abstract: this paper discusses two topics. at first, it uses continuous wavelet (morlet) transform, coherency, and phase angle analysis to study the effect of mean volatility in opec crude oil prices, wti crude oil prices, brent crude oil prices and iran’s crude oil prices on the iran’s reer. to this end, the monthly data of the variables for years 2003 to 2012 is used. coherency analysis revea...

the main objective of this paper is to study the relationship between the energy consumption (energy use intensity), economic growth, and co2 emission in iran. to that end, we use time series data of iran during 1967–2004. in order to examine the long-run relationship among these variables, the johanson-juselius cointegration method is used along with vecm. the main finding of this study shows ...

This paper presents a novel characterization of continuous time processes that captures the primary idiosyncratic features of many financial time series. We extend the analysis of unit root behaviors to incorporate series that have continuous sampling, but do so in such a way that the overall series does not tend towards explosive paths, as is implied by many unit root setups. In doing so, we e...

Abstract The aim of the paper is to compare forecasting performance a class statedependent autoregressive (SDAR) models for univariate time series with two alternative families nonlinear models, such as SETAR and GARCH models. study conducted on US GDP growth rate using quarterly data. Two methods forecast comparison are employed. first method consists in evaluation average by measures root mea...

A panel of ex-ante forecasts of a single time series is modeled as a dynamic factor model, where the conditional expectation is the single unobserved factor. When applied to out-of-sample forecasting, this leads to combination forecasts that are based on methods other than OLS. These methods perform well in a Monte Carlo experiment. These methods are evaluated empirically in a panel of simulate...

Are share markets too volatile? While it is difficult to ignore share market volatility it is important to determine whether volatility is excessive. This paper replicates the Shiller (1981) test as well as applying standard time series analysis to annual Australian stock market data for the period 1883 to 1999. While Shiller’s test suggests the possibility of excess volatility, time series ana...

Multiresolution wavelet analysis is a natural way to decompose economic time series into components of various frequencies: long-run trend, business-cycle component, and high frequency noise. This paper illustrates the method on real GNP and inflation. The business-cycle component of the wavelet-filtered series closely resembles the series filtered by the approximate bandpass filter (Baxter and...

Much time series data are recorded on economic and financial variables. Statistical modelling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of ’memory’, or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are d...

Confidence intervals in econometric time series regressions suffer from notorious coverage problems. This is especially true when the dependence in the data is noticeable and sample sizes are small to moderate, as is often the case in empirical studies. This paper suggests using the studentized block bootstrap and discusses practical issues, such as the choice of the block size. A particular da...