Forecasting and Granger Modelling with Non-linear Dynamical Dependencies

Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the predictive function but also the matrix-valued kernel underlying the function search space directly from the data. Our approach is based on learning multiple matrix-valued kernels, each of those composed of a set of input kernels and a set of output kernels learned in the cone of positive semi-definite matrices. In addition to superior predictive performance in the presence of strong non-linearities, our method also recovers the hidden dynamic relationships between the series and thus is a new alternative to existing graphical Granger techniques.Comment: Accepted for ECML-PKDD 201

RICE PRICE MODELING IN SIX PROVINCE OF JAVA ISLAND USING VARMAX MODEL

Rice is an important food commodity in Indonesia because it is not only a main food but also social commodity, and has influence in politic stabilities and economic growth in Indonesia. Based on this condition is showed that everything about rice especially rice price has social economic impact in Indonesia. Factors that influence the domestic rice price in Indonesia are real exchange value, domestic corn price, and basic rice price This research aims to create models of rice price monthly data from six province in Java to real exchange value from 2007 until 2014 by using multivariate time series modeling approach with covariate, that is VARMAX (Vector ARIMAX) model. The results show that rice price in West Java, DI Yogyakarta, and Banten are influenced by rice price in DKI Jakarta, Central Java, and East Java, and real exchange value. Based on RMSE value, the best model is using VECMX(2,1) model. Keywords : ARIMA, VARMA, VARMA

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

Who was in the driving seat in Europe during the nineties, International financial markets or the BUBA?

The purpose of this paper is to reexamine empirically the relationship between long-term interest rates in well integrated ?nancial markets. The analysis focuses on long-term interest rates in the US and Germany and has been carried out within the framework of a ?ve dimensional VAR for the simultaneous determination of short- and long-term interest rates in the US and Germany and the rate of exchange rate depreciation. The results strongly support the existence of a long-run relationship between the long-term German and the longterm US interest rate and imply a full pass-through of changes in the long-term US rate into the corresponding German rate. The analysis also substantiates that the direction of causality goes from the longterm US to the long-term German interest rate. With regard to the possibility of controlling the long end of the market on the part of the Bundesbank, the paper apparently takes on a rather pessimistic view, as there is nothing to indicate a long-run relationship between shortand long-term German interest rates. However, the strong in?uence that short-term German interest rates exhibit on German long-term interest rates in the very short run according to the structural model of this paper, might be taken to indicate that the opposite is the case, as e ects originating from expectations of future short-term interest rates might totally neutralize an unequivocally positive short-run portfolio e ect in the long run. If this is the case, there is nothing strange to the fact that one is unable to identify a long-run relationship between short- and long-term German interest rates. On the contrary, it is exactly what to be expected if the monetary transmission mechanism works appropriately.Cointegration, Simultaneous Equation Models, International Interest Rate Linkages, Transmission Mechanism