Costationarity of Locally Stationary Time Series Using Costat
نویسندگان
چکیده
This article describes the R package costat. This package enables a user to (i) perform a test for time series stationarity; (ii) compute and plot time-localized autocovariances, and (iii) to determine and explore any costationary relationship between two locally stationary time series. Two locally stationary time series are said to be costationary if there exists two time-varying combination functions such that the linear combination of the two series with the functions produces another time series which is stationary. Costationarity existing between two time series indicates a relationship between the series that might be usefully exploited in a number of ways. Sometimes the relationship itself is of interest, sometimes the derived stationary series is of interest and useful as a substitute for either of the original stationary series in some applications.
منابع مشابه
Costationarity and stationarity tests for stock index returns
We present a new analysis of the FTSE and SP500 stock index log return series and provide evidence that they are not stationary. We then discover two time-varying linear combinations of the FTSE and SP500 series that are stationary and hence declare the two series to be costationary. The stationary combinations are themselves worthy of study using classical time series methods. The existence of...
متن کاملCostationarity of locally stationary time series
Loosely speaking, a stationary time series is one whose statistical properties remain constant over time, whereas the statistical properties of locally stationary (LS) time series change slowly over time. As a consequence, LS series can appear stationary when examined close up, but appear nonstationary when examined on a larger scale. Priestley (1983) and Nason and von Sachs (1999) review local...
متن کاملCostationarity of locally stationary time series
Loosely speaking, a stationary time series is one whose statistical properties remain constant over time, whereas the statistical properties of locally stationary (LS) time series change slowly over time. As a consequence, LS series can appear stationary when examined close up, but appear nonstationary when examined on a larger scale. Priestley (1983) and Nason and von Sachs (1999) review local...
متن کاملSome New Methods for Prediction of Time Series by Wavelets
Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...
متن کاملUsing Wavelets and Splines to Forecast Non-Stationary Time Series
This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...
متن کامل