Данная статья посвящена вопросам реализации алгоритмов моделей векторной авторегрессии (VAR) в программной среде R. Достоинствами являются: простота их использования, точность прогнозов, сопоставимая с точностью сложных макроэкономических моделей, отсутствие каких-либо структурных или идентификационных ограничений на параметры. Однако последняя особенность приводит к проблеме перепараметризации...

In recent years Bayesian methods have become widespread in many domains including computer vision, signal processing, information retrieval and genome data analysis. The availability of fast computers allows the required computations to be performed in reasonable time, and thereby makes the benefits of a Bayesian treatment accessible to an ever broadening range of applications. In this tutorial...

Corporate economic performance and its association with carbon emissions has been the subject of extensive research within last few decades. The present study examines green firms as reflected in Financial Times Stock Exchange Environmental Opportunities Index Series (FTSE EO) emissions, incorporating role two more indices, namely Baltic Clean Tanker (BAIT) EUR/USD exchange rate. methodology em...

The Bayesian support vector regression (BSVR) metamodel is widely used in various engineering fields to analyze the uncertainty arising from uncertain parameters. However, accuracy of BSVR based on traditional one-shot sampling method fails meet requirements analysis complex systems. To this end, an error-pursing adaptive presented by combining a new scheme. This scheme was improved error-pursu...

This paper compares the out-of-sample forecasting accuracy of five classes of time series models for market shares of the six most important Portuguese car market competitors over different horizons. As representative time series models I employ a random walk with drift (Naive), a univariate ARIMA, a near-VAR and a general BVAR. The out-of-sample forecasts are also compared against forecasts ge...