Browsing by Subject "Time-series analysis."

Now showing 1 - 9 of 9

Open Access
An application of seasonal cointegration and error correction models on monthly data
(1995) Erçoşkun, Güliz
In this study, I try to analyze and show the monthly changes and their effects on each other of Istanbul Stock Exchange (ISE), TL / $ Exchange Rate (E), M l, M2, price level (P), Interest rate on securities (R) and Advances o f the central bank to the treasury (A) by developed techniques in time series econometrics, namely unit roots, seasonal cointegration and error correction. The long run relationship between stock prices and exchange rate, price level. M l, M2 investigated by using these techniques of time series. Conclusions are made for future use o f models for monthly time series. To our knowledge, this is among the pioneering studies conducted in an emerging market that uses an updated econometric methodology to allow for an analysis o f monthly data for long run steady state properties together with short run dynamics.
Open Access
Application of spectral and cross-spectral analysis to İstanbul Stock Exchange Market
(1995) Erigüç, Cüneyt Altan
In this study, stock exchange index and selected four securities Ege Gübre, Bağfaş, Adana Gübre and Tüpraş from Istanbul Stock Exchange Market were analyzed with spectral and cross-spectral methods. Consumer price index was used to find the real values of securities. First o f aU, spectral analysis was apphed to be able to find periodicity of securities and significant periodicities were foimd for these four o f them. Cross-spectral analysis was then apphed between stock exchange index and four o f these securities, each pair displayed statistically significant coherencies, the lead and lag relationships o f certain frequencies were found from phase difference values o f significant coherencies.
Open Access
Comparison of the forecast performances of linear time series and artificial neural network models within the context of Turkish inflation
(2001) Uçar, Nuri
This thesis compares a variety of linear and nonlinear models to find the one with the best inflation forecast performance for the Turkish Economy. These comparisons are performed by considering the type of series whether or not stationary. Different combination techniques are applied to improve the forecasts. It is observed that the combination forecasts based on nonstationary vector autoregressive (VAR) and artificial neural network (ANN) models are better than the ones generated by other models. Furthermore, the forecast values combined with ANN technique produce lower root mean square errors (RMSE) than the other combination techniques.
Open Access
Maximizing profit per unit time in cointegration based pairs trading
(2014) Tutal, Duygu
Open Access
Monetary dynamics: evidence from cointegration and error correction modeling: the case of Turkey
(1992) Kelezoğlu, Hüseyin
This paper addresses Lhe issue of Les-Ling Lhe cointegration relationship for a conventional money demand function and constructing an error correction model CECMD of it to analyze both long-run and short run dynamics by using Turkish quarterly data during the period 1977:1-1989:4. The assumption that all the determinants of the long run money demand function are endogenous allowed the construction of ECM in vector autoregressive CVARD form. This became much helpful on the examination of temporal causality characteristics of the long run Turkish money demand function.
Open Access
Novel time-frequency analysis techniques for deterministic signals
(2004) Durak, Lütfiye
In this thesis novel time frequency analysis techniques are proposed for deterministic signals It is shown that among all linear time frequency representations only the short time Fourier transformation STFT family satis es both the shift invariance and rotation invariance properties in both time frequency and all fractional Fourier domains The time frequency domain localization by STFT is then characterized by introducing a novel generalized time bandwidth product GTBP de nition which is an extension of the time bandwidth product TBP on the fractional Fourier domains For mono component signals it is shown that GTBP provides a rotation independent measure of compactness The GTBP optimal STFT which is a well localized and high resolution time frequency representation is introduced and its computationally e cient form is presented The GTBP optimal STFT provides optimal results for chirp like signals which can be encountered in a variety of application areas including radar sonar seismic and biological signal processing Also a linear canonical decomposition of the GTBP optimal STFT analysis is presented to identify its relation to the rotationally invariant STFT Furthermore for signals with non convex time frequency support an improved GTBP optimal STFT analysis is obtained through chirp multiplication or equivalently shearing in the D time frequency domain Finally we extend these ideas from time frequency distributions to joint fractional Fourier domain representations
Open Access
Sparsity and convex programming in time-frequency processing
(2014-12) Deprem, Zeynel
In this thesis sparsity and convex programming-based methods for timefrequency (TF) processing are developed. The proposed methods aim to obtain high resolution and cross-term free TF representations using sparsity and lifted projections. A crucial aspect of Time-Frequency (TF) analysis is the identification of separate components in a multi component signal. Wigner-Ville distribution is the classical tool for representing such signals but suffers from cross-terms. Other methods that are members of Cohen’s class distributions also aim to remove the cross terms by masking the Ambiguity Function (AF) but they result in reduced resolution. Most practical signals with time-varying frequency content are in the form of weighted trajectories on the TF plane and many others are sparse in nature. Therefore the problem can be cast as TF distribution reconstruction using a subset of AF domain coefficients and sparsity assumption in TF domain. Sparsity can be achieved by constraining or minimizing the l1 norm. Projections Onto Convex Sets (POCS) based l1 minimization approach is proposed to obtain a high resolution, cross-term free TF distribution. Several AF domain constraint sets are defined for TF reconstruction. Epigraph set of l1 norm, real part of AF and phase of AF are used during the iterative estimation process. A new kernel estimation method based on a single projection onto the epigraph set of l1 ball in TF domain is also proposed. The kernel based method obtains the TF representation in a faster way than the other optimization based methods. Component estimation from a multicomponent time-varying signal is considered using TF distribution and parametric maximum likelihood (ML) estimation. The initial parameters are obtained via time-frequency techniques. A method, which iterates amplitude and phase parameters separately, is proposed. The method significantly reduces the computational complexity and convergence time.
Open Access
Spectral analysis: money, income and price, 1962-1987
(1990) Özyıldırım, Sezgin
In this study, the influence of monetary policy upon the price level and the real income over the business cycle is analyzed. The cross-spectral analysis, which is utilised in this study, minimises the effects of differential goverment policies. The observation period is from 1962 to 1987. The findings of the study show that the monetary policy has a significant influence upon the price level and so on the inflation as well.
Open Access
Time frequency representation and its economic applications
(2009) Akkoyun, Hüseyin Çağrı
This thesis analyzes real oil price crises and US output gap by using time frequency representation. Firstly, time frequency representation is introduced by giving some basic definitions, formulations and illustrative examples. After that, frequency characteristics of demand-side driven and supply-side driven real oil price shocks are analyzed. Also, frequency characteristic of US output gap is analyzed by dividing the output gap series in three parts.