The Spatial Effect of Aging Population on Innovation in Entrepreneurship--Based on Empirical Analysis of Western China

This study researches the impact of an aging population on Innovation in Entrepreneurship (IE), and applying fixed effects models (FE), mediated effects models and spatial lag regression model (SAR) to panel data of Western China (excluding Tibet) from 2004 to 2019.The results showed that an aging population and IE inverted significantly U-curve, and human capital plays a significant partial mediation between the two. A theoretical perspective based on the First Law of Geography, in the western China, aging population and IE are both positive spatial correlation, and both show the characteristics of "High-High" spatial agglomeration. Under the spatial model, aging population and IE also inverted U-curve.

[W]hat Lies Beneath: Using Latent Networks to Improve Spatial Predictions

Spatial interdependencies commonly drive the spread of violence in civil conflict. To address such interdependence, scholars often use spatial lags to model the diffusion of violence, but this requires an explicit operationalization of the connectivity matrices that represent the spread of conflict. Unfortunately, in many cases, there are multiple competing processes that facilitate the spread of violence making it difficult to identify the true data-generating process. We show how a network-driven methodology can allow us to account for the spread of violence, even in the cases where we cannot directly measure the factors that drive diffusion. To do so, we estimate a latent connectivity matrix that captures a variety of possible diffusion patterns. We use this procedure to study intrastate conflict in eight conflict-prone countries and show how our framework enables substantially better predictive performance than canonical spatial-lag measures. We also investigate the circumstances under which canonical spatial lags suffice and those under which a latent network approach is beneficial.

Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis

COVID-19 has spread throughout the world since the virus was discovered in 2019. Thus, this study aimed to identify the global transmission trend of the COVID-19 from the perspective of the spatial correlation and spatial lag. The research used primary data collected of daily increases in the amount of COVID-19 in 14 countries, confirmed diagnosis, recovered numbers, and deaths. Findings of the Moran index showed that the propagation of infection was aggregated between 9 May and 21 May based on the composite spatial weight matrix. The results from the Lagrange multiplier test indicated the COVID-19 patients can infect others with a lag.

The Impact of Inter-City Traffic Restriction on COVID-19 Transmission from Spatial Econometric Perspective

The aim of this paper is to conduct a spatial correlation study of virus transmission in the Hubei province, China. The number of confirmed COVID-19 cases released by the National Health and Construction Commission, the traffic flow data provided by Baidu migration, and the current situation of Wuhan intercity traffic were collected. The Moran’s I test shows that there is a positive spatial correlation between the 17 cities in the Hubei province. The result of Moran’s I test also shows that four different policies to restrict inter-city traffic can be issued for the four types of cities. The ordinary least squares regression, spatial lag model, spatial error model, and spatial lag error model were built. Based on the analysis of the spatial lag error model, whose goodness of fit is the highest among the four models, it can be concluded that the speed of COVID-19 spread within a certain region is not only related to the current infection itself but also associated with the scale of the infection in the surrounding area. Thus, the spill-over effect of the COVID-19 is also presented. This paper bridges inter-city traffic and spatial economics, provides a theoretical contribution, and verifies the necessity of a lockdown from an empirical point of view.

Spatial Models in Econometric Research

Since the late 1990s, spatial models have become a growing addition to econometric research. They are characterized by attention paid to the location of observations (i.e., ordered spatial locations) and the interaction among them. Specifically, spatial models formally express spatial interaction by including variables observed at other locations into the regression specification. This can take different forms, mostly based on an averaging of values at neighboring locations through a so-called spatially lagged variable, or spatial lag. The spatial lag can be applied to the dependent variable, to explanatory variables, and/or to the error terms. This yields a range of specifications for cross-sectional dependence, as well as for static and dynamic spatial panels. A critical element in the spatially lagged variable is the definition of neighbor relations in a so-called spatial weights matrix. Historically, the spatial weights matrix has been taken to be given and exogenous, but this has evolved into research focused on estimating the weights from the data and on accounting for potential endogeneity in the weights. Due to the uneven spacing of observations and the complex way in which asymptotic properties are obtained, results from time series analysis are not applicable, and specialized laws of large numbers and central limit theorems need to be developed. This requirement has yielded an active body of research into the asymptotics of spatial models.

