Vector autoregressive order selection and forecasting via the modified divergence information criterion

This paper examines the problem of order selection in connection to the forecasting performance for vector autoregressive (VAR) processes. For this purpose we present a generalisation of the modified divergence information criterion (MDIC) for VAR models and compare it with traditional information criteria by Monte Carlo methods for different data generating processes for small, medium, and large sample sizes. The VAR modified divergence information criterion (VAR/MDIC) shows remarkable good results by choosing the correct model more frequently than the known traditional information criteria with the smallest mean squared forecast error.average squared forecasting errors, order selection, modified divergence information criterion, MDIC, vector autoregressive, VAR process,

Adaptation and Validation of the Learning Style Inventory Version 3.1 in Greek Language: A Methodological Study

Abstract BACKGROUND AND PURPOSE: The nursing research on learning differences is currently expanding, suggesting the need for trustful measurements. This study aimed to adapt and cross-culturally validate the Learning Style Inventory. METHODS: The first phase involved symmetrical translation and adaptation to the Greek target language. The second phase concerned the psychometric testing. RESULTS: Internal reliability showed satisfactory alpha values. Kappa coefficients supported the test-retest reliability, and paired t test correlations justified the stability. Factor analysis yielded 2 constructs fitted with theory. The internal validity was also evidenced. The nursing students' learning style profile was discussed within their educational field and cultural background. CONCLUSIONS: The inventory presented content and construct equivalence to original scales. Certain implications were drawn for nursing supporting the utility of learning styles' measurements