Narrow Results

The study aims to examine the relationships between variables from different perspectives by using Turkey’s Real exchange rate (TL/USD), Real interest rate and Consumer price index data. Data from 2012M7 to 2021M12 were used in the study. In order to examine the relationships between the variables, seasonality tests and stationarity studies, which are among the time series analysis methods, were performed. Then, the model was estimated within the scope of VAR Analysis, the compatibility of the model with the real data was checked, the validity and reliability tests of the model were made and the residuals were examined. Inter-variable Impact Response Function and Variance Decomposition statistics are discussed for the model that meets all assumptions. The use of current data in the study and the use of graphics for qualitative evaluation contributed to the literature. As a result of this study, it has been determined that the consumer price index moves independently of other variables, and there is a limited relationship between exchange rate and real interest in every respect. In the first part of the study, the introduction and the theoretical framework are discussed. In the second part, the literature is examined, and in the third part, the methods and applications used in the study are given. The last part is the conclusion and discussion.

We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.