Autoregressive Stochastic Model Research Articles

Statistical Mobility of Multicellular Colonies of Flagellated Swimming Cells.

We study the stochastic hydrodynamics of colonies of flagellated swimming cells, typified by multicellular choanoflagellates, which can form both rosette and chainlike shapes. The objective is to link cell-scale dynamics to colony-scale dynamics for various colonial morphologies. Via autoregressive stochastic models for the cycle-averaged flagellar force dynamics and statistical models for demographic cell-to-cell variability in flagellar properties and placement, we derive effective transport properties of the colonies, including cell-to-cell variability. We provide the most quantitative detail on disclike geometries to model rosettes, but also present formulas for the dynamics of general planar colony morphologies, which includes planar chain-like configurations.

Monitoring the Impacts of Artificial Recharge Structures on Water Table at Ambedkar Nagar, Uttar Pradesh, India

To study the groundwater recharge through rainfall and artificial recharge structures in selected dry well in different locations of UP was experimented. The data was recorded (1998-2017) to execute the artificial recharge structure at the appropriate locations with best geological condition to enhance the recharge rate at least cost for control of declining groundwater level. Study reveals that the stochastic auto regressive lime series model is an effective tool for management of ground water resource at pre and post monsoon. The variation of pre and post monsoon ground water level is maximum as the physical soil characteristics including hydraulic conductivity may enhance the recharge rate at least cost.

Effects of future climate conditions on the zooplankton of a Mediterranean coastal lagoon

Coastal lagoons are important ecosystems that contribute to the production of seafood worldwide. The impact of climate change on these vulnerable ecosystems can be evaluated by studying the zooplankton community due to its sensitivity to changes in environmental conditions. In this study, the authors investigated the effect of future climate conditions on the zooplankton community of a Mediterranean coastal lagoon (Antinioti lagoon, Greece), by incorporating field measurements and projected future temperatures in a multivariate autoregressive (MAR) stochastic model. The findings indicated a strong relationship between future water temperatures in Antinioti lagoon and the abundance of specific zooplankton groups. Furthermore, the productivity of Antinioti lagoon will be positively influenced by global warming, and the future zooplankton community’s structure will be subjected to competitive interactions among Copepods, Others, and Mysidacea.

Semiparametric estimation of spatial autoregressive smooth-coefficient panel stochastic frontier models

This paper considers the estimation of a spatial autoregressive stochastic frontier model, where the production frontier coefficients, as well as the spatial parameter, are allowed to depend on a set of observed environmental factors. The inefficiency term is multiplicatively separable into a scaling function of either the same or totally different set of environmental factors and a standard half-normal random variable. A two-step semiparametric procedure is developed using a combination of local GMM and maximum likelihood approaches. We also derive the estimators of direct and indirect average partial effects and predictors of technical efficiency. We work out the asymptotic theory for the proposed second step estimator and propose a test of the relevance of the environmental factors. A special case of the model where the spatial parameter is a non-zero constant is also considered. The finite sample behavior of the proposed estimator and test are examined using Monte Carlo simulations. An empirical application to the Chinese chemical industry is included to illustrate the usefulness of our proposed models and methods.

Математическое моделирование рекомендательной системы и обработка данных телекоммуникационной компании с помощью моделей машинного обучения

The purpose of the study is to develop data modeling methods for projecting recommender algorithms using doubly stochastic autoregressive models of random processes and checking their adequacy by applying machine learning algorithms to cluster users in a simulated data set and predict probabilities of interest. Research methods. The article discusses the methods used in the construction of recommender systems. At the same time, the problem of modeling user behavior using a doubly stochastic model is considered. This model is proposed for generating artificial data. The doubly stochastic model allows generating non-stationary processes, thus creating users with different probabilistic properties in different groups of objects of interest. After that, artificially created users (and their activity) are clustered based on a modified K-means algorithm. The main modification is the need for automatic pre-estimation of the number of clusters, and not its choice by a person. Next, the behavior of representatives of each user group for new events is modeled. Based on the generated information and training data, the problem of predictiing and ranking the services offered is solved. At the same time, at the first stage, the use of regression models is sufficient to assign users to a group and form offers for this user. Results of the study. On the training data in 2 clusters, high determination indices were achieved, which indicates approximately 90% of the explained variance when using the proposed doubly stochastic model. Particular attention is paid to the work of modern recommender systems on the example of the Disco system developed by Yandex. In addition, pre-processing and preliminary analysis of data from the real sector was performed, namely, the data of a telecommunications company are being studied. For the purpose of issuing relevant proposals for communication services, a test recommender system has been developed. Conclusion. Thus, the main results of the work include a mathematical model that simulates the reaction of users to various services, as well as a logistic regression model used to predict the probability of a user's interest in a new service. Based on predicted probabilities, it is not difficult to rank new proposals. Approbation on the synthesized data showed the high efficiency of the model.

