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The recent blossoming of panel econometrics in general and panel time-series methods in particular has enabled many more research questions to be investigated than before. However, this development has not assuaged serious concerns over the lack of diagnostic testing procedures in panel econometrics, in particular vis-a-vis the prominence of such practices in the time-series domain: the recent introduction of residual cross-section independence tests aside, within mainstream panel empirics the combination of 'model', 'spefication' and 'testing' typically refers to the distinction between fixed and random effects, as opposed to a rigorous investigation of residual properties. In this paper we investigate these issues in the context of non-stationary panels with multifactor error structure, employing Monte Carlo simulations to investigate the distributions and rejection frequencies for standard time-series diagnostic procedures, including tests for residual autocorrelation, ARCH, normality, heteroskedasticity and functional form.

The 50th Lunar and Planetary Science Conference in The Woodlands, Texas, March 18–22, 2019., Introduction: The InSight mission landed on Mars on November, 26, 2018. This is the first planetary mission designed to deploy a complete geophysical observatory on Mars, following in the footsteps of the Apollo Lunar Surface Experiments Package (ALSEP) deployed on the Moon in the 1970s [1]. It will thus provide the first ground truth constraints on interior structure of the planet. The Seismic Experiment for Interior Structure (SEIS) [2] is one of three primary scientific investigations, the others being the Heat Flow and Physical Properties Package (HP3) [3] and the Rotation and Interior Structure Experiment (RISE) [4]. SEIS is supported by the APSS package, (Auxiliary Payload Sensor Suite, [5]), whose goal is to document environmental sources of seismic noise and signals, as well as an imaging system [6]. After a brief description of the SEIS experiment, we will describe the deployment process, including the evolution of the SEIS noise from the deck (with only SPs) to the ground (with both VBBs and SPs), first without and finally with wind shield. We will then discuss early scientific observations, providing first constrains on the Mars micro-seismic noise, atmospherically-generated seismic signals, and surface and subsurface elastic structure., The SEIS team acknowledge the supports of NASA, CNES, UKSA, SSO and DLR for the experiment funding and of the SEIS operation team [38] for delivering SEIS data.

Active UHF RFID systems are often used for identifying, tracking and locating objects. In the present publication a GPS- based localization system for saving fawns during pasture mowing was introduced and tested. Fawns were first found by a UAV before mowing began. They were then tagged with small active RFID transponders, and an appropriate reader was installed on a mowing machine. Conventional direction-of-arrival approaches require a large antenna array with multiple elements and a corresponding coherent receiver, which introduces a large degree of complexity on the reader-side. Instead, our transponders were equipped with a small GPS module, allowing a transponder to determine its own position on request from the reader. A UHF link was used to transmit the location to a machine- mounted reader, where a second GPS receiver was installed. Using information from this second position and a machine- mounted magnetometer for determining the relative north direction of a vehicle, relative distance, and angle between GPS receivers can be calculated. The accuracy and reliability of this novel method were tested under realistic operating conditions, considering critical factors such as the height of grass, the lying position of a fawn, humidity and geographical area.

In line with the wider macro productivity literature existing studies of agricultural production largely neglect technology heterogeneity, variable time-series properties and the potential for heterogeneous but correlated total factor productivity (TFP) across countries. Our empirical approach accommodates these difficulties and seeks to model the nature of the cross-section dependence in a sample of 128 countries (1961-2002). Our results suggest that agro-climatic environment drives similarity in TFP evolution across countries with heterogeneous production technology. This provides a possible explanation for the failure of technology transfer from advanced countries of the temperate 'North' to arid and/or equatorial developing countries of the 'South'.

Why do we observe such dramatic differences in labour productivity across countries in the macro data? This thesis argues that the growth empirics literature oversimplifies the complexity of the production process across countries and neglects data cross-section and time-series properties, leading to bias in the empirical estimates. Chapter 1 presents two general empirical frameworks for cross-country productivity analysis and demonstrates that they encompass the growth empirics literature of the past decades. We introduce our central argument of cross-country heterogeneity in the impact of observables and unobservables on output and develop this against the background of the pertinent time-series and cross-section properties of macro panel data. Chapter 2 uses data from 48 countries to estimate manufacturing production functions. We discuss standard and novel estimators, focusing on their treatment of parameter heterogeneity and data time-series and cross-section properties. We develop the Augmented Mean Group (AMG) estimator and show its similarity to the Pesaran (2006) Common Correlated Effects (CCE) approach. Our results confirm parameter heterogeneity across countries in the impact of observable inputs on output. We check the robustness of this finding and highlight its implications for empirical measures of TFP. Chapter 3 investigates the heterogeneity of agricultural production technology using data for 128 countries. We develop an extension to the CCE estimators which allows us to suggest that TFP is structured such that countries with similar agro-climatic environment are influenced by the same unobserved factors. This finding offers a possible explanation for the failure of technology-transfer from advanced countries of the temperate 'North' to developing countries of the arid/equatorial 'South'. Our Monte Carlo simulations in Chapter 4 investigate the performance of the AMG, CCE and standard (micro-)panel estimators. Failure to account for cross-section dependence is shown to result in serious distortion of the empirical estimates. We highlight scenarios in which the AMG is biased and offer simple remedies.

