Optimal Industrial Classification: An Application to the German Industrial Classification System

A widely used method in the analysis of large-scale econometric models is to replace the ``true model'' by an aggregative one in which the variables are grouped and replaced by sums or weighted averages of the variables in each group. The modes of aggregation of the independent and dependent variables may in principle be chosen optimally by minimizing a measure of mean-square forecast error in predicting the dependent variables from the independent variables by using the aggregative rather than detailed variables. However, this results in an optimization problem of a high degree of complexity. Nevertheless, many efficient optimization heuristics have been developed for these kinds of complex problems. We implement the Threshold Accepting heuristic for the problem of optimal aggregation of price indices in a model of the transmission of external (import and export) prices on internal prices, using German data. The algorithm and the resulting groupings are presented. The results suggest that the use of standard or ``official'' modes of aggregation will in general be far from being optimal.

Cardinality versus q-Norm Constraints for Index Tracking

Index tracking aims at replicating a given benchmark with a smaller number of its constituents. Different quantitative models can be set up to determine the optimal index replicating portfolio. In this paper, we propose an alternative based on imposing a constraint on the q-norm, 0 < q < 1, of the replicating portfolios’ asset weights: the q-norm constraint regularises the problem and identifies a sparse model. Both approaches are challenging from an optimisation viewpoint due to either the presence of the cardinality constraint or a non-convex constraint on the q-norm. The problem can become even more complex when non-convex distance measures or other real-world constraints are considered. We employ a hybrid heuristic as a flexible tool to tackle both optimisation problems. The empirical analysis on real-world financial data allows to compare the two index tracking approaches. Moreover, we propose a strategy to determine the optimal number of constituents and the corresponding optimal portfolio asset weights

Sensory drive mediated by climatic gradients partially explains divergence in acoustic signals in two horseshoe bat species, Rhinolophus swinnyi and Rhinolophus simulator

Geographic variation can be an indicator of still poorly understood evolutionary processes such as adaptation and drift. Sensory systems used in communication play a key role in mate choice and species recognition. Habitat-mediated (i.e. adaptive) differences in communication signals may therefore lead to diversification. We investigated geographic variation in echolocation calls of African horseshoe bats, Rhinolophus simulator and R . swinnyi in the context of two adaptive hypotheses: 1) James' Rule and 2) the Sensory Drive Hypothesis. According to James' Rule body-size should vary in response to relative humidity and temperature so that divergence in call frequency may therefore be the result of climate-mediated variation in body size because of the correlation between body size and call frequency. The Sensory Drive Hypothesis proposes that call frequency is a response to climate-induced differences in atmospheric attenuation and predicts that increases in atmospheric attenuation selects for calls of lower frequency. We measured the morphology and resting call frequency (RF) of 111 R . simulator and 126 R . swinnyi individuals across their distributional range to test the above hypotheses. Contrary to the prediction of James' Rule, divergence in body size could not explain the variation in RF. Instead, acoustic divergence in RF was best predicted by latitude, geography and climate-induced differences in atmospheric attenuation, as predicted by the Sensory Drive Hypothesis. Although variation in RF was strongly influenced by temperature and humidity, other climatic variables (associated with latitude and altitude) as well as drift (as suggested by a positive correlation between call variation and geographic distance, especially in R . simulator ) may also play an important role

Exploring and Visualizing A-Train Instrument Data

The succession of US and international satellites that follow each other in close succession, known as the A-Train, affords an opportunity to atmospheric researchers that no single platform could provide: Increasing the number of observations at any given geographic location.. . a more complete "virtual science platform". However, vertically and horizontally, co-registering and regridding datasets from independently developed missions, Aqua, Calipso, Cloudsat, Parasol, and Aura, so that they can be inter-compared can be daunting to some, and may be repeated by many. Scientists will individually spend much of their time and resources acquiring A-Train datasets of interest residing at various locations, developing algorithms to match up and graph datasets along the A-Train track, and search through large amounts of data for areas and/or phenomena of interest. The aggregate amount of effort that can be expended on repeating pre-science tasks could climb into the tens of millions of dollars. The goal of the A-Train Data Depot (ATDD) is to enable free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) is accomplished by through the ATDDs Giovanni data analysis and visualization tool. Giovanni brings together data from Aqua (MODIS, AIRS, AMSR-E), Cloudsat (cloud profiling radar) and Calipso (CALIOP, IIR), as well as the Aura (OMI, MLS, HIRDLS, TES) to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. This easy to learn and use exploration tool will allow users to create vertical profiles of any desired A-Train dataset, for any given time of choice. This presentation shows the power of Giovanni by describing and illustrating how this tool facilitates and aids A-Train science and research. A web based display system Giovanni provides users with the capability of creating co-located profile images of temperature and humidity data from the MODIS, MLS and AIRS instruments for a user specified time and spatial area. In addition, Cloud and Aerosol profiles may also be displayed for the Cloudsat and Caliop instruments. The ability to modify horizontal and vertical axis range, data range and dynamic color range is also provided. Two dimensional strip plots of MODIS, AIRS, OM1 and POLDER parameters, co-located along the Cloudsat reference track, can also be plotted along with the Cloudsat cloud profiling data. Center swath pixels for the same parameters can also be shown as line plots overlaying the Cloudsat or Calipso profile images. Images and subsetted data produced in each analysis run may be downloaded. Users truly can explore and discover data specific to their needs prior to ever transferring data to their analysis tools

Empirical macromodels under test: a comparative simulation study of the employment effects of a revenue neutral cut in social security contributions