Spatial Modelling for Cholera Incidence Rates in Morogoro Municipality, Tanzania

Spatial modelling was conducted to examine community factors associated with cholera incidence rates in Morogoro Municipality. The study employed both secondary (cholera cases) and primary (geographic coordinates of community risk factors) data. Spatial lag model was applied in examining association between the variables. All wards of Morogoro Municipality were considered in the study to capture their variations because cholera cases have a tendency to be clustered. Results indicated that market density, distance to the market and distance to the dumpster are significant factors associated with cholera incidence rates in the wards (p < 0.05). Geographically weighted Poisson model was used to show the variations of those factors between the wards in Municipality. A statistically significant positive association of cholera incidence rates; and market density was only found in Mazimbu ward (p < 0.05) and distance from the community to the dumpster was found in Kihonda, Kingolwira, Bigwa, Kichangani, Kilakala and Boma wards (p < 0.001) and some wards at the centre of the municipality which are Mji Mkuu and Kingo (p < 0.05). A statistically significant negative association of cholera incidence rates and distance from the centre of the community to the market was found in Kihonda, Kingolwira and Kichangani (p < 0.001) and Bigwa wards (p < 0.05). Therefore, measures taken to control and prevent cholera disease should base on the variations of the risk factors found in the Municipal wards. Keywords: Cholera incidence rate; Spatial lag regression model; Community risk factor; Geographically weighted Poisson model

Pemodelan Regresi Spatial Autoregressive Fixed Effect Model Data Panel Pada Tingkat Kemiskinan Di Provinsi Jawa Barat

Kemiskinan merupakan salah satu permasalahan global yang terjadi di semua negara berkembang termasuk negara Indonesia. Pengentasan kemiskinanan menjadi prioritas utama dalam tujuan pembangunan berkelanjutan atau Sustainable Development Goals (SDGs), dimana pengentasan kemiskinan menjadi tujuan pertama yang ingin dicapai. Kemiskinana juga menjadi salahsatu permasalahan yang menjadi isu salahsatu isu strategis RPJMD tahun 2018-2023 yang menjadi pusat perhatian pemerintah khususnya di Provinsi Jawa Barat yang merupakan provinsi dengan jumlah penduduk terbanyak di Indonesia. Pada penelitian ini akan dilakukan analisis faktor-faktor yang mempengaruhi kemiskinan di Provinsi Jawa Barat. Data kemiskinan tiap-tiap kabupaten/kota memiliki tingkat yang berbeda -beda, sehingga terdapat kemungkinan adanya efek spasial dalam data. Maka pemodelan harus mengakomodasi aspek spasial kemudian terbatasnya variabel yang dilibatkan karena keterbatasan data tentunya menimbulkan oomited variabel atau varaiebel yang relevan namun tidak ada dalam model maka digunakan pendekatan fixed effect model dalam mengatasi masalah tersebut. Sehingga pemodelan yang digunakan adalah Spatial Autoregressive Fixed Effact model ( SAR-FEM). Hasil penelitian ini didapatkan bahwa Variabel Tingkat pengangguran terbuka, Indkes pembangunan Manusia dan persentase penduduk berpengaruh signifikan terhadap Tingginya tingkat kemiskina di Provinsi Jawa Barat. Model spatial lag fixed effect yang terbentuk dapat menjelaskan besarnya keragaman dari Tingkat Kemiskinan yang dapat dijelaskan oleh variabel prediktor sebesar 98.88% sedangkan 1.116% sisanya dijelaskan oleh variabel lain yang tidak dimasukkan kedalam model.

Information transparency and pricing strategy in the Scottish housing market

Purpose Information transparency is crucially important in price setting in real estate, particularly when information asymmetry is concerned. This paper aims to examine how a change in government policy in relation to information disclosure and transparency impacts residential real estate price discovery. Specially, this paper investigates how real estate traders determined asking prices in the context of the Scottish housing market before and after the implementation of the Home Report, which aimed to prevent artificially low asking prices. Design/methodology/approach This paper uses spatial lag hedonic pricing models to empirically observe how residential asking prices are determined by property sellers in response to a change in government policy that is designed to enhance market transparency. It uses over 79,000 transaction data of the Aberdeen residential market for the period of Q2 1998 to Q2 2013 to test the models. Findings The empirical findings provide some novel insights in relation to the price determination within the residential market in Scotland. The spatial lag models suggest that spatial autocorrelation in property prices has increased since the Home Report came into effect, indicating that property sellers have become more prone to infer asking prices based on prior sales of dwellings in close vicinity. The once-common practice of setting artificially low asking prices seems to have dwindled to a certain extent statistically. Originality/value The importance of understanding the relationship between information transparency and property price determination has gathered momentum over the past decade. Although spatial hedonic techniques have been extensively used to study the impact of various property- and neighbourhood-specific attributes on residential real estate market in general, surprisingly little is known about the empirical relationship between spatial autocorrelation in real estate prices and information transparency.

IMPLEMENTASI DATA PANEL SPASIAL TERHADAP TINGKAT PRODUK DOMESTIK REGIONAL BRUTO DI PROVINSI BALI

Gross Regional Domestic Product (GRDP) is an economic indicator to see the economic movements of a region during a certain period, whether based on current and constant price. Economic activities in a region use the GRDP calculation based on current prices by industrial base year 2010. In 2019, Bali's economic growth increased by , exceeding national economic growth of . Using spatial panel data in analysis consists of common effect model, fixed individual effect model, fixed time effect model, random effect model, and spatial lag fixed effect model. The best model to modeling GRDP Bali Province is spatial lag fixed effect which has a difference in constant values ??at any time, with of 99.41 percent, the remaining is explained by other variables not examined