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

Frequency stability of maser oscillators operated with cavity Q

The short term frequency stability of masers equipped with an external feedback loop to increase the cavity quality factor was studied. The frequency stability of a hydrogen and a rubidium maser were measured and compared with theoretical evaluation. It is shown that the frequency stability passes through an optimum when the cavity Q is varied. Long term fluctuations are discussed and the optimum mid term frequency stability achievably by small size active and passive H-masers is considered

Estimates of the impact of static and dynamic knowledge spillovers on regional factor productivity

We develop an empirical approach to examine static and dynamic knowledge externalities in the context of a regional total factor productivity relationship. Static externalities refer to current period scale or industry-size effects which have been labeled localization externalities or region-size effects known as agglomeration externalities. Dynamic externalities refer to the relationship between accumulated or prior period knowledge and current levels of innovation, where past learning-by-doing makes innovation positively related to cumulative production over time. Our empirical specification allows for the presence of both static and dynamic externalities, and provides a way to assess the relative magnitude of spillovers associated with spillovers from these two types of knowledge externalities. The magnitude of own-region impacts and other-region (spillovers) can be assessed using scalar summary measures of the own- and cross-partial derivatives from the model. We find evidence supporting the presence of dynamic externalities as well as static, and our estimates suggest that dynamic externalities may have a larger magnitude of impact than static externalities.

Laughlin's wave functions, Coulomb gases and expansions of the discriminant

In the context of the fractional quantum Hall effect, we investigate Laughlin's celebrated ansatz for the groud state wave function at fractional filling of the lowest Landau level. Interpreting its normalization in terms of a one component plasma, we find the effect of an additional quadrupolar field on the free energy, and derive estimates for the thermodynamically equivalent spherical plasma. In a second part, we present various methods for expanding the wave function in terms of Slater determinants, and obtain sum rules for the coefficients. We also address the apparently simpler question of counting the number of such Slater states using the theory of integral polytopes.Comment: 97 pages, using harvmac (with big option recommended) and epsf, 7 figures available upon request, Saclay preprint Spht 93/12

Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing

Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structures can enable important clinical applications. We present a framework for tracking tree structures comprising of elongated branches using probabilistic state-space models and Bayesian smoothing. Unlike most existing methods that proceed with sequential tracking of branches, we present an exploratory method, that is less sensitive to local anomalies in the data due to acquisition noise and/or interfering structures. The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector. Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother, which provides Gaussian density estimates of branch states at each tracking step. We select likely branch seed points automatically based on the response of the blob detection and track from all such seed points using the RTS smoother. We use covariance of the marginal posterior density estimated for each branch to discriminate false positive and true positive branches. The method is evaluated on 3D chest CT scans to track airways. We show that the presented method results in additional branches compared to a baseline method based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in Biomedical Image Analysis. MICCAI 2017. Quebec Cit

Spatial Econometric Issues for Bio-Economic and Land-Use Modeling

We survey the literature on spatial bio-economic and land-use modelling and review thematic developments. Unobserved site-specific heterogeneity is common in almost all of the surveyed works. Heterogeneity appears also to be a significant catalyst engendering significant methodological innovation. To better equip prototypes to adequately incorporate heterogeneity, we consider a smorgasbord of extensions. We highlight some problems arising with their application; provide Bayesian solutions to some; and conjecture solutions for others.spatial econometrics, bio-economic and land-use modelling, Bayesian solution, Land Economics/Use,

Towards sketch-based exploration of terrain : a feasibility study

CISRG discussion paper ; 1