We propose a tree-structured heterogeneous autoregressive (tree-HAR) process as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors’ dependent regime shifts in the conditional mean dynamics of the realized correlation series. Testing the model on S&P 500 and 30-year treasu...

This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling procedures: Information Criterion Pruning (ICP), Cross-Validation Pruning (CVP) and Bayesian Regularization...

This paper considers the problem of constructing confidence sets for the date of a single break in a linear time series regression. We establish analytically and by small sample simulation that the current standard method in econometrics for constructing such confidence intervals has a coverage rate far below nominal levels when breaks are of moderate magnitude. Given that breaks of moderate ma...

In this paper, we develop topological data analysis methods for classification tasks on univariate time series. As an application, perform binary and ternary two public datasets that consist of physiological signals collected under stress non-stress conditions. We accomplish our goal by using persistent homology to engineer stable features after use a delay embedding the subwindowing instead wi...

Using econometric tools for selecting I(1) and I(2) trends, we found the existence of static long-run steady-state and dynamic long-run steady-state relations between temperature and radiative forcing of solar irradiance and a set of three greenhouse gases series. Estimates of the adjustment coefficients indicate that temperature series is error correcting around 5e65% of the disequilibria each...

this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

s tructural change is defined as a change in the relative weight of the important constituents of the macro-economic indicator such as production, taxes, imports and exports, workforce etc. since the structure change is one of the main reasons for the growth and economic development of countries, the investigation of the trend of changes in economic important constituents is important. tax as a...

This paper introduces a conditional extreme value volatility estimator (EVT) based on highfrequency returns. The relative performance of the EVT is compared with the discrete-time GARCH and implied volatility models for 1-day and 20-day-ahead forecasts of realized volatility. This is also a first attempt towards detecting any time-series variation in extreme value distributions using high-frequ...

This paper estimates the speed of the adjustment coefficient in structural error correction models (ECM) and employs a system method for real exchange rates with Hansen and Sargent’s (1980, 1982) IV methods. Empirical results show that the half-lives of purchasing power parity deviations are less than one year in most cases. JEL classification: C22, F31, F41

In this study, we look at the relationship between export stability, investment and economic growth in nine Asian countries using time series data. The few previous time series studies in this area have not paid any attention to stationarity and cointegration issues. We find that in most cases, the variables are non-stationary in their levels and not cointegrated. These results raise serious do...