Nonlinear autoregressive stochastic frontier model with dynamic technical inefficiency in panel data

In this paper, we focused on the new stochastic frontier model with the nonlinear autoregressive structure in panel data. In classic finance models, uncorrelated errors are considered, which are frequently not met in empirical situations. In our stochastic frontier model, the composite error consists of two components, the statistical error and technical inefficiency, whereas the technical inefficiency is assumed to be autocorrelated. A semiparametric method is suggested to estimate nonlinear autoregressive function by two steps procedure as parametric Taylor series expansion and nonparametric adjustment factor. For the model parameters estimation, the Expectation-Maximization approach is applied and the performance of the estimation is checked by Monte Carlo simulation. Finally, we investigate the applicability of new model using a real data set.

Spatial autoregressive stochastic frontier model with application to Indonesia’s aquaculture

Stochastic frontier analysis (SFA) is one of the popular methods for measuring efficiency and productivity. SFA generally assumes that decision making units are independent. However, this assumption may be inappropriate since decision making units are probable interdependent depending on their spatial proximities. This paper aims to incorporate spatial lag into standard SFA model. The performance of spatial autoregressive stochastic frontier model (SAR-SFA) is compared to standard SFA using simulation and real data. Simulation study is undertaken using various degrees of spatial autocorrelation. Furthermore, SAR-SFA is applied to estimate technical efficiency of aquaculture in Indonesia. Simulation results showed that the SAR-SFA generally produces better performance than the standard SFA. If spatial dependencies are significant, then the estimation of production frontier from standard SFA could be misleading. It affects the distribution of technical efficiency and its ranking. Moran’s I test indicated that Indonesia’s aquaculture has significant positive spatial autocorrelation. If standard SFA is applied to estimate technical efficiency of Indonesia’s aquaculture, then the resulted technical efficiency is implausible. This issue is well handled by the SAR-SFA model. Moreover, SAR-SFA model serves procedure to estimate efficiency spillovers.

The scale and structure of government financial investment in traditional medicine based on optimal efficiency: evidence from public traditional Chinese medicine hospitals (PTHs) of Henan province, China

BackgroundTraditional medicine has been widely used to address relatively common illnesses. In this regard, Chinese government has been continually topping up its investments on public Traditional Chinese Medicine hospitals (PTHs) in recent years. This study aimed to assess the optimal scales and structure of the investments in Henan province by analyzing the contribution of Government Financial Investment (GFI) to the efficiency and revenue growth of PTHs as well as recommending proper investment strategies for implementation to policy-makers.MethodsThis study was a panel data study, conducted in Henan Province, China. By collecting 143 PTHs’ operational data from the year 2005 to 2017, Barro Economic Growth (BEG) model, Stochastic Frontier Analysis (SFA) and Vector Autoregressive (VAR) model were used to assess the efficiency and PTHs revenue.ResultsThe study observed the positive contribution of GFI to PTHs’ revenue growth (average MPG = 2.84), indicating that the GFI had not reached the required optimal level of “Barro Law”. In order to maximize the input-output efficiency, the scales of GFI on Grade III, Grade II A, Grade II B PTHs need to be increased by − 5.96, 4.88 and 11.51%, respectively. The third year following the first investment may be a more essential period for conducting an effective GFI evaluation in Henan Province.ConclusionsGFI on PTHs usually has a long-term impact on PTHs. Governments can adjust its GFI policy so as to maximize the input-output efficiency.

An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be [Formula: see text] and [Formula: see text], respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively.

State-Dependent Autoregressive Models: Properties, Estimation and Forecasting

This paper considers a class of C-convolution-based Markov models in which we assume that the error term is dependent on the first lagged state variable and the dependence structure is modeled by a copula function. Such models appear suitable for studying nonlinearity in time series. We show that the process is equivalent to a stochastic autoregressive coefficient model depending on the first lagged value of the process itself. We call such a model state-dependent first-order autoregressive process, (SDAR). We introduce some assumptions under which this class of models is strictly stationary and geometrically ergodic and the quasi-maximum likelihood estimator of the parameters is consistent and asymptotically normal with not very restrictive assumptions about the distribution of the error term . Some particular specifications of the model are analyzed and applied to model nonlinear time series provided by the weekly realized volatility extracted from returns of some European financial indexes. The comparison of forecasting accuracy is made considering an alternative approach provided by a two-regime SETAR model.

A white noise test under weak conditions

We consider the well-known white noise test that is available in the literature. Its null distribution is known to be asymptotically normal under different sets of conditions for processes with finite 8th moment. We show that for some specific models, the normality continues to hold under the finiteness of only the 4th moment. This includes various GARCH models, stochastic autoregressive volatility model and autoregressive conditional duration model. Under the alternate hypothesis, for specific models such as infinite order moving average, non-linear moving average and bilinear models with finite 4th moment, we show that with suitable centering and scaling, which is of a different order of magnitude compared to the null case, the test statistic is asymptotically normal.

Asymptotic properties of a spatial autoregressive stochastic frontier model

This paper considers asymptotic properties of a spatial autoregressive stochastic frontier model. Relying on the asymptotic theory for nonlinear spatial NED processes, we prove the consistency and asymptotic distribution of the maximum likelihood estimator under regularity conditions. When inefficiency exists, all parameter estimators have the $$\sqrt{n}$$ -rate of convergence and are asymptotically normal. However, when there is no inefficiency, only some parameter estimators have the $$\sqrt{n}$$ -rate of convergence, and the rest have slower convergence rates. We also investigate a corrected two stage least squares estimator that is computationally simple, and derive the asymptotic distributions of the score and likelihood ratio test statistics that test for the existence of inefficiency.

