Search Results for « Time Series Analysis »

This paper introduces a new graphical method of displaying the evolutionary correlation properties of nonstationary time series. The method, called time‐correlation analysis (TCA), summarizes the local correlation properties with a smooth and monotone characterization function interpretable as the first‐order autocorrelation coefficient. The TCA plot is constructed by plotting the discretized characterization function as time series. Issues concerning the range of the TCA plot are investigated in detail. Both simulated and real‐data examples are given to demonstrate the application and interpretation of the method.

Abstract. Cook's likelihood displacement is a convenient measure of the impact of a model perturbation on parameter estimates. A commonly used model perturbation in regression is the deletion of a case , or equation. A natural model perturbation in the time series context is the deletion of an observation , or a group of observations. Diagnostics that measure the impact of individual observations on the time series estimates are explored in this paper. A diagnostic that compares the estimates of the innovation variance with and without a particular observation is studied in detail.

Abstract. The nearest‐neighbour method, because of its intuitively appealing nature and competitive theoretical properties, deserves consideration in time‐series applications akin to attention it has received lately in the i.i.d. case. Here it is shown that as a nonparametric regression device, like the kernel method, under the G 2 mixing assumption, it converges in quadratic mean at the Stone‐optimal rate. In the closing sections, our methodology is extended to a broader pattern‐recognition context, and applied to hydrologic data.

Abstract. We propose the logistic transformation as an appropriate instantaneous transformation for time series variables known to be bounded, such as those measured as proportions, and present an application to two unemployment rate series. Since such series are often analysed in logarithmic form, the two transformations are compared, and a likelihood ratio selection criterion is developed, which marginally favours the logistic transformation. We consider the question of transformation bias and its removal. The logistic transformation of bounded variables ensures consistency with the limiting behaviour and is symmetric, thus on transforming and modelling the unemployment rate or its complement, the employment rate, equivalent results are obtained.