Cointegration methodology for psychological researchers: An introduction to the analysis of dynamic process systems.

Longitudinal data analysis focused on internal characteristics of a single time series has attracted increasing interest among psychologists. The systemic psychological perspective suggests, however, that many long-term phenomena are mutually interconnected, forming a dynamic system. Hence, only multivariate methods can handle such human dynamics appropriately.

Analyzing fractal dynamics employing R.

Recent empirical studies from cognitive, social and biological psychology revealed the fractal properties of many psychological phenomena. Employing methodologies from time- and frequency-domain analyses enabled detecting persistent long-range dependencies in various psychological and behavioral time series. These very slowly decaying autocorrelations are known as 1/f noise and typical for self-similar long memory processes.

Estimating long-range dependence in time series: an evaluation of estimators implemented in R.

Recent studies have shown that many physiological and behavioral processes can be characterized by long-range correlations. The Hurst exponent H of fractal analysis and the fractional-differencing parameter d of the ARFIMA methodology are useful for capturing serial correlations. In this study, we report on different estimators of H and d implemented in R, a popular and freely available software package.

Comparison of automated procedures for ARMA model identification.

This article evaluates the performance of three automated proceduresfor ARMA modelidentification commonly available in current versions of SAS for Windows: MINIC, SCAN, and ESACF. Monte Carlo experiments with different model structures, parameter values, and sample sizes were used to compare the methods. On average, the procedures either correctly identified the simulated structures or selected parsimonious nearly equivalent mathematical representations in at least 60% of the trials conducted.

Model Identification of Integrated ARMA Processes.

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can be applied to either nontransformed or differenced series, so the advantages and drawbacks of both procedures were compared. The best results were 79% of correct identifications for SCAN and 80% for ESACF.