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Accurate forecasting of high-resolution particulate matter 2.5 (PM2.5) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approach to handle this problem. The first stage is a multivariate spatial time series (MSTS) model, used to generate forecasts for the sampled spatial units and to impute missing observations. The MSTS model utilizes the similarities between the temporal patterns of the time series of the spatial units to impute the missing data across space. The second stage is the high-resolution prediction model, which generates predictions that cover the entire study domain. The second stage faces the big N problem giving rise to complex memory and computational problems. As a solution to the big N problem, we propose a Gaussian Markov random field (GMRF) for innovations with the Matérn covariance matrix obtained from the corresponding Gaussian field (GF) matrix by means of the stochastic partial differential equation (SPDE) method and the finite element method (FEM). For inference, we propose Bayesian statistics and integrated nested Laplace approximation (INLA) in the R-INLA package. The above approach is demonstrated using daily data collected from 13 PM2.5 monitoring stations in Jakarta Province, Indonesia, for 1 January–31 December 2022. The first stage of the model generates PM2.5 forecasts for the 13 monitoring stations for the period 1–31 January 2023, imputing missing data by means of the MSTS model. To capture temporal trends in the PM2.5 concentrations, the model applies a first-order autoregressive process and a seasonal process. The second stage involves creating a high-resolution map for the period 1–31 January 2023, for sampled and non-sampled spatiotemporal units. It uses the MSTS-generated PM2.5 predictions for the sampled spatiotemporal units and observations of the covariate's altitude, population density, and rainfall for sampled and non-samples spatiotemporal units. For the spatially correlated random effects, we apply a first-order random walk process. The validation of out-of-sample forecasts indicates a strong model fit with low mean squared error (0.001), mean absolute error (0.037), and mean absolute percentage error (0.041), and a high R² value (0.855). The analysis reveals that altitude and precipitation negatively impact PM2.5 concentrations, while population density has a positive effect. Specifically, a one-meter increase in altitude is linked to a 7.8% decrease in PM2.5, while a one-person increase in population density leads to a 7.0% rise in PM2.5. Additionally, a one-millimeter increase in rainfall corresponds to a 3.9% decrease in PM2.5. The paper makes a valuable contribution to the field of forecasting high-resolution PM2.5 levels, which is essential for providing detailed, accurate information for public health policy. The approach presents a new and innovative method for addressing the problem of missing data and high-resolution forecasting. [ABSTRACT FROM AUTHOR]

This article addresses modeling oscillation in continuous time. It criticizes Steele and Ferrer's article "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011), particularly the approximate estimation procedure applied. This procedure is the latent version of the local linear approximation procedure based on Boker (2001) and Boker, Neale, and Rausch (2004). It furthermore presents two exact alternative estimation procedures, one using filter techniques and the other using structural equation modeling.

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1467-6435.2009.00435.x Byline: Henk Folmer (1) Abstract: SUMMARY This note analyzes the impacts of the fragmentation of economics into different schools of thought and of social science into the sub-disciplines economics, sociology and psychology. Fragmentation is based on the assumption that it is possible to split the set of an individual's behavioural motives into separable and disjoint subsets. However, this assumption runs counter to the insights in psychology. Moreover, even if splitting up were possible, the different subsets of motives finally need to be checked on consistency and weighted so as to obtain a comprehensive description and explanation. Another serious drawback is that specification of empirical models on the basis of one school of thought or one sub-discipline leads to omitted variables bias and hence biased estimators and tests. Finally, fragmentation may lead to a 'pick-and-mix package', whereby policy-makers and politicians feel free to use what suits them. The social rationality model together with the methodological approach prevalent in modern sociology is presented as a framework for integrating the schools of thought in economics and the social science sub-disciplines. Author Affiliation: (1)Faculty of Spatial Sciences, University of Groningen, P.O. Box 72, NL 9700 AB Groningen

We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it possible to obtain a closer correspondence between theory and empirics, to explicitly account for measurement errors, and to reduce multicollinearity. We extend the standard SEM maximum likelihood estimator to allow for spatial dependence and propose easily accessible SEM software like LISREL 8 and Mx. We present an illustration based on Anselin's Columbus, OH, crime data set. Furthermore, we combine the spatial lag model with the latent multiple-indicators-multiple-causes model and discuss estimation of this latent spatial lag model. We present an illustration based on the Anselin crime data set again., Introduction The purpose of this article is to introduce the class of structural equations models (SEMs) and corresponding estimation procedures into an econometric spatial dependence framework. SEM allows the presence [...]

This paper analyzes the impacts of privatization in Ghana on the basis of a survey of 300 workers in privatized and state-owned enterprises. The findings indicate a significant positive relationship between privatization and job satisfaction. Whereas monthly wage is an important determinant of job satisfaction in state-owned enterprises, education and the availability of training opportunities are strong determinants in privatized enterprises. The study also shows that there exists a variety of instruments to implement a privatization program smoothly. Key words--privatization, job satisfaction, employment model, state-owned enterprise, Ghana

Increasing demand for firewood in Harare and its effects on deforestation is discussed.