Some Current Issues in the Statistical Analysis of Spillovers

Spillover phenomena are usually statistically estimated on the basis of regional and temporal panel data. In this paper we review and investigate exploratory and confirmatory statistical panel data techniques. We illustrate the methods by calculations in the stetting of the well known Research and Development Spillover study by Coe and Helpman (1995). It will be demonstrated that alternative estimation techniques that are well compatible with the data can lead to opposite conclusions.Panel data; fixed effects; random coefficients; DOLS; R&D spillover

Model-based Geostatistics

Abstracts not available for BookReview

R&D Spillovers: A Non-Spatial and a Spatial Examination

In recent years there were many debates and different opinions whether R&D spillover effects exist or not. In 1995 Coe and Helpman published a study about this phenomenon, based on a panel dataset, that supports the position that such R&D spillover effects are existent. However, this survey was criticized and many different suggestions for improvement came from the scientific community. Some of them were selected and analysed and finally led to a new model. And even though this new model is well compatible with the data, it leads to different conclusions, namely that there does not exist an R&D spillover effect. These different results were the motivation to run a spatial analysis, which can be done by considering the countries as regions and using an adequate spatial link matrix. The used methods from the field of spatial econometrics are described briefly and quite general, and finally the results from the spatial models (the ones which correspond to the non-spatial ones) are compared with the results from the non-spatial analysis. The preferred model supports the position that R&D spillover effects exist

CAMP@FLASH: an end-station for imaging, electron- and ion-spectroscopy, and pump–probe experiments at the FLASH free-electron laser

The non-monochromatic beamline BL1 at the FLASH free-electron laser facility at DESY was upgraded with new transport and focusing optics, and a new permanent end-station, CAMP, was installed. This multi-purpose instrument is optimized for electron- and ion-spectroscopy, imaging and pump–probe experiments at free-electron lasers. It can be equipped with various electron- and ion-spectrometers, along with large-area single-photon-counting pnCCD X-ray detectors, thus enabling a wide range of experiments from atomic, molecular, and cluster physics to material and energy science, chemistry and biology. Here, an overview of the layout, the beam transport and focusing capabilities, and the experimental possibilities of this new end-station are presented, as well as results from its commissioning

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Spatial Methods in Econometrics. An Application to R&D Spillovers.

In this paper I will give a brief and general overview of the characteristics of spatial data, why it is useful to use such data and how to use the information included in spatial data. The first question to be answered is: how to detect spatial dependency and spatial autocorrelation in data? Such effects can for instance be found by calculating Moran's I, which is a measure for spatial autocorrelation. The Moran's I is also the basis for a test for spatial autocorrelation (Moran's test). Once we found some spatial structure we can use special models and estimation techniques. There are two famous spatial processes, the SAR- (spatial autoregressive) and the SMA- (spatial moving average process) process, which are used to model spatial effects. For estimation of spatial regression models there are mainly two different possibilities, the first one is called spatial filtering, where the spatial effect is filtered out and standard techniques are used, the second one is spatial two stage least square estimation. Finally there are some results of a spatial analysis of R&D spillovers data (for a panel dataset with 22 countries and 20 years) shown. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematic

Treatment of Far-Off Objects in Moran's I Test

Spatial dependency is commonly measured and tested with Moran's I statistic. The question to be answered is, whether far-off objects affect this statistic and influence the test. Far-off objects are observations that are far apart from all other objects in the dataset, i.e. they do not have spatial links to other design points. In the paper different possibilities of treating such objects are discussed, and their influence on Moran's I and the corresponding spatial autocorrelation test is analysed. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematic

Spatial methods in econometrics

This thesis deals with the appropriate handling of spatial data in general, and in particular in the framework of economic sciences. An overview of well known methods from the field of spatial statistics and spatial econometrics is given. Furthermore a special class of spatial objects is described, namely objects that are that far apart from all other observations in the dataset, that they are not connected to them anymore. Different treatments of such objects are suggested and their influence on the Moran's I test for spatial autocorrelation is analyzed in more detail. After this theoretical part some adequate spatial methods are applied to the well-known problem of R&D spillovers. The corresponding dataset is not obviously spatial, nevertheless spatial methods can be used. The spatial contiguity matrix is based on an economic distance measure instead of the commonly used geographic distances. Finally, optimal design theory and spatial analysis are combined via a new criterion. This criterion was developed to be able to take a potential spatial dependency of the data points into account. The aim is to find the best design points that show the same spatial dependence structure as the true population. (author's abstract

Some current issues in the statistical analysis of spillovers

Spillover phenomena are usually statistically estimated on the basis of regional and temporal panel data. In this paper we review and investigate exploratory and confirmatory statistical panel data techniques. We illustrate the methods by calculations in the stetting of the well known Research and Development Spillover study by Coe and Helpman (1995). It will be demonstrated that alternative estimation techniques that are well compatible with the data can lead to opposite conclusions. (author's abstract)Series: Working Papers Series "Growth and Employment in Europe: Sustainability and Competitiveness