Abstract
The article discusses a hierarchical and spatial approach to assessing the spatial dependence of data. The advantages and disadvantages of each approach and the potential for their combination are determined on the basis of a literature review.The results of the OLS, SAR, SEM, HLM, HSAR models are compared. Despite an interesting set of data (2285 municipalities in the context of 85 constituent entities of the Russian Federation), the emphasis in the work is not on identifying the relationship between the dependent variable and factors, but on comparing spatial effects that can be identified within each of the models under consideration. The calculations showed a significant influence on the dependent variable of the following factors: the share of the average number of employees in the resident population, the volume of investments in fixed assets per capita and the share of the urban population. This result was shown by all the constructed models. In the context of models, the identified spatial effects have their own characteristics. The inclusion of spatial matrices is possible at the upper (for example, the subject of the Russian Federation), lower (for example, the municipal level), or both levels simultaneously. In hierarchical models, spatial relationships are additionally taken into account by grouping the objects of observation on a territorial basis. Calculations have shown that the spatial lag is not significant in all models. Spatial error is significant at the municipal level in the SEM model and at the regional level in the HLM and HSAR models. Additionally, hierarchical models showed a significant influence of the region on the municipalities variation. In general, the results of modeling and evaluating modelsquality are ambiguous. Despite this, the potential for expanding spatial econometrics on the basis of a combination of spatial and hierarchical (multilevel) modeling approaches is noted, and the need to select a model for each case is substantiated, taking into account the significance of spatial and hierarchical effects
Highlights
Более того, в рамках одной страны могут быть выделены различные пространственные паттерны (Коломак, 2010; Tian et al, 2010; Demidova, 2015)
the potential for their combination are determined on the basis of a literature review
The calculations showed a significant influence on the dependent variable of the following factors
Summary
Экономическая активность территорий: сравнительный анализ способов оценки пространственных эффектов. При используемом наборе данных (2285 муниципальных образований в разрезе 85 субъектов РФ) акцент в работе сделан не на выявление связи между зависимой переменной и факторами, а на сравнение пространственных эффектов, которые могут быть выделены в рамках каждой из рассматриваемых моделей. Пространственная ошибка значима на уровне муниципальных образований в модели SEM и на уровне регионов в моделях HLM, HSAR. Дополнительно иерархические модели показали значимое влияние особенностей региона на вариацию муниципальных образований по рассматриваемому показателю. Несмотря на это отмечается потенциал расширения пространственной эконометрики на основе сочетания подходов пространственного и многоуровневого моделирования, а также обосновывается необходимость подбора модели для каждого случая с учетом значимости пространственных и иерархических эффектов. Для цитирования: Тимирьянова В.М., Зимин А.Ф., Юсупов К.Н. Экономическая активность территорий: сравнительный анализ способов оценки пространственных эффектов // Пространственная экономика.
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