The paper presents an applied study of a multivariate AR(p) process fitted to daily data from U.S. commodity futures markets with the use of Bayesian statistics. In the first part a detailed description of the methods used is given. In the second part two BVAR models are chosen one with assumption of lognormal, the second with normal distribution of prices conditioned on the parameters. For a c...

We are interested in an XML representation for programming languages. Could content markup of MathML be used for this purpose, especially in the setting of a functional programming language? In order to address this question we have developed an interpreter for a Scheme-like language encoded in XML. We demonstrate that, with some degree of extension to its core elements, syntax and default sema...

We study cross-country differences in monetary policy transmission across the largest euro area economies (France, Germany, Italy, and Spain) using a large Bayesian vector autoregressive (BVAR) model with endogenous prior selection. Drawing on posterior distributions of impulse responses other tests, we find real output money supply to respond more strongly Germany than countries. Whereas, pric...

با توجه به رشد بالای جمعیت در جهان و نیاز اطمینان از امنیت غذایی، افزایش تولید واحد سطح محصولات زراعی به‌منزلة راهبردی اساسی حل مسئلة تأمین غذا به‌شمار می‌رود. سوی دیگر، وجود محدودیت زیرکشت پایین‌بودن میانگین عملکرد برخی کشاورزی مانند گندم کشور، محصول می‌تواند راهکاری عملی پاسخ کشور محسوب شود. یکی مهم‌ترین بیماری‌های فوزاریوم است که، نقش پیش‌بینی این بیماری جلوگیری کاهش بهره‌وری محصول، مدل‌هایی...

This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better sui...

Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange...

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