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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. For some models and parameterizations, the accuracy of SCAN and ESACF was disappointing. The key finding of the study is that both human experts and automated methods provide inconsistent model identifications. Hence an elaborated strategy for model selection combining different techniques was developed and demonstrated on 2 empirical examples.

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. Unlike the majority of time series methodologies, the cointegration approach allows interdependencies of integrated (i.e., extremely unstable) processes to be modelled. This advantage results from the fact that cointegrated series are connected by stationary long-run equilibrium relationships. Vector error-correction models are frequently used representations of cointegrated systems. They capture both this equilibrium and compensation mechanisms in the case of short-term deviations due to developmental changes. Thus, the past disequilibrium serves as explanatory variable in the dynamic behaviour of current variables. Employing empirical data from cognitive psychology, psychosomatics, and marital interaction research, this paper describes how to apply cointegration methods to dynamic process systems and how to interpret the parameters under investigation from a psychological perspective.

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Recent empirical studies on human performance in cognitive tasks have provided evidence of long-range dependence in psychological time series. ARFIMA (p, d, q) methodology, an extension of the traditional Box-Jenkins ARIMA modeling, allows estimation of the long-term dependence in the presence of any possible short-memory components. This article examines, by means of Monte Carlo experiments, sample size requirements for the accurate estimation of the long-memory parameter d and documents the quality of the estimates for time series of different length in various (0, d, 0) and (1, d, 1) models. We demonstrate that the conditional sum of squares estimation, a computationally convenient technique available in current versions of SAS for Windows, provides good finite-sample performance, comparable to that of ML. Furthermore, a minimum sample size for parsimonious planning of psychological experiments is recommended.

Time series with deterministic and stochastic trends possess different memory characteristics and exhibit dissimilar long-range development. Trending series are nonstationary and must be transformed to be stabilized. The choice of correct transformation depends on patterns of nonstationarity in the data. Inappropriate transformations are consequential for subsequent analysis and should be omitted. The objectives of this article are (1) to introduce unit root testing procedures, (2) to evaluate the strategies for distinguishing between stochastic and deterministic alternatives by means of Monte Carlo experiments, and (3) to demonstrate their implementation on empirical examples using SAS for Windows.

This article evaluates the performance of three automated procedures for ARMA model identification 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. For autoregressive models, MINIC achieved the best results. SCAN was superior to the other two procedures for mixed structures. For moving-average processes, ESACF obtained the most correct selections. For all three methods, model identification was less accurate for low dependency than for medium or high dependency processes. The effect of sample size was more pronounced for MIMIC than for SCAN and ESACF. SCAN and ESACF tended to select higherorder mixed structures in larger samples. These fmdings are confined to stationary nonseasonal time series.Time series analysis deals with repeated and equally spaced observations on a single unit. Classical statistical techniques are no longer appropriate here because data points cannot be assumed independent and uncorrelated. One of the most widely employed procedures for time series data is the autoregressive integrated moving-average (ARIMA) approach proposed by Box and Jenkins (1970). Since Glass, Willson, and Gottman (1975) introduced the ARIMA technique to the social and behavioral sciences, this methodology has been increasingly employed in different research fields (Delcor,

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