In the paper we simulate a revenue-neutral cut in the social security contribution rate using five different types of macro- / microeconomic models, namely two models based on time-series data where the labour market is modelled basically demand oriented, two models of the class of computable equilibrium models which are supply oriented and finally a firm specific model for international tax burden comparisons. Our primary interest is in the employment effects the models predict due to the cut in the contribution rate. It turns out that qualitatively all models considered predict an increase in employment three years after the cut. But the employment effects differ considerably in magnitude, which follows immediately from the different behavioral assumptions underlying the different models. -- In dem Beitrag wird der Beschäftigungseffekt infolge einer aufkommensneutralen Senkung der Sozialversicherungsbeiträge simuliert. Zu diesem Zweck werden fünf unterschiedliche ökonomische Modelle verwendet, namentlich zwei Modelle, die auf Zeitreihendaten aufbauen und in denen der Arbeitsmarkt überwiegend von der Nachfrageseite dominiert wird, zwei Modelle aus der Klasse der computable equilibrium models, die typischerweise angebotsorientiert sind, und ein mikroökonomisches, firmenspezifisches Steuerbelastungsvergleichsmodell. Alle Simulationsergebnisse der Modelle weisen auf einen, wenngleich teilweise kleinen, positiven Beschäftigungseffekt hin, der sich allerdings beträchtlich in seiner Größenordnung unterscheidet. Dies ist eine unmittelbare Folge aus den unterschiedlichen Verhaltensannahmen, die den einzelnen Modellen unterliegen.

Where is the best site on Earth? Domes A, B, C and F, and Ridges A and B

The Antarctic plateau contains the best sites on earth for many forms of astronomy, but none of the existing bases was selected with astronomy as the primary motivation. In this article, we try to systematically compare the merits of potential observatory sites.We include South Pole, Domes A, C, and F, and also Ridge B (running northeast from Dome A), and what we call "Ridge A" (running southwest from Dome A). Our analysis combines satellite data, published results, and atmospheric models, to compare the boundary layer, weather, aurorae, airglow, precipitable water vapor, thermal sky emission, surface temperature, and the free atmosphere, at each site. We find that all Antarctic sites are likely to be compromised for optical work by airglow and aurorae. Of the sites with existing bases, Dome A is easily the best overall; but we find that Ridge A offers an even better site. We also find that Dome F is a remarkably good site. Dome C is less good as a thermal infrared or terahertz site, but would be able to take advantage of a predicted "OH hole" over Antarctica during spring.Comment: Revised version. 16 pages, 21 figures (22 in first version). Submitted to PASP 16/05/09, accepted 13/07/09; published 20/08/0

Studying Algebraic Structures Using Prover9 and Mace4

In this chapter we present a case study, drawn from our research work, on the application of a fully automated theorem prover together with an automatic counter-example generator in the investigation of a class of algebraic structures. We will see that these tools, when combined with human insight and traditional algebraic methods, help us to explore the problem space quickly and effectively. The counter-example generator rapidly rules out many false conjectures, while the theorem prover is often much more efficient than a human being at verifying algebraic identities. The specific tools in our case study are Prover9 and Mace4; the algebraic structures are generalisations of Heyting algebras known as hoops. We will see how this approach helped us to discover new theorems and to find new or improved proofs of known results. We also make some suggestions for how one might deploy these tools to supplement a more conventional approach to teaching algebra.Comment: 21 pages, to appear as Chapter 5 in "Proof Technology in Mathematics Research and Teaching", Mathematics Education in the Digital Era 14, edited by G. Hanna et al. (eds.), published by Springe

Empirical Macromodels Under Test

This paper examines the employment effects of a revenue-neutral cut in the social security contribution rate in Germany by running policy simulations in four different types of macroeconomic models. Two models are based on time-series data where the labor market is modeled basically demand oriented, whereas the other two models are supply oriented computable general equilibrium models. While the predicted employment effects of the cut in the contribution rate are qualitatively similar across models three years after the cut, they differ considerably in magnitude. These differences can to a large extent be attributed to differences in the basic structure of the models. Of special importance is how prices and wages react in each model to the cut in the social security tax rate on one side, and the necessary increase of the indirect tax rate on the other side. The results, therefore, provide a guideline for assessing the outcome of policy simulations and for the further development of macroeconomic models suitable for this kind of experiments

Empirical Macromodels Under Test : A Comparative Simulation Study of the Employment Effects of a Revenue Neutral Cut in Social Security Contributions

In the paper we simulate a revenue-neutral cut in the social security contribution rate using five different types of macro- / microeconomic models, namely two models based on time-series data where the labour market is modelled basically demand oriented, two models of the class of computable equilibrium models which are supply oriented and finally a firm specific model for international tax burden comparisons. Our primary interest is in the employment effects the models predict due to the cut in the contribution rate. It turns out that qualitatively all models considered predict an increase in employment three years after the cut. But the employment effects differ considerably in magnitude, which follows immediately from the different behavioral assumptions underlying the different models

Optimization Heuristics for Determining Internal Rating Grading Scales

Basel II imposes regulatory capital on banks related to the default risk of their credit portfolio. Banks using an internal rating approach compute the regulatory capital from pooled probabilities of default. These pooled probabilities can be calculated by clustering credit borrowers into different buckets and computing the mean PD for each bucket. The clustering problem can become very complex when Basel II regulations and real-world constraints are taken into account. Search heuristics have already proven remarkable performance in tackling this problem. A Threshold Accepting algorithm is proposed, which exploits the inherent discrete nature of the clustering problem. This algorithm is found to outperform alternative methodologies already proposed in the literature, such as standard k-means and Differential Evolution. Besides considering several clustering objectives for a given number of buckets, we extend the analysis further by introducing new methods to determine the optimal number of buckets in which to cluster banks’ clients