Spatial Stochastic Frontier Model with Endogenous Spillovers

We propose a spatial autoregressive stochastic frontier model, which allows for endogenous spillovers (i.e., the spatial weighting matrix is not independent of the two-sided error term). The parameters of the model are estimated via the maximum likelihood estimation method. Monte Carlo simulations illustrate that our model performs well in finite samples. As an example, we employed our methodology to the US banks and found evidence for endogenous spillovers. The empirical example suggested potential biases in the parameter estimates when endogeneity of spillovers is ignored.

Double Stochastic Filtration of Spatially Inhomogeneous Images

Abstract—The representation of spatially inhomogeneous images using double stochastic autoregressive models is considered. The possibility of synthesizing semicausal double stochastic image filtering algorithms based on such models is shown. Variants for reducing the computational costs required to implement double stochastic filters using cascades of moving windows are considered. A comparative analysis of the proposed algorithms with well-known counterparts is carried out, confirming the practical possibility of using double stochastic filters for processing real two-dimensional images.

A spatial stochastic frontier model with endogenous frontier and environmental variables

We propose a spatial autoregressive stochastic frontier model, which allows for the endogeneity in both the frontier and environmental variables (i.e., endogeneity due to correlation of inefficiency term and two-sided error term). The model parameters are estimated using a single-stage control function approach. Monte Carlo simulations show that our proposed model and approach perform well in finite samples. We employed our methodology to the Chinese chemicals firm data and found evidence for both spatial effects and endogeneity.

A Spatial Autoregressive Combination Model Separating Spatial Dependence in the Frontier and in Technical Inefficiency

We identify the attributes and limitations of spatial autoregressive stochastic frontier models, spatial inefficiency error stochastic frontier models, and straightforward integrated models. We also propose a separable spatial autoregressive combination stochastic frontier model that overcomes the limitations. The proposed model’s main features are as follows: (a) spatial dependence in the frontier and in technical inefficiency are separately identified; (b) a negative spatial autocorrelation in technical inefficiency is permitted; (c) technical inefficiency follows a truncated-normal distribution.

A spatial autoregressive stochastic frontier model for panel data incorporating a model of technical inefficiency

By integrating Battese and Coelli’s (1995) model and the spatial autoregressive model (SAR), a spatial autoregressive stochastic frontier model for panel data is developed. The main feature of this frontier model is a spatial lag term of explained variables and the joint structure of a production possibility frontier with a model of technical inefficiency. The model addresses both spatial dependence and heteroskedastic technical inefficiency. This study applies maximum likelihood methods considering the endogenous spatial lag term. The proposed model nests several existing models. Further, an empirical analysis using data on the Japanese manufacturing industry is conducted and the existing models are tested against the proposed model, which is found to be statistically supported. The findings suggest that estimates in the existing spatial and non-spatial models may exhibit bias because of lack of determinants of technical inefficiency, as well as a spatial lag. This bias also affects the technical efficiency score and its ranking.

A Spatial Autoregressive Stochastic Frontier Model for Panel Data Incorporating a Model of Technical Inefficiency

By integrating Battese and Coelli’s (1995) model and the spatial autoregressive model (SAR), a spatial autoregressive stochastic frontier model for panel data is developed. The main feature of this frontier model is a spatial lag term of explained variables and the joint structure of a production possibility frontier with a model of technical inefficiency. The model addresses both spatial dependence and heteroskedastic technical inefficiency. This study applies maximum likelihood methods considering the endogenous spatial lag term. The proposed model nests several existing models. Further, an empirical analysis using data on the Japanese manufacturing industry is conducted and the existing models are tested against the proposed model, which is found to be statistically supported. The findings suggest that estimates in the existing spatial and non-spatial models may exhibit bias because of lack of determinants of technical inefficiency, as well as a spatial lag. This bias also affects the technical efficiency score and its ranking.

Dependence dynamics among exchange rates, commodities and the Brazilian stock market using the R-vine SCAR model

The objective of this paper is to assess the dependence dynamics among Brazilian real exchange rates, commodity prices and the Brazilian stock market using a regular vine copula combined with the stochastic autoregressive copula model. The results suggest that the Brazilian financial markets are strongly dependent on the US dollar (USD), Petrobras stock prices and oil prices, and the dependence dynamics of the variables that comprise these markets are volatile and persistent over time.

Estimating efficiency in a spatial autoregressive stochastic frontier model

The spatial autoregressive stochastic frontier model of Glass et al.(2016) is based on distributional assumptions on two-sided and one-sided error terms. After estimating the model parameters, the efficiency estimates need to be corrected due to the presence of spatial autoregressive term in their model. Glass et al.(2016) estimate the corrected efficiencies by employing ideas from a distribution-free method on the efficiency estimation, which may be sensitive to outliers. We propose an alternative way to correct efficiency estimates that is in line with the distribution-based methods.