Abstract
The series of graphs presented here, based on standard time series diagnostics and display graphs, eases the tasks of identifying and checking an ARIMA model. Each diagnostic display consists of a matrix of plots for a series of ARIMA(p, d, q) models (with p = 1:p(max), q =1:q(max) and d constant). In this way the identification phase of the analysis is eased because the analyst can directly see the incremental effect of each proposed term. The direct visual comparison of the models is helpful to the experienced analyst because it makes immediate the difference in the explanatory capabilities of the various models. The series of plots is very helpful in presenting time series concepts, particularly the identification phase, to introductory classes. The plots have been implemented using the Trellis system in S-Plus.

Abstract
Many time series encountered in practice are nonstationary, and instead are often generated from a process with a unit root. Because of the process of data collection or the practice of researchers, time series used in analysis and modeling are frequently obtained through temporal aggregation. As a result, the series used in testing for a unit root are often time series aggregates. In this paper, we study the effects of the use of aggregate time series on the Dickey-Fuller test for a unit root. We start by deriving a proper model for the aggregate series. Based on this model, we find the limiting distributions of the test statistics and illustrate how the tests are affected by the use of aggregate time series. The results show that those distributions shift to the right and that this effect increases with the order of aggregation, causing a strong impact both on the empirical significance level and on the power of the test. To correct this problem, we present tables of critical points appropriate for the tests based on aggregate time series and demonstrate their adequacy. Examples illustrate the conclusions of our analysis.

Abstract
BackgroundThe COVID-19 pandemic reinforced the need to invest in nursing practice environments and health institutions were led to implement several changes. In this sense, this study aimed to analyze the impact of the changes that occurred in nursing practice environments between the first and fourth critical periods of the pandemic.MethodsQuantitative, observational study, conducted in a University Hospital, with the participation of 713 registered nurses. Data were collected through a questionnaire with sociodemographic and professional characterization and the Scale for the Environments Evaluation of Professional Nursing Practice, applied at two different points in time: from 1 to 30 June 2020 and from 15 August to 15 September 2021. Data were processed using descriptive and inferential statistics.ResultsOverall, the pandemic had a positive impact on nursing practice environments. However, the Process component remained favourable to quality of care, while the Structure and Outcome components only moderately favourable. Nurses working in Medicine Department services showed lower scores in several dimensions of the Structure, Process and Outcome components. On the other hand, nurses working in areas caring for patients with COVID-19 showed higher scores in several dimensions of the Structure, Process and Outcome components.ConclusionsThe pandemic had a positive impact on various dimensions of nursing practice environments, which denotes that regardless of the adversities and moments of crisis that may arise, investment in work environments will have positive repercussions.However, more investment is needed in Medicine Department services, which have historically been characterised by high workloads and structural conditions that make it difficult to promote positive and sustainable workplaces.

Abstract
Many time series exhibit conditional heteroscedasticity such as stock prices or returns, interest rates or exchange rates. Time series used in empirical analysis are often temporal aggregates. We study the effects of using temporally aggregated time series in testing for heteroscedasticity. The distribution of the test statistics is affected by aggregation which causes a severe power loss that worsens with the order of aggregation. Thus, the tests often fail to detect the heteroscedastic nature of the data which is a misleading outcome and can entail wrong decisions. Our conclusions are illustrated by an empirical application.

Abstract
It is well known that conditional heteroscedasticity is exhibited by many economic and financial time series such as stock prices or returns. Empirical analysis is often based on a subseries obtained through systematically sampling from an underlying time series and we analyze how that can affect testing for heteroscedasticity. The results show the distribution of the test statistics is changed by systematic sampling, causing a serious power loss that increases with the sampling interval. Consequently, the tests often fail to reject the hypothesis of no conditional heteroscedasticity, leading to the wrong decision and missing the true nature of the data-generating process.