Objective: Meningiomas, mostly benign tumors that arise from the meninges, can be associated with headaches. The origin of these secondary headaches is unknown; the size of the tumor is not a crucial factor for the occurrence of headaches. We asked whether neuropeptides like substance P (SP) or calcitonin[for full text, please go to the a.m. URL], 64. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)

The cross-country growth literature commonly uses aggregate economy datasets such as the Penn World Table (PWT) to estimate homogeneous production function or convergence regression models. Against the background of a dual economy framework this paper investigates the potential bias arising when aggregate economy data instead of sectoral data is adopted in macro production function regressions. Using a unique World Bank dataset we estimate production functions in agriculture and manufacturing for a panel of 41 developing and developed countries (1963-1992). We employ novel empirical methods which can accommodate technology heterogeneity, variable nonstationarity and the breakdown of the standard crosssection independence assumption. We focus on technology heterogeneity across sectors and countries and the potential for biased estimates due to aggregation and empirical misspecification, relying on both theory and empirical evidence. Using data for a stylised aggregate economy made up of agricultural and manufacturing sectors we confirm substantial bias in the technology coefficients and thus any total factor productivity measures computed. Our empirical findings imply that sectoral structure is of crucial importance in the analysis of growth and development, thus strengthening the recent revival of research on structural change in development economics.

This paper analyses characteristics and determinants of the recent explosion of patent filings by Chinese firms both in China and the United States. We construct a firm-level dataset by matching USPTO and SIPO patents to Chinese manufacturing census data for the period 1999-2006. Using this integrated firm-level dataset, we show that the patent explosion is accounted for by a tiny, highly select group of Chinese companies in the information & communication technology (ICT) equipment industry. This handful of ICT companies accounts for nearly all Chinese USPTO patent filings as well as the vast majority of domestic SIPO patents despite there being a larger number of Chinese companies distributed across a wider range of industries that seeks patent protection domestically. Our empirical analysis further suggests that firms patenting in both US and China are considerably younger, larger and substantially more export-oriented than firms patenting exclusively in China. Our study contributes to the debate on China’s innovative prowess and its potential to transition from an imitator to an innovator economy.

This paper surveys the most popular parametric and semi-parametric estimators for Cobb-Douglas production functions arising from the econometric literature of the past two decades. We focus on the different approaches dealing with 'transmission bias' in firm-level studies, which arises from firms' reaction to unobservable productivity realisations when making input choices. The contribution of the paper is threefold: we provide applied economists with (i) an in-depth discussion of the estimation problem and the solutions suggested in the literature; (ii) a detailed empirical example using FAME data for UK high-tech firms, emphasising analytical tools to investigate data properties and the robustness of the empirical results; (iii) a powerful illustration of the impact of estimator choice on TFP estimates, using matched data on patents in 'TFP regressions'. Our discussion concludes that while from a theoretical point of view the different estimators are conceptually very similar, in practice, the choice of the preferred estimator is far from arbitrary and instead requires in-depth analysis of the data properties rather than blind belief in asymptotic consistency.

Despite the widely recognised importance of the manufacturing industry for successful development relatively few studies empirically investigate this sector in cross-country analysis. In this paper we attempt to fill this gap in the literature by investigating manufacturing production across a large number of developing and developed countries using an unbalanced panel of UNIDO data (1970-2002). Our empirical framework allows us not only a great deal of flexibility with regard to the production technology, but also enables us to model the complex network of endogeneities, the web of interdependencies not merely within but also across economies, which previously has been largely ignored in cross-country empirics. We apply a novel two-stage estimator which can account for these matters and check the robustness of our results against a range of alternative methods from the recent panel time series literature. Our results imply that differences in production technology, not just differences in TFP, are of crucial importance for understanding cross-country differences in labour productivity and their underlying causes. They furthermore imply that conventional interpretation of regression intercepts as TFP level estimates is no longer valid and we therefore provide an alternative methodology.

Introduction We examined the control of modifiable risk factors among a national sample of diabetic people with and without lower extremity disease (LED). Methods The sample from the 1999-2004 National Health and Nutrition Examination Survey consisted of 948 adults aged 40 years or older with diagnosed diabetes and who had been assessed for LED. LED was defined as peripheral arterial disease (ankle-brachial index

In this paper we investigate a ‘global’ production function for agriculture, using FAO data for 128 countries from 1961-2002. Our review of the empirical literature in this field highlights that existing cross-country studies largely neglect variable time-series properties, parameter heterogeneity and the potential for heterogeneous Total Factor Productivity (TFP) processes across countries. We motivate the case for technology heterogeneity in agricultural production and present statistical tests indicating nonstationarity and cross-section dependence in the data. Our empirical approach deals with these difficulties by adopting the Pesaran (2006) Common Correlated Effects estimators, which we extend by using alternative weight-matrices to model the nature of the cross-section dependence. We furthermore investigate returns to scale of production and production dynamics. Our results support the specification of a common factor model in intercountry production analysis, highlight the rejection of constant returns to scale in pooled models as an artefact of empirical misspecification and suggest that agro-climatic environment, rather than neighbourhood or distance, drives similarity in TFP evolution across countries. The latter finding provides a possible explanation for the observed failure of technology transfer from advanced countries of the temperate ‘North’ to arid and/or equatorial developing countries of the ‘South’.