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EN
Purpose: Due to the visible disproportions, the problem of innovation is increasingly often perceived regionally. These inequalities result from the concentration of knowledge, resources and the amount of expenditure on innovation in a few regions. The aim of the paper is to study the spatial dependence between Polish voivodships in terms of expenditure on innovative activities in enterprises in the service sector. Design/methodology/approach: For the selected variable conditioning the innovative activity of the enterprise, a classical econometric model will be built and the necessity to include the spatial factor in the modeling process will be verified. For this purpose, two spatial models will be considered: the spatial error model and spatial lag model. Findings: During the study, the hypothesis on the legitimacy of introducing spatial relationships to the econometric model describing the shaping of the amount of expenditure on innovative activities in enterprises in the service sector in Polish voivodeships was verified. The hypothesis has been verified positively – there are spatial relationships between the examined objects. Research limitations/implications: The need to take into account the spatial factor in the econometric model results in taking into account spatial estimation methods. The research used selected spatial models. Due to the limitations resulting from the availability of source data, the analysis was conducted only for voivodships in selected years. The analysis should be further deepened, e.g., by even more precise identification of the models and taking into account other neighborhood matrices - only the first-order neighborhood matrix was included in the study. Practical implications: Modeling the phenomenon of innovation. Social implications: An essential condition influencing the innovative activity of enterprises is their environment. It is the regional factors that largely influence innovation and faster development of enterprises. Originality/value: Introducing spatial relationships to the econometric model of outlays on innovative activities of enterprises in the service sector.
PL
Proces starzenia się społeczeństwa jest zjawiskiem wieloaspektowym, na które ma wpływ m.in. demograficzna i przestrzenna charakterystyka badanych obszarów. Zastosowanie analizy przestrzennej do badania tego procesu pozwala na ustalenie istniejących relacji między badanymi regionami w odniesieniu do tego zjawiska. W artykule podjęto próbę przestrzennego modelowania zjawiska starzenia się ludności. Dla wybranej zmiennej charakteryzującej starzejącą się populację, tj. liczby osób w wieku poprodukcyjnym, stworzono klasyczny model ekonometryczny oraz zweryfikowano konieczność uwzględnienia czynnika przestrzennego w modelowaniu badanego zjawiska. Jako zmienne objaśniające modelu wzięto pod uwagę zmienne demograficzne oraz zmienne o charakterze ekonomiczno-społecznym. W tym celu rozpatrzono dwa modele przestrzenne: błędu przestrzennego i opóźnienia przestrzennego.
EN
The aging process is a multifaceted phenomenon affected by, inter alia, the demographic and spatial nature of individuals. The application of spatial analysis to investigate this process will demonstrate the existing relationships between the studied regions with respect to this phenomenon. The article attempts to model the phenomenon of spatial aging of the population. For selected variables characterizing an aging population, i.e. the total number of post-working age people – classic econometric models were constructed and the necessity of including the spatial factor in the process of modelling was verified. The demographic variables and variables of an economic and social nature were chosen as explaining variables of the model. For this purpose, two spatial models will be considered: the spatial error model and spatial lag model. All calculations and maps will be made in the statistical program R CRAN and Microsoft Excel.
EN
Purpose: The presence of a long-term memory component in a time series means that even very distant observations exert a certain influence on subsequent implementations of the process. Generally, this relationship is not particularly strong, but it does exist. Interpreting this phenomenon in the context of financial time series, one can come to the conclusion that information that has affected the market some time ago may still be important for the current quotation. The article is devoted to checking the existence of a long-term memory in the financial time series and assessing the investment risk of these series based on the long-term memory parameter. Design/methodology/approach: In order to study the phenomenon of long-term memory in financial time series, the local Whittle estimator was used, while the investment risk assessment was carried out using the fractal dimension, β-coefficient and standard deviation of rates of return. Findings: In the first part of the study the author indicated time series which were characterized by the phenomenon of long-term memory. Then, on the basis of selected measures, the risk of investment was estimated and shares with the least risk were indicated. Research limitations/implications: The results obtained for selected measures showed discrepancies between the shares with the highest and the lowest level of investment risk. Although the results obtained do not give a definite answer which risk measure is more effective, they encourage the use of other measures related to the phenomenon of long-term memory. Practical implications: Application in portfolio analysis. Originality/value: The use of the long-term memory parameter to assess the investment risk of shares.
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