicates, their explanation and logic. ¹ We shall not rely on the idea of factor analysis where some “main components” are fit into the data. Because the notion of euclidean normalization and orthogonality, in this context, is a bit artificial. We first show how combinatorial manifolds of statements can be represented in finite vector spaces over the Galois field F₂. This space which we denote as logic statement space can be spanned by generators of alternating codes. In this way the old concepts of truth tables and interaction attributes can be linked to informatics and in particular code theory and geometry. Extending the logic statement space onto a real vector space by mapping units of the finite rings onto units of the real rings we obtain logistic statement spaces. It turns out that vectors in logistic statement spaces represent cross-tables of logits connected with logistic regression analysis. As a completion of the theory we show that the logit is the first derivative of entropy with respect to probability.     

Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers

In this paper we estimate the DEA technical efficiency for 4796 Brazilian municipalities, by applying a recently proposed “Jackstrap” method, which combines Bootstrap and Jackknife resampling techniques, to reduce the effect of outliers and possible errors in the data set. We perform calculations to identify and eliminate high leverage municipalities, using different variants of Data Envelopment Analysis (DEA), as well as Free Disposal Hull (FDH). Corroborating previous results, efficiency results for the Brazilian municipalities show a clear relationship between the size of the municipality and its efficiency scores. Indeed, under both DEA variants, smaller cities tend to be less efficient than larger ones hence indicating that the quality of the frontier adjustment improves significantly as the size of the municipality increases. We present arguments that may explain to some extent these findings, such as economies of scale and the excess spending due to revenue from royalties. However, such effects require further, more careful examination.     

Prediction of the next hypercalcemia free period: application of random effect models with selection on first event

The length of repeated hypercalcemia free periods of patients with bone metastasis of breast cancer with at least one hypercalcemic event was modelled according to a generalized linear mixed model formulated in terms of transition probabilities and according to a latent variable model. In the former case the periods were assumed to be lognormally distributed with two variance components (patients and residue). In the latter case the conditional intensity given a patient was assumed to be the intensity of the Weibull distribution, while the random patient effect (frailty) was assumed to be drawn from a gamma distribution. In both cases the selection of only patients with at least one hypercalcemic event was taken into consideration. In both models the variance of the patient effect turned out to be negligible. For the second and later periods the Weibull appeared to fit better than the lognormal model. For the first period there was almost no information available.     

Descriptive statistics of large data sets by scatter plots, an exploratory approach

In the analysis of large tables of M variables on N observations one is interested in the relations between the variables and it is usual to inspect the M(M-1)/2 scatter plots of N points. Clearly, the scatter plot approach relies on visual inspection and is to be preferred in so far as applicable to detect simple relations, namely when M is small. Other approaches are needed for large values of M .
We consider that only the relatively few scatter plots that present a 'structure' are of interest for an exploratory analysis and, by 'structure', we mean a domain of specially high local density in the plot. Based on this concept, we propose a method constructed around two steps: the selection of the possibly interesting pairs of variables and the validation of the corresponding scatter plots. The selection of the pairs results from an algorithm based on a binary partitioning tree. The validation of the corresponding scatter plots enables the production of only those where a structure is found the recognition of a structure is derived from a statistic based on the length of the Minimum Spanning Tree constructed on the N points of the candidate scatter plot.
For illustration, we report on an industrial application where the method is routinely applied for exploratory purposes.     

Is precise econometrics an illusion?1

Abstract

Many empirical economists say that the teaching of econometrics is unbalanced, and students are not well-prepared for the serious problems they will encounter with real data. Here, the author considers the problem of noisy data, which is present in most econometric studies, but receives far too little attention. Most econometric studies are done in a world of low signal-to-noise ratios, and educated common sense suggests that we cannot expect precise results in such an environment. Sensitivity analysis shows that the apparent precision of reported econometric results is generally an illusion, because it is highly dependent on error term independence assumptions.^1,2     

Partial identification of distributional and quantile treatment effects in difference-in-differences models

In this paper, we supplement the identification results for the mean treatment effect for the treated in the difference-in-differences framework studied by Abadie (2005) by establishing partial identification results for the distribution and quantile of the counterfactual outcome and of the treatment effect for the treated. Unlike the mean treatment effect which is identifiable from either the panel or the repeated cross sectional data, we show that the distribution/quantile of either the counterfactual outcome or of the treatment effect is only partially identified and in general the identified interval is tighter with panel than with the repeated cross sectional data. We further illustrate this gain from the panel via a numerical example.     

TESTING FOR STATIONARITY WITH A BREAK IN PANELS WHERE THE TIME DIMENSION IS FINITE

In this paper, we consider the case of finite time dimension in the panel stationarity tests with structural breaks. By fixing T, the finite sample properties of the tests for both micro (T small and N large) and macro (both T and N large) panel data are generally greatly improved. More importantly, the derivation of the tests for finite T and , as opposed to joint asymptotic where N and simultaneously, avoids the imposition of the rate condition making the test valid for any (T, N) blend. Four models corresponding to the usual combination of breaks are considered. The asymptotic distributions of the test are derived under the null hypothesis and are shown to be normally distributed. Their moments for T fixed are derived analytically employing Ghazal’s corollary 1. The case with unknown breaks is also considered. The proposed tests have generally empirical sizes that are very close to the nominal size. The Monte Carlo simulations show that the power of the test statistics increases substantially with N and T.     

入境旅游对地区收入的影响

本文利用面板数据模型对不同地区的入境旅游消费支出与地区收入进行实证分析,结果发现,不同地区入境旅游的消费支出并没有显著差异,入境旅游的消费支出每提高1个百分点,国内主要城市的旅游收入平均只能提高0.09个百分点。不同区域的自发收入水平差距是最重要的影响因素,其中,旅游发展的自发收入以拉萨最高,其次是深圳和南京等城市;最低的城市为兰州,其次是西宁和成都等城市。本文认为提高旅游发展投资力度、规范旅游发展制度、进一步开发多元化旅游市场、扩大文化交流和加大海外旅游营销力度是提高我国入境旅游业的发展能力,缩小不同区域间的旅游发展差距的有利举措。