The development of a time series methodology: from recursive residuals to dynamic conditional score models

2015 ◽  
pp. 10-13
Author(s):  
Andrew Harvey
Keyword(s):  
Time Series ◽  
Recursive Residuals ◽  
Time Series Methodology

scholarly journals STUDY OF TESTS FOR TREND IN TIME SERIES

2021 ◽  
Vol 39 (2) ◽  
pp. 311-333
Author(s):  
Denise de Assis PAIVA ◽  
Thelma SÁFADI
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Structural Breaks ◽  
Consumer Price Index ◽  
Unit Root Tests ◽  
Metropolitan Region ◽  
Maximum Temperature ◽  
Pettitt Test ◽  
Using Data ◽  
Time Series Methodology

The time series methodology is an important tool when using data over time. The time series can be composed of the components trend (Tt), seasonality (St) and the random error (at). The aim of this study was to evaluate the tests used to analyze the trend component, which were: Pettitt, Run, Mann-Kendall, Cox-Stuart and the unit root tests (Dickey-Fuller, Dickey-Fuller Augmented and Zivot and Andrews), given that there is a discrepancy between the test results found in the literature. The four series analyzed were the maximum temperature in the Lavras city, MG, Brazil, the unemployment rate in the Metropolitan Region of S~ao Paulo (RMSP), the Broad Consumer Price Index (IPCA) and the nominal Gross Domestic Product (GDP) of Brazil. It was found that the unit root tests showed similar results in relation to the presence of the stochastic trend for all series. Furthermore, the turning point of the Pettitt test diverged from all the structural breaks found through the Zivot and Andrews test, except for the GDP series. Therefore, it was found that the trend tests diverged, obtaining similar results only in relation to the unemployment series.

scholarly journals A Clinician's Primer for Idiographic Research

2019 ◽  
Author(s):  
Marilyn Piccirillo ◽  
Emorie D Beck ◽  
Thomas Rodebaugh
Keyword(s):  
Time Series ◽  
Clinical Practice ◽  
Data Collection ◽  
Clinical Psychology ◽  
Word Of Mouth ◽  
Group Level ◽  
Individual Level ◽  
Clinical Questions ◽  
Time Series Methodology

Theorists and clinicians have long noted the need for idiographic (i.e., individual-level) designs within clinical psychology. Results from idiographic work may provide a possible resolution of the therapist’s dilemma – the problem of treating an individual using information gathered via group-level research. Due to advances in data collection and time series methodology, there has been increasing interest in using idiographic designs to answer clinical questions. Although time series methods have been well-studied outside the field of clinical psychology, there is limited direction on how clinicians can use such models to inform their clinical practice. In this primer, we collate decades of published and word-of-mouth information on idiographic designs, measurement, and modeling. We aim to provide an initial guide on the theoretical and practical considerations that we urge interested clinicians to consider before conducting idiographic work of their own.

scholarly journals Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

2020 ◽  
Author(s):  
Prashant Verma ◽  
Mukti Khetan ◽  
Shikha Dwivedi ◽  
Shweta Dixit
Keyword(s):  
Time Series ◽  
Health Care Systems ◽  
Medical Personnel ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Data Set ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Time Series Methodology ◽  
Near Future

Abstract Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

scholarly journals Structural Growth in Labor Costs: Evidence from the Employment Cost Index

2014 ◽  
Vol 25 (1) ◽  
pp. 17-24 ◽  
Author(s):  
Gary R. Albrecht ◽  
Kurt V. Krueger
Keyword(s):  
Time Series ◽  
Growth Rates ◽  
Wage Growth ◽  
The United States ◽  
Labor Costs ◽  
Cost Index ◽  
Forensic Economics ◽  
Structural Growth ◽  
The Common ◽  
Time Series Methodology

Abstract Wage growth forecasting is a necessary part of forensic economics. In this paper, we present a time series methodology to test whether wage and compensation growth in the United States varies by industry and occupation. If growth varies, then the common use of “all-worker” net discount or wage growth rates would not be accurate for every forensic economic case. Using the Employment Cost Index, we find that total compensation, wage, and benefit growth in some, but not all, industries and occupations has been significantly different from that of the wage growth of all workers. That finding may concern the forensic economist who needs to construct a variety of net discount or wage growth rates. As an alternative to constructing multiple forecasts, this paper provides estimated industry and occupational specific differentials from the growth in all workers' wages.

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