Non-Markov stochastic dynamics of real epidemic process of respiratory infections

The study of social networks and especially of the stochastic dynamics of the diseases spread in human population has recently attracted considerable attention in statistical physics. In this work we present a new statistical method of analyzing the spread of epidemic processes of grippe and acute respiratory track infections (ARTI) by means of the theory of discrete non-Markov stochastic processes. We use the results of our last theory (Phys. Rev. E 65, 046107 (2002)) to study statistical memory effects, long - range correlation and discreteness in real data series, describing the epidemic dynamics of human ARTI infections and grippe. We have carried out the comparative analysis of the data of the two infections (grippe and ARTI) in one of the industrial districts of Kazan, one of the largest cities of Russia. The experimental data are analyzed by the power spectra of the initial time correlation function and the memory functions of junior orders, the phase portraits of the four first dynamic variables, the three first points of the statistical non-Markov parameter and the locally averaged kinetic and relaxation parameters. The received results give an opportunity to provide strict quantitative description of the regular and stochastic components in epidemic dynamics of social networks taking into account their time discreteness and effects of statistical memory. They also allow to reveal the degree of randomness and predictability of the real epidemic process in the specific social network.Comment: 18 pages, 8figs, 1 table

International Migration and FDI: Can Migrant Networks Foster Investments toward Origin Countries?

With the growing trends of international migration, the literature looking at the economic impact of migrants has also expanded, focusing on both the perspectives of the host and the origin countries in regard to various aspects such as labor force growth, GDP growth, and poverty rates. In the specific literature investigating the impact of migrants on origin countries, FDI is a key factor that cannot be overlooked, as it can play an essential role in the economic development of origin countries. Studies in this area have hypothesized that migrants’ impact on FDI is positive, since the information about the origin countries that migrants can provide to potential investors outside of those countries could help reduce information asymmetries and facilitate FDI flows. Through a panel regression spanning 2000-2017, this paper estimates the above relationship, focusing on migrant networks in the US and US FDI to the migrants’ origin countries, using migration data from the American Community Surveys for 86 countries of origin. The estimation, which controls for a set of gravity specifications and FDI determinants, finds a significant migrant network effect on US FDI, which is stronger for highly educated migrants. JEL classification: F21; F22; O11; O1

Dynamic agricultural supply response under economic transformation

China has experienced dramatic economic transformation and is facing the challenge of ensuring steady agricultural growth. This study examines the crop sector by estimating the supply response for major crops in Henan province from 1998 to 2007. We use a Nerlovian adjustment adaptive expectation model. The estimation uses dynamic Generalized Method of Moments (GMM) panel estimation based on pooled data across 108 counties. We estimate acreage and yield response functions and derive the supply response elasticities. This research links supply response to exogenous factors (weather, irrigation, government policy, capital investment, and infrastructure) and endogenous factors (prices). The significant feature of the model specification used in the study is that it addresses the endogeneity problem by capturing different responses to own- and cross-prices. Empirical results illustrate that there is still great potential to increase crop production through improvement of investment priorities and proper government policy. We confirm that farmers respond to price by both reallocating land and more intensively applying non-land inputs to boost yield. Investment in rural infrastructure, human capacity, and technology are highlighted as major drivers for yield increase. Policy incentives such as taxes and subsidies prove to be effective in encouraging grain production.acreage and yield response, dynamic panel model, Generalized Method of Moments (GMM), supply elasticity,

Lee Carter mortality forecasting: application to the Italian population

In this paper we investigate the feasibility of using the Lee-Carter methodology to construct mortality forecasts for the Italian population. We fit the model to the matrix of Italian death rates for each gender from 1950 to 2000. A time-varying index of mortality is forecasted in an ARIMA framework and is used to generate projected life tables. In particular we focus on life expectancies at birth and, for the purpose of comparison, we introduce an alternative approach for forecasting life expectancies on a period basis. The resulting forecasts generated by the two methods are then compared

