Inference for VARs identified with sign restrictions

There is a fast growing literature that partially identifies structural vector autoregressions (SVARs) by imposing sign restrictions on the responses of a subset of the endogenous variables to a particular structural shock (sign-restricted SVARs). To date, the methods that have been used are only justified from a Bayesian perspective. This paper develops methods of constructing error bands for impulse response functions of sign-restricted SVARs that are valid from a frequentist perspective. The authors also provide a comparison of frequentist and Bayesian error bands in the context of an empirical application — the former can be twice as wide as the latter.Vector autoregression ; Econometric models

A Study of Downward Nominal Wage Rigidity in Korea

This paper examines downward nominal wage rigidity in Korea using aggregate and individual-level data. We find that the degree of downward nominal wage rigidity differs depending on the data sources used. Results from the aggregate data indicate that, on average, wages have been flexible. By contrast, evidence from the micro data suggests that nominal wages are downwardly rigid most of the time. We also find that downward nominal wage rigidity can differ across industries at both industry and individual levels. At the individual level, wage rigidity is greater in the service than in the manufacturing sector, even though the latter exhibits smaller volatility in its rate of wage growth

Inference for VARs Identified with Sign Restrictions

There is a fast growing literature that partially identifies structural vector autoregressions (SVARs) by imposing sign restrictions on the responses of a subset of the endogenous variables to a particular structural shock (sign-restricted SVARs). To date, the methods that have been used are only justified from a Bayesian perspective. This paper develops methods of constructing error bands for impulse response functions of sign-restricted SVARs that are valid from a frequentist perspective. We also provide a comparison of frequentist and Bayesian error bands in the context of an empirical application - the former can be twice as wide as the latter.

Acclimatization across space and time in the effects of temperature on mortality: a time-series analysis

Background: Climate change has increased the days of unseasonal temperature. Although many studies have examined the association between temperature and mortality, few have examined the timing of exposure where whether this association varies depending on the exposure month even at the same temperature. Therefore, we investigated monthly differences in the effects of temperature on mortality in a study comprising a wide range of weather and years, and we also investigated heterogeneity among regions. Methods: We analyzed 38,005,616 deaths from 148 cities in the U.S. from 1973 through 2006. We fit city specific Poisson regressions to examine the effect of temperature on mortality separately for each month of the year, using penalized splines. We used cluster analysis to group cities with similar weather patterns, and combined results across cities within clusters using meta-smoothing. Results: There was substantial variation in the effects of the same temperature by month. Heat effects were larger in the spring and early summer and cold effects were larger in late fall. In addition, heat effects were larger in clusters where high temperatures were less common, and vice versa for cold effects. Conclusions: The effects of a given temperature on mortality vary spatially and temporally based on how unusual it is for that time and location. This suggests changes in variability of temperature may be more important for health as climate changes than changes of mean temperature. More emphasis should be placed on warnings targeted to early heat/cold temperature for the season or month rather than focusing only on the extremes. Electronic supplementary material The online version of this article (doi:10.1186/1476-069X-13-89) contains supplementary material, which is available to authorized users

Distinct sites in E-cadherin regulate different steps in Drosophila tracheal tube fusion.

We have investigated how E-cadherin controls the elaboration of adherens junction associated cytoskeletal structures crucial for assembling tubular networks. During Drosophila development, tracheal branches are joined at branch tips through lumens that traverse doughnut-shaped fusion cells. Fusion cells form E-cadherin contacts associated with a track that contains F-actin, microtubules, and Shot, a plakin that binds F-actin and microtubules. Live imaging reveals that fusion occurs as the fusion cell apical surfaces meet after invaginating along the track. Initial track assembly requires E-cadherin binding to b- catenin. Surprisingly, E-cadherin also controls track maturation via a juxtamembrane site in the cytoplasmic domain. Fusion cells expressing an E-cadherin mutant in this site form incomplete tracks that contain F-actin and Shot, but lack microtubules. These results indicate that Ecadherin controls track initiation and maturation using distinct, evolutionarily conserved signals to F-actin and microtubules, and employs Shot to promote adherens junction-associated cytoskeletal assembly.The authors thank K. Miller, T. Uemura, M. Peifer and S. Myster, A. Pacquelet, P. Rorth, S. Rogers, R. Vale, G. Beitel, U. Tepass and E. Giniger for providing fly stocks and reagents. Hanwei Cao provided excellent technical assistance. D. Greenstein,V. Bennett and anonymous reviewers provided helpful comments on the manuscript. One reviewer prompted us to carry out the co-expression experiment. We also thank A. Reynolds for helpful discussions. This research was supported by NIH RO1 GM62101 to P.A.K

Class Day Programme, May (1892)

https://red.mnstate.edu/commencement/1002/thumbnail.jp

Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011

Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas

A Novel Multifunctional Nanowire Platform for Highly Efficient Isolation and Analysis of Circulating Tumor-Specific Markers

Circulating tumor-specific markers are crucial to understand the molecular and cellular processes underlying cancer, and to develop therapeutic strategies for the treatment of the disease in clinical applications. Many approaches to isolate and analyze these markers have been reported. Here, we propose a straightforward method for highly efficient capture and release of exosomes and circulating tumor cells (CTCs) in a single platform with well-ordered three-dimensional (3D) architecture that is constructed using a simple electrochemical method. Conductive polypyrrole nanowires (Ppy NWs) are conjugated with monoclonal antibodies that specifically recognize marker proteins on the surface of exosomes or CTCs. In response to electrical- or glutathione (GSH)-mediated stimulation, the captured exosomes or cells can be finely controlled for retrieval from the NW platform. A surface having nano-topographic structures allows the specific recognition and capture of small-sized exosome-like vesicles (30–100 nm) by promoting topographical interactions, while physically blocking larger vesicles (i.e., microvesicles, 100–1,000 nm). In addition, vertically aligned features greatly improve cell capture efficiency after modification with desired high-binding affinity biomolecules. Notably, exosomes and CTCs can be sequentially isolated from cancer patients' blood samples using a single NW platform via modulating electrochemical and chemical cues, which clearly exhibits great potential for the diagnosis of various cancer types and for downstream analysis due to its facile, effective, and low-cost performance

Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach

Background A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. Methods We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. Results We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April – September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October–March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. Conclusions We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change