Dynamic modelling of Nonresponse in Business Surveys

It is well-known that nonresponse affects the results of surveys and can even cause bias due to selectivities if it cannot be regarded as missing at random. In contrast to household surveys, response behaviour in business surveys has been examined rarely in the literature. This paper is one of the first which analyses a large business survey on micro data level for unit nonresponse. The data base is the Ifo Business Tendency Survey, which was established in 1949 and has more than 5,000 responding firms each month. The panel structure allows to use statistical modelling including time-varying effects to check for the existence of a panel fatigue. The results show that there are huge differences in business characteristics such as size or subsector and that nonresponse is more frequent in economically good times

Dynamic modelling of Nonresponse in Business Surveys

It is well-known that nonresponse affects the results of surveys and can even cause bias due to selectivities if it cannot be regarded as missing at random. In contrast to household surveys, response behaviour in business surveys has been examined rarely in the literature. This paper is one of the first which analyses a large business survey on micro data level for unit nonresponse. The data base is the Ifo Business Tendency Survey, which was established in 1949 and has more than 5,000 responding firms each month. The panel structure allows to use statistical modelling including time-varying effects to check for the existence of a panel fatigue. The results show that there are huge differences in business characteristics such as size or subsector and that nonresponse is more frequent in economically good times

Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey

A widespread method for now- and forecasting economic macro level parameters such as GDP growth rates are survey-based indicators which contain early information in contrast to official data. But surveys are commonly affected by nonresponding units which can produce biases if these missing values can not be regarded as missing at random. As many papers examined the effect of nonresponse in individual or household surveys, only less is known in the case of business surveys. So, literature leaves a gap on this issue. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications on the macro level. Finally, we show that the bias is minimal and ignorable

Dynamic Modelling of Nonresponse in Business Surveys

It is well-known that nonresponse affects the results of surveys and can even causebias due to selectivities if it cannot be regarded as missing at random. In contrast tohousehold surveys, response behaviour in business surveys has been examined rarely inthe literature. This paper is one of the first which analyses a large business survey onmicro data level for unit nonresponse. The data base is the Ifo Business TendencySurvey, which was established in 1949 and has more than 5,000 responding firms eachmonth. The panel structure allows to use statistical modelling including time-varyingeffects to check for the existence of a panel fatigue. The results show that there are hugedifferences in business characteristics such as size or sub-sector and that nonresponse ismore frequent in economically good times.Business survey, logistic regression, nonresponse, panel survey, varyingcoefficient model.

A functional renormalization group approach to electronic structure calculations for systems without translational symmetry

A formalism for electronic-structure calculations is presented that is based on the functional renormalization group (FRG). The traditional FRG has been formulated for systems that exhibit a translational symmetry with an associated Fermi surface, which can provide the organization principle for the renormalization group (RG) procedure. We here advance an alternative formulation, where the RG-flow is organized in the energy-domain rather than in k-space. This has the advantage that it can also be applied to inhomogeneous matter lacking a band-structure, such as disordered metals or molecules. The energy-domain FRG ({\epsilon}FRG) presented here accounts for Fermi-liquid corrections to quasi-particle energies and particle-hole excitations. It goes beyond the state of the art GW-BSE, because in {\epsilon}FRG the Bethe-Salpeter equation (BSE) is solved in a self-consistent manner. An efficient implementation of the approach that has been tested against exact diagonalization calculations and calculations based on the density matrix renormalization group is presented. Similar to the conventional FRG, also the {\epsilon}FRG is able to signalize the vicinity of an instability of the Fermi-liquid fixed point via runaway flow of the corresponding interaction vertex. Embarking upon this fact, in an application of {\epsilon}FRG to the spinless disordered Hubbard model we calculate its phase-boundary in the plane spanned by the interaction and disorder strength. Finally, an extension of the approach to finite temperatures and spin S = 1/2 is also given.Comment: 25 pages, 14 figure

Ranking Economists on the Basis of Many Indicators: An Alternative Approach Using RePEc Data

In socio-economic sciences the RePEc network (Research Papers in Economics) has become an essential source for the gathering and the spread of both existing and new economic research. Furthermore, it is currently the largest bibliometric database in economic sciences containing 33 different indicators for more than 30,000 economists. Based on this bibliographic information RePEc calculates well-known rankings for authors and academic institutions. We provide some cautionary remarks concerning the interpretation of some provided bibliometric measures in RePEc. Moreover, we show how individual and aggregated rankings can be biased due to the employed ranking methodology. In order to select key in-dicators describing and assessing research performance of scientist, we propose to apply principal component analysis in this data-rich environment. This approach allows us to assign weights to each indicator prior to aggregation. We illustrate the approach by providing a new overall ranking of economists based on RePEc data.RePEc, ranking aggregation, principal components analysis, economics profession

RePEc – An Independent Platform for Measuring Output in Economics

Bewertung; Netzwerk; Ranking-Verfahren; Deutschland

Ranking Economists and Economic Institutions Using RePEc: Some Remarks

In socio-economic sciences the RePEc network (Research Papers in Economics) has become an essential source both for the spread of existing and new economic research. Furthermore the calculation of rankings for authors and academic institutions play a central role. We provide some cautionary remarks on the ranking methodology employed by RePEc and show how the aggregated rankings maybe biased. Furthermore we offer anew ranking approach, based on standardization of scores, which allows interpersonal comparisons and is less sensitive to outliers. We illustrate our new approach with a large data set provided by RePEc based on 24,500 authors.Rankings, RePEc, ranking aggregation, standardization