Why the poor in rural Malawi are where they are: An Analysis of the Spatial Determinants of the Local Prevalence of Poverty

"We examine the spatial determinants of the prevalence of poverty for small spatially defined populations in rural Malawi. Poverty prevalence was estimated using a small-area poverty estimation technique. A theoretical approach based on the risk chain conceptualization of household economic vulnerability guided our selection of a set of potential risk and coping strategies — the determinants of our model — that could be represented spatially. These were used in two analyses to develop global and local models, respectively. In our global model—a spatial error model — only eight of the more than two dozen determinants selected for analysis proved significant. In contrast, all of the determinants considered were significant in at least some of the local models of poverty prevalence. The local models were developed using geographically weighted regression. Moreover, these models provided strong evidence of the spatial nonstationarity of the relationship between poverty and its determinants. That is, in determining the level of poverty in rural communities, where one is located in Malawi matters. This result for poverty reduction efforts in rural Malawi implies that such efforts should be designed for and targeted at the district and subdistrict levels. A national, relatively inflexible approach to poverty reduction is unlikely to enjoy broad success." Authors' AbstractSpatial analysis (Statistics) ,Poverty mapping ,Spatial regression ,Poverty determinants ,

Deep Statistical Models with Application to Environmental Data

When analyzing environmental data, constructing a realistic statistical model is important, not only to fully characterize the physical phenomena, but also to provide valid and useful predictions. Gaussian process models are amongst the most popular tools used for this purpose. However, many assumptions are usually made when using Gaussian processes, such as stationarity of the covariance function. There are several approaches to construct nonstationary spatial and spatio-temporal Gaussian processes, including the deformation approach. In the deformation approach, the geographical domain is warped into a new domain, on which the Gaussian process is modeled to be stationary. One of the main challenges with this approach is how to construct a deformation function that is complicated enough to adequately capture the nonstationarity in the process, but simple enough to facilitate statistical inference and prediction. In this thesis, by using ideas from deep learning, we construct deformation functions that are compositions of simple warping units. In particular, deformation functions that are composed of aligning functions and warping functions are introduced to model nonstationary and asymmetric multivariate spatial processes, while spatial and temporal warping functions are used to model nonstationary spatio-temporal processes. Similarly to the traditional deformation approach, familiar stationary models are used on the warped domain. It is shown that this new approach to model nonstationarity is computationally efficient, and that it can lead to predictions that are superior to those from stationary models. We show the utility of these models on both simulated data and real-world environmental data: ocean temperatures and surface-ice elevation. The developed warped nonstationary processes can also be used for emulation. We show that a warped, gradient-enhanced Gaussian process surrogate model can be embedded in algorithms such as importance sampling and delayed-acceptance Markov chain Monte Carlo. Our surrogate models can provide more accurate emulation than other traditional surrogate models, and can help speed up Bayesian inference in problems with exponential-family likelihoods with intractable normalizing constants, for example when analyzing satellite images using the Potts model

Unraveling the relationship between firm size and economic development: The roles of embodied and disembodied technological progress

In the last decade, an increasing body of literature has studied the relation between economic development and the rate of independent entrepreneurship. For several developed countries, this relation seems to have changed from a negative relation into a positive one. However, the role of technology, and in particular the roles of embodied and disembodied technological progress, in shaping this relation has not yet been established. We estimate a model, based on Lucas (1978), able to disentangle the roles of both these types of technological progress in determining average firm size (a concept closely but inversely related to the rate of independent entrepreneurship) for 23 OECD countries over the period 1972-2008. Our estimations allow us to establish, for each country, the relative importance of embodied technological change, vis-�-vis disembodied technological change, in determining average firm size. Our results suggest that, notwithstanding the rise of independent entrepreneurship observed in many countries over the last few decades, economies of scale and scope (embodied technological change) continue to play an important role in many advanced economies.