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The empirical properties of benchmark revisions to key US macroeconomic aggregates are examined. News versus noise impact of revisions is interpreted via the cointegration property of successive benchmark revisions. Cointegration breaks down in the last two years before a benchmark revision. Hence, we conclude that there is some information content in benchmark revisions. This last point is illustrated by reporting that inflation forecasts could be improved by the addition of a time series that reflects benchmark revisions to real GDP. Standard backward- and forward-looking Phillips curves are used to explore the statistical significance of benchmark revisions.

Penn World Tables (PWT) data on output measured at international prices are the data most frequently used in cross‐country growth regressions. These data are subject to revision, and the amendments can be substantial for a minority of countries, although negligible for most. The purpose of this paper is to investigate the effect of data revisions on research results using the data.

Using Hanushek and Kimko's analysis of the relationship between growth and schooling quality and Sala‐i‐Martin's tests of model selection, the authors investigate how much the results of cross‐country growth regressions vary if the most recent vintage (6.2) of PWT data is used, rather than the previous vintage (6.1).

The variation is substantial enough to imply significant differences in research results using different vintages of the PWT data.

The results reinforce the case for examining the sensitivity of growth regressions to outliers, which may be subject to subsequent data revision that might substantially affect the conclusions.

Previous research has identified significant revisions between successive vintages of PWT growth data, but has implied that this is not likely to affect the results of cross‐country growth regressions based on long‐run averages rather than on annual data. The findings suggest that this is not necessarily the case.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

Soon after beginning operations, the Federal Reserve established a nationwide network for collecting information about the economy. In 1919, the Fed began tabulating data by about retail sales, which it viewed as a fundamental measure of consumption. From 1920 until 1929, the Federal Reserve published data about retail sales each month by Federal Reserve district, but ceased to do so after 1929. It continued to compile monthly data on retail sales by reserve district, but this data remained in house. We collected these in-house reports from the archives of the Board of Governors and constructed a consistent series on retail trade at the district level. The new series enhances our understanding of economic trends during the Roaring ‘20s and Great Depression.

The present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.

The cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.

The findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.

This research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.

This paper discusses the nominal and real convergence regarding Greece being a country-member of the European Union (EU), and of the Economic and Monetary Union (EMU). We argued that nominal convergence is relative to Maastricht criteria when real convergence has been investigated through six different axes: (1) the five Maastricht Criteria, (2) the GDP per capita in PPP prices, (3) the real GDP growth rates, (4) the minimum wages, (5) the HDI index development, and (6) the unemployment rates. We concluded for the case of Greece that by utilizing alternative indicators, such as the Maastricht criteria, and the above criteria only nominal convergence exists while real convergence appears to be a long-term target with many obstacles. In particular, Greece has managed to achieve the criteria proposed by the EMU (Maastricht Criteria) for membership, decisively different levels of unemployment, wages, and GDP growth rate/GDP per capita in PPP prices, and different human development indexes appear for the case of Greece.

This article describes an important project in the history of preservation of Egypt's culture heritage. Initiated by the Egyptian Ministry of Culture and executed by the Cabinet Information and Decision Support Center (IDSC), the project involves the Cairo Egyptian museum that contains the world largest collection of ancient Egyptian antiquities (160,000 objects). A major problem in the museum is the lack of a standardised approach for registration, resulting in a variety of unrelated registration schemes and the non‐existence of efficient tools for information dissemination. This project aims to resolve these problems. A team of Egyptologists, museum curators and system analysts, designers and developers defined the basis for work and specified project challenges which included the indexing of all museum objects ensuring cross‐referencing between already used registration schemes. The result has involved the use of multimedia computer technology to establish a complete database of museum objects (text, image and sound) and various cultural products, in addition to the establishment of required procedures and standards to organise the internal museum workflow and the provision of training programmes for staff. The outcome of the project is expected to be profound in enabling researchers and curators to have instant access to any object in the museum and providing the museum's visitors and public with a wealth of information about ancient Egypt.

The purpose of this study is to analyze the author’s experience related to several attempts of getting approved a paper of their authorship about gender and organizations by the lens of a researcher trying to meet his goal.

Throughout the endeavor, the author received substantial feedback and evaluations from editors and reviewers that allowed him to understand better the motivation of those that are dedicated to carry out such task; enhance his work whenever was possible; increase his resilience and self-motivation; and identify several inconsistencies of the peer-review process. Furthermore, that ample opinionated material allowed him to conduct the current autoethnographic study.

Accordingly, the author’s findings identified three aggregate dimensions, namely, demotivating assessments, mixed perceptions and motivating assessments. Moreover, the author did not identify any trace of developmental review (help), bill of rights or notion of being an “artist,” as some scholars suggest, from both the reviewers’ or editors’ part, but only from a specific journal’s editor and one of its reviewers. On the one hand, the majority of the reviewers/editors showed a harsh view about the author’s work or even a lack of interest to ponder his arguments and difficulties to carry out that study. Even though the author alluded to the limitations and unsurmountable hurdles that he faced along the way, they showed neither sympathy nor comprehension to his comments. On the other hand, it was not an easy task to the author to sift the hints provided by them.

Nonetheless, the author also recognizes his own limitations that eventually affected his analysis and point of views. It is also worth noting that this method relies on a unique source (the author).

The author believes that his ideas and opinions have some base and merit. Rather, his findings embrace profound implications for reviewers and editors, particularly in terms of how they perform their work.

To the best of the author’s knowledge, this is the first endeavor focusing on peer-review system related to organizational studies and grounded in an autoethnography approach. Therefore, their contribution is derived from a researcher that is familiar with the system and its flaws.

Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.