Предрогнозна оцінка структури майна і капіталу промислового підприємства

Однією зі складових управління фінансами підприємства є прогнозування фінансового стану, яке дозволяє виявити здатність підприємства до стійкого функціонування і розвитку в умовах зміни зовнішнього і внутрішнього середовища господарювання. Сьогодні неможна недооцінювати роль прогнозування, оскільки функціонування підприємства завжди пов'язане з невизначеністю майбутніх наслідків дій того або іншого управлінського рішення. Однією із дисциплінуючих умов прогнозування фінансового стану промислових підприємств виступає оцінка їх фінансового стану у динаміці взагалі та предпрогнозна оцінка структури майна і капіталу зокрема.Одной из составляющих управления финансами предприятия является прогнозирование финансового состояния, которое позволяет выявить способность предприятия к стойкому функционированию и развитию в условиях изменения внешней и внутренней среды ведения хозяйства. Сегодня невозможно недооценивать роль прогнозирования, поскольку функционирование предприятия всегда связано с неопределенностью будущих последствий действий того или другого управленческого решения. Одним из дисциплинирующих условий прогнозирования финансового состояния промышленных предприятий выступает оценка их финансового состояния в динамике вообще и предпрогнозируемая оценка структуры имущества и капитала в частности

What's in a Day? : A Guide to Decomposing the Variance in Intensive Longitudinal Data

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.Peer Reviewe

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available