To investigate the Spatial error correlation in Panel Regression models, various statistical hypothesizes and testings have been proposed. This paper, within introduction to Spatial Panel data Regression model, existence of Spatial error correlation and random effects is investigated by a joint Lagrange Multiplier test, which simultaneously tests their existence. For this purpose, joint Lagrange Multiplier test statistic and its asymptotic distribution is introduced. A simulation study is performed for considering the size and power of the test this test for joint hypothesizes. Then the application of this test is shown with investigating Spatial errors correlation and random effects in data of agricultural product exports of ECO member states. Finally, discussion and conclusion are given.

Previous studies and observations of the earthquake have shown that frame filled with concrete 3d Panel wall, can have adverse effects on the seismic behavior of structures. On the other hand, yielding dampers that used with steel braces, have a high capacity to absorb and depreciating energy from the earthquake. In this paper, targets are reducing damage to the infills and moment frame in during of the earthquake, reduce the horizontal and vertical irregularities in structure. To rech these targets, Three experimentally models with half scale were constructed and tested on the shaking table under 7 scale accelerated. Also, for investigate the Effective of parameters in the seismic behavior of moment frames, using Abaqus software, finite element analysis was performed after verification of model behavior with experimentally results. Experimentally results showed that use the yielding connection between wall and frame, reduces the lateral displacement and plastic strain in the structure. Numerical analyzes showed that by adding the yielding connection to the frame and infill wall, The frame has linear behavior In high intensity earthquake, and not be created plastic hinges in the members of the frame. The input energy is amortized only in the connections. So the number and geometric specification of these connections can affect the lateral hardness and lateral strength of the structure.

BACKGROUND AND OBJECTIVES: This study is investigated on endogenous variables inclusive of green growth by developing the concept of inclusive green growth in Indonesia. The objective of the current study was to describe the conditions of inclusive green development in each province in Indonesia, which is due to the unavailability of data describing the conditions of inclusive green development. METHODS: This study used time series data from 2011-2019, and cross section data of 34 provinces, which were analyzed using Panel data Regression research methods. The novelty of this study is the use of environmental quality variables to replace environmental degradation in calculating the composite variable of inclusive green growth. The determinants of inclusive green growth used in this study were inclusive human development, regional independence, infrastructure, crime, industrialization and natural disasters. FINDINGS: The important study findings were inclusive human development, regional financial performance, infrastructure and natural disasters have a significant positive effect on inclusive green growth in Indonesia. On the other hand, industrialization had a significant negative effect on inclusive green growth in Indonesia, while crime did not have a significant effect on inclusive green growth in Indonesia. Simultaneously, inclusive human development, regional independence, infrastructure, crime, industrialization and natural disasters had a significant impact on inclusive green growth in Indonesia. CONCLUSION: The second hypothesis in this study proved to be accepted. Meanwhile, the first hypothesis is not entirely accepted. Therefore, it is highly recommended for the provincial government in Indonesia to intervene on the variables of inclusive human development, regional financial performance, infrastructure, industrialization and natural disasters to increase inclusive green growth. Meanwhile, for future researchers, it is recommended to find other variables that contribute in achieving inclusive green growth.

Introduction: The COVID-19 epidemic is considered a geographical phenomenon, and its impact on decision-making and daily life is significant. Geographic information systems (GIS) and Spatial techniques play crucial roles in analyzing the spread of COVID-19 globally. Studies using Spatial analysis have highlighted the importance of social and health variables in infection and mortality rates, despite existing uncertainties about the effects of meteorological variables. Given Iran's climatic diversity, it is valuable to identify the key Spatial factors influencing COVID-19. Therefore, this study aims to model and determine the factors affecting the COVID-19 epidemic based on available data.Materials and Methods: This study investigated the factors influencing the distribution of COVID-19 infection rates using global and local Spatial Regression methods. Seventy-three cities were selected, with data on COVID-19 infections available from March 10 to June 20, 2019. The factors considered were altitude, population density, average age, the ratio of the population over 55 years to the total population, and meteorological parameters, including humidity, temperature, pressure, and wind speed. Their relationships with the disease were analyzed using Spatial statistics methods. Stepwise Regression identified population density, air pressure, average age, and wind speed as significant predictors, and the occurrence of the disease was modeled using the Ordinary Least Squares (OLS) technique. Due to the unstable relationship between the independent and dependent variables, the Geographically Weighted Regression (GWR) technique was used. Principal Component Analysis (PCA) and SPSS software were employed to address Spatial variability and multicollinearity.Results and Discussion: The results showed that the OLS model was statistically significant, with variance values explained by the model being non-random. However, the explanatory variables had an inconsistent relationship with the dependent variable in both geographic and data spaces. The residual distribution deviated somewhat from normal, indicating model instability. Thus, the GWR technique was applied for modeling. PCA addressed multicollinearity (due to a cluster pattern in meteorological variables), reducing meteorological factors to one component, explaining nearly 70% of the variance. The model improved by consolidating the average age and the ratio of the population over 55 into one factor. Subsequently, population density, meteorological factors, and age demographics were utilized as predictive variables in the GWR model. A 10% increase in the adjusted R-squared of the GWR model (63%) demonstrated its relative improvement over the OLS model. Moran's Spatial autocorrelation test indicated that, while the cluster pattern of residuals was less pronounced in the GWR model than in the OLS model, it remained significant at the 99% confidence level. Hot spot analysis at the 95% confidence level identified the western parts of Kurdistan province and the northern and western parts of Khuzestan province as hot spots (areas of significant underestimation), and the eastern parts of Hamadan province and the northern parts of Bushehr province as cold spots (areas of significant overestimation). Thus, at least one variable affecting disease occurrence was not considered. Other potential factors, such as cultural, health, and genetic variables, were not included due to unavailability or measurement difficulties.Conclusion: This study highlights the importance of demographic and environmental factors in COVID-19 infection rates and provides a foundation for further research in the study area.

Identifying the impact of consumption of different inputs on agricultural production helps to provide the right information to the policymaker. This study aimed at estimating the production function of two basic products (irrigated and rainfed wheat, and rice) using the three-dimensional Spatial Panel pattern (product, province and time). The required information was collected for the 30 provinces of Iran in the period from 2006 to 2018; and according to the selection criteria of the production functions, the translog function was selected as the superior function. Due to Spatial effects and diagnostic tests, the Spatial correlation model was evaluated as the optimal model. The study results showed that the elasticity of cultivated area, consumption of pesticides, tractors and combine harvesters to the production of irrigated wheat, rainfed wheat and rice products was positive and the elasticity of labor, consumption of seeds and fertilizers to the production of these products was negative; in addition, the elasticity of production inputs did not change significantly over time, and the trend of impacts of the production inputs on the production of the studied products was almost constant. Due to the surplus of labor in the production of these three products, the withdrawal of labor from production in this sector should be on the agenda and more investment should be made in investment factors such as combine harvesters and tractors to increase production so that the country can achieve self-sufficiency. . .