Efficient closed-form estimation of large spatial autoregressions

Gupta, Abhimanyu

doi:10.1016/j.jeconom.2021.05.005

Cited by 6 publications

(4 citation statements)

References 48 publications

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“…k ) for some unknown smooth function g(•). The forth avenue is to develop a fast algorithm with theoretical justification that can implement MIR when n or d is large, such as the one-step estimate proposed by Gupta (2021). The fifth avenue is to develop a criterion to obtain the optimal γ for EBIC.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, Lee and Liu (2010), Elhorst, Lacombe and Piras (2012), Lee and Yu (2014), Kwok (2019), and Lam and Souza (2020) considered a higher-order SAR model that includes multiple weight matrices (i.e., W (t) s) along with their associated parameters. Gupta andRobinson (2015, 2018) further extended it by allowing the number of weight matrices to diverge. In general, the elements of weight matrix W (t) are functions of the geographic or economic distances among the n actors.…”

Section: Introductionmentioning

confidence: 99%

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Mutual Influence Regression Model

Fan¹,

Lan²,

Zou³

et al. 2024

STAT SINICA

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In this article, we propose the mutual influence regression model (MIR) to establish the relationship between the mutual influence matrix of actors and a set of similarity matrices induced by their associated attributes. This model is able to explain the heterogeneous structure of the mutual influence matrix by extending the commonly used spatial autoregressive model while allowing it to change with time. To facilitate making inferences with MIR, we establish parameter estimation, weight matrices selection and model testing. Specifically, we employ the quasi-maximum likelihood estimation method to estimate unknown regression coefficients, and demonstrate that the resulting estimator is asymptotically normal without imposing the normality assumption and while allowing the number of similarity matrices to diverge. In addition, an extended BIC-type criterion is introduced for selecting relevant matrices from the divergent number

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mutual Influence Regression Model

Fan¹,

Lan²,

Zou³

et al. 2024

STAT SINICA

View full text Add to dashboard Cite

show abstract

“…But a closed-form solution is not obtained. On the other hand, Gupta [53] proposes a Newton-step computational algorithm of QMLE for a large-parameter-size SAR model, which is free from the compactness assumptions. Under the normality assumption, it has the same asymptotic efficiency as MLE, but has a closed-form solution and is computationally simple.…”

Section: Estimation and Inferencementioning

confidence: 99%

A Survey of Spatial Unit Roots

Baltagi,

Shu

2024

Mathematics

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This paper conducts a brief survey of spatial unit roots within the context of spatial econometrics. We summarize important concepts and assumptions in this area and study the parameter space of the spatial autoregressive coefficient, which leads to the idea of spatial unit roots. Like the case in time series, the spatial unit roots lead to spurious regression because the system cannot achieve equilibrium. This phenomenon undermines the power of the usual Ordinary Least Squares (OLS) method, so various estimation methods such as Quasi-maximum Likelihood Estimate (QMLE), Two Stage Least Squares (2SLS), and Generalized Spatial Two Stage Least Squares (GS2SLS) are explored. This paper considers the assumptions needed to guarantee the identification and asymptotic properties of these methods. Because of the potential damage of spatial unit roots, we study some test procedures to detect them. Lastly, we offer insights into how to relax the compactness assumption to avoid spatial unit roots, as well as the relationship between spatial unit roots and other models, such as the Spatial Dynamic Panel Data (SDPD) model and Lévy–Brownian motion.

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“…Furthermore, the implication of the findings of this study is that because teacher solidity has a direct positive effect on the effectiveness of school management, to improve the effectiveness of school management, efforts are made to improve teacher solidity. Improving teacher solidity requires an increased sense of solidity [43] (cohesiveness and togetherness). Strengthening the meaning of the essence of humanity [44] spirituality that cannot be separated from man's need for God [45].…”

Section: Distribution Of Teacher Soliditymentioning

confidence: 99%

Teacher solidity in the digital age and its effect on the effectiveness of primary school management

Lubis

Amin²,

Alimni³

2023

IJERE

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<span lang="EN-US">Often an era affects a person’s lifestyle in attitude. The attitude of solidity in the digital age is questionable because of its role in the effectiveness of school management. Some other members still believe in contributing to its effectiveness. To overcome these confusions and doubts, research is needed to test whether solidity still plays a role in improving management effectiveness. The purpose of this study was to determine the effect of teacher solidity on the effectiveness of primary school management. Data were collected using quantitative methods. The data collection technique is to use questionnaires. The data were analyzed through Pearson product moments and t-tests. The results of the interpretation of research data showed that teacher solidity has a direct positive effect on the effectiveness of primary school management. The assessment of the results of the study emphasized that solidity cannot be ignored although it is still necessary to have the individual skills of teachers to face the digital age.</span>

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Efficient closed-form estimation of large spatial autoregressions

Cited by 6 publications

References 48 publications

Mutual Influence Regression Model

Mutual Influence Regression Model

A Survey of Spatial Unit Roots

Teacher solidity in the digital age and its effect on the effectiveness of primary school management

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