Conditional inference with a complex sampling: exact computations and Monte Carlo estimations

In survey statistics, the usual technique for estimating a population total consists in summing appropriately weighted variable values for the units in the sample. Different weighting systems exit: sampling weights, GREG weights or calibration weights for example. In this article, we propose to use the inverse of conditional inclusion probabilities as weighting system. We study examples where an auxiliary information enables to perform an a posteriori stratification of the population. We show that, in these cases, exact computations of the conditional weights are possible. When the auxiliary information consists in the knowledge of a quantitative variable for all the units of the population, then we show that the conditional weights can be estimated via Monte-Carlo simulations. This method is applied to outlier and strata-Jumper adjustments

A comment on finite temperature correlations in integrable QFT

I discuss and extend the recent proposal of Leclair and Mussardo for finite temperature correlation functions in integrable QFTs. I give further justification for its validity in the case of one point functions of conserved quantities. I also argue that the proposal is not correct for two (and higher) point functions, and give some counterexamples to justify that claim.Comment: 11 page

Determination of the position angle of stellar spin axes

Measuring the stellar position angle provides valuable information on binary stellar formation or stellar spin axis evolution. We aim to develop a method for determining the absolute stellar position angle using spectro-astrometric analysis of high resolution long-slit spectra. The method has been designed in particular for slowly rotating stars. We investigate its applicability to existing dispersive long-slit spectrographs, identified here by their plate scale, and the size of the resulting stellar sample. The stellar rotation induces a tilt in the stellar lines whose angle depends on the stellar position angle and the orientation of the slit. We developed a rotation model to calculate and reproduce the effects of stellar rotation on unreduced high resolution stellar spectra. Then we retrieved the tilt amplitude using a spectro-astrometric extraction of the position of the photocentre of the spectrum. Finally we present two methods for analysing the position spectrum using either direct measurement of the tilt or a cross-correlation analysis. For stars with large apparent diameter and using a spectrograph with a small plate scale, we show that it is possible to determine the stellar position angle directly within 10deg with a signal-to-noise ratio of the order of 6. Under less favourable conditions, i.e. larger plate scale or smaller stellar diameter, the cross-correlation method yields comparable results. We show that with the currently existing instruments, it is possible to determine the stellar position angle of at least 50 stars precisely, mostly K-type giants with apparent diameter down to 5 milliarcseconds. If we consider errors of around 10deg still acceptable, we may include stars with apparent diameter down to 2 mas in the sample that then comprises also some main sequence stars.Comment: 10 pages, 9 figures, A&A (in press

Discussion: Applications and Innovations in Spatial Econometrics

These articles provide a discussion of studies presented in a session on spatial econometrics, focusing on the ability of spatial regression models to quantify the magnitude of spatial spillover impacts. Both articles presented argue that a proper modeling of spatial spillovers is required to truly understand the phenomena under study, in one case the impact of climate change on land values (or crop yields) and in the second the role of regional industry composition on regional business establishment growth.lagged variables, panel data, spatial spillovers, Community/Rural/Urban Development, Environmental Economics and Policy, Resource /Energy Economics and Policy, C33, C51,

Network dependence in multi-indexed data on international trade flows

Faced with the problem that conventional multidimensional fixed effects models only focus on unobserved heterogeneity, but ignore any potential cross-sectional dependence due to network interactions, we introduce a model of trade flows between countries over time that allows for network dependence in flows, based on sociocultural connectivity structures. We show that conventional multidimensional fixed effects model specifications exhibit cross-sectional dependence between countries that should be modeled to avoid simultaneity bias. Given that the source of network interaction is unknown, we propose a panel gravity model that examines multiplenetwork interaction structures, using Bayesian model probabilities to determine those most consistent with the sample data. This is accomplished with the use of computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of the log-marginal likelihood that can be used for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency, and spatial proximity of countries.Series: Working Papers in Regional Scienc

Conventional versus network dependence panel data gravity model specifications

Past focus in the panel gravity literature has been on multidimensional fixed effects specifications in an effort to accommodate heterogeneity. After introducing conventional multidimensional fixed effects, we find evidence of cross-sectional dependence in flows. We propose a simultaneous dependence gravity model that allows for network dependence in flows, along with computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of log-marginal likelihood useful for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency and spatial proximity of countries.Series: Working Papers in Regional Scienc

Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows

We introduce a regression-based gravity model for commodity flows between 35 regions in Austria. We incorporate information regarding the highway network into the spatial connectivity structure of the spatial autoregressive econometric model. We find that our approach produces improved model fit and higher likelihood values. The model accounts for spatial dependence in the origin-destination flows by introducing a spatial connectivity matrix that allows for three types of spatial dependence in the origins to destinations flows. We modify this origin-destination connectivity structure that was introduced by LeSage and Pace (2005) to include information regarding the presence or absence of a major highway/train corridor that passes through the regions. Empirical estimates indicate that the strongest spatial autoregressive effects arise when both origin and destination regions have neighboring regions located on the highway network. Our approach provides a formal spatial econometric methodology that can easily incorporate network connectivity information in spatial autoregressive models.Commodity flows, Spatial autoregression, Bayesian, Maximum likelihood, Spatial connectivity of origin-destination flows