Ata Ozkaya

A model of active trading by using the properties of chaos

Article history: Available online xxxx

A new method to estimate short-run and long-run interaction mechanisms in interictal state

Abstract Using different methods, most of the research articles on epilepsy analyze the structures of different neurological states (interictal, pre-ictal, ictal and post-ictal) to determine their distinguishing properties. On the other hand, some studies investigate the causal relationship between interictal state, pre-ictal and ictal state, especially in order to predict the seizures from the interictal EEG activities. This type of usage of the interictal data mathematically needs the imposition of some constraints which in turn may prevent researchers to extract more useful information hidden in the interictal EEG data. In the present study: firstly, we explore the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals we analyzed the revealed short-run and long-run interaction mechanisms of neuronal ensembles. Here we find: first, that short interictal series contains unit root and can be represented as autoregressive integrated moving average (ARIMA) process; second, between the interictal EEG signals there exists bidirectional causality (anthogonist effects) in the long-run. Therefore, in the long-run neither of the synchronized neuronal assemblies are positively affected (increasing amplitudes) from this relationship. Moreover, the long-run mechanism originated by co-movement (cointegration) of the interictal series reveals why there should not be a causal link from the interictal state to ictal state.

Hidden overhang of domestic debt and its role in the this-time-is-different syndrome: : An empirical contingent liabilities model

The recent studies in public finance literature open an exciting research area on hidden overhang of domestic public debt and creative accounting. In this study, I identify hidden public debts in Turkey. I then develop a dynamical model that takes as given the stock of contingent liabilities generated by lending/borrowing relationships among public entities and looks for the debt (in)tolerance of government to liquidate it in finite periods. Last, I introduce a general empirical methodology to analyze the role of overborrowing in the this-time-is-different syndrome and test model outcome against data for hidden debts in Turkeys postliberalization period (1989-2010).The recent studies in public finance literature open an exciting research area on hidden overhang of domestic public debt and creative accounting. In this study, I identify hidden public debts in Turkey. I then develop a dynamical model that takes as given the stock of contingent liabilities generated by lending/borrowing relationships among public entities and looks for the debt (in)tolerance of government to liquidate it in finite periods. Last, I introduce a general empirical methodology to analyze the role of overborrowing in the this-time-is-different syndrome and test model outcome against data for hidden debts in Turkeys postliberalization period (1989-2010).

Estimating short-run and long-run interaction mechanisms in interictal state

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.