A comparative study of the reliability of nine statistical software packages

Keeling, Kellie B.; Pavur, Robert

doi:10.1016/j.csda.2006.02.013

Cited by 54 publications

(44 citation statements)

References 13 publications

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“…Developers do respond to benchmarks. This has been mentioned already in the context of the FIML and GARCH benchmarks, but more generally has been documented by Keeling and Pavur (2007), who re-evaluated the packages evaluated by McCullough (1999a) and found that errors have been corrected.…”

Section: Inaccurate Var Resultsmentioning

confidence: 94%

The Accuracy of Econometric Software

McCullough

2009

Handbook of Computational Econometrics

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Section: Inaccurate Var Resultsmentioning

confidence: 94%

The Accuracy of Econometric Software

McCullough

2009

Handbook of Computational Econometrics

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“…SAS provides a periodic newsletter containing bug warnings and it releases on a timely basis hot xes addressing various software issues. Moreover, as shown by studies such as Keeling and Pavur (2007) and McKenzie and Takaoka (2007), most software vendors do correct in the subsequent releases accuracy errors discovered by users. Finally, many if not all commercial programs have a time-limited trial version also.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the accuracy of software cannot be taken for granted and it is possible to nd serious errors in commonly used statistical programs as shown by surveys such as McCullough (1999), McCullough (2004), and Keeling and Pavur (2007). It is also documented by Newbold et al (1994), McCullough and Vinod (2003), and Stokes (2004) that dissimilar sets of results can be obtained when the same data and model is analyzed through dierent econometric programs.…”

Section: Introductionmentioning

confidence: 99%

“…It is evident that researchers and practitioners should treat computer output with a degree of circumspection and verify computer generated forecasts manually or by using more than one software package where available. This practice not only allows fewer errors and more accurate forecasts, but also leads to better programs because developers do correct errors discovered by users as shown by Keeling and Pavur (2007) and McKenzie and Takaoka (2007).…”

Section: Introductionmentioning

confidence: 99%

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On the importance of verifying forecasting results

Yalta

Jenal²

2009

International Journal of Forecasting

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We discuss the various sources of error in numerical computations with the use of examples from the literature relevant to time series analysis. We also submit a case where, by manual verication, we were able to discover a plausible forecast to be erroneous due to a number of software aws in the XLSTAT addin for Microsoft Excel. Furthermore, after discussing the alternative techniques for implementing on a computer the ARIMA (AutoRegressive, Integrated, Moving Average) methodology, we show that dierent approaches can cause considerable discrepancies in the results across dierent programs and even within a single software system.

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“…37 (2008), No. 2, 195-206 of many about the accuracy of statistical procedures in Excel (Heiser, 2006;Keeling and Pavur, 2007;Knüsel, 1998Knüsel, , 2005McCullough and Wilson, 1999, 2002.…”

Section: Measures Of Variabilitymentioning

confidence: 99%

Teaching Statistics with Excel A Big Challenge for Students and Lecturers

Duller

2016

AJS

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This paper has a look on the implementation of basic statistical methods in Excel, in particular in describing data. Often a frequency distribution is the starting point for describing a dataset, but its calculation is not that easy in Excel. Therefore the first section gives some alternatives to the common way. The next part presents some interesting solutions for measures of central tendency and variability provided by Excel. The last important topic is about creating graphs, where Excel has an advantage: creating histograms with unequal interval width is possible.Zusammenfassung: Dieser Beitrag diskutiert einige der Probleme, die bei der deskriptiven Statistik mit Excel auftreten. Häufigkeitsverteilungen sind meistens der Ausgangspunkt für eine umfassendere Datenanalyse, aber deren Berechnung in Excel ist nicht so leicht wie man vermuten könnte. Daher werden im ersten Abschnitt Alternativen zumüblichen Weg aufgezeigt. Der folgende Abschnitt zeigt interessante Ergebnisse bei der Berechnung von Lageund Streuungsmaßzahlen. Der letzte wichtige Bereich ist der Erstellung von Grafiken gewidmet, bei denen Excel einen wesentlichen Vorteil bietet: Histogramme sind auch mit unterschiedlichen Intervallbreiten darstellbar.

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A comparative study of the reliability of nine statistical software packages

Cited by 54 publications

References 13 publications

The Accuracy of Econometric Software

The Accuracy of Econometric Software

On the importance of verifying forecasting results

Teaching Statistics with Excel A Big Challenge for Students and Lecturers

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