A real-time data set for macroeconomists

This paper presents the concept and uses of a real-time data set that can be used by economists for testing the robustness of published econometric results, for analyzing policy, and for forecasting. The data set consists of vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper illustrates why such data may matter, explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, examines key empirical papers in macroeconomics and investigates their robustness to different vintages, looks at how policy analysis may be affected by data revisions, and shows how forecasts can be affected by data revisions.Forecasting ; Macroeconomics

Forecasting coin demand.

Shortages of coins in 1999 and 2000 motivated the authors to develop models for forecasting coin demand. A variety of models were developed, tested, and used in realtime forecasting. This paper describes the models that were developed and examines the forecast errors from the models both in quasi-ex-ante forecasting exercises and in realtime use. Tests for forecast efficiency are run on each model. Real-time forecasts are examined. The authors conclude with suggestions for further refinements of the models.Coinage

Is macroeconomic research robust to alternative data sets?

This paper uses a real-time data set to analyze data revisions and to test the robustness of published econometric results. The data set consists of vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper illustrates why such data may matter, examines the properties of several of the variables in the data set across vintages, and examines key empirical papers in macroeconomics, investigating their robustness to different vintages.Macroeconomics

Forecasting with a real-time data set for macroeconomists

This paper discusses how forecasts are affected by the use of real-time data rather than latest-available data. The key issue is this: In the literature on developing forecasting models, new models are put together based on the results they yield using the data set available to the model’s developer. But those are not the data that were available to a forecaster in real time. How much difference does the vintage of the data make for such forecasts? The authors explore this issue with a variety of exercises designed to answer this question. In particular, they find that the use of real-time data matters for some forecasting issues but not for others. It matters for choosing lag length in a univariate context. Preliminary evidence suggests that the span—or number—of forecast observations used to evaluate models may also be critical: the authors find that standard measures of forecast accuracy can be vintage-sensitive when constructed on the short spans (five years of quarterly data) of data sometimes used by researchers for forecast evaluation. The differences between using real-time and latest-available data may depend on what is being used as the “actual” or realization, and we explore several alternatives that can be used. Perhaps of most importance, we show that measures of forecast error, such as root-mean-squared error and mean absolute error, can be deceptively lower when using latest-available data rather than real-time data. Thus, for purposes such as modeling expectations or evaluating forecast errors of survey data, the use of latest-available data is questionable; comparisons between the forecasts generated from new models and benchmark forecasts, generated in real time, should be based on real-time data.Forecasting

A real-time data set for macroeconomists: does data vintage matter for forecasting?

This paper describes a real-time data set for macroeconomists that can be used for a variety of purposes, including forecast evaluation. The data set consists of quarterly vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, and provides an example showing how data revisions can affect forecasts.Forecasting

USBcat - Towards an Intrusion Surveillance Toolset

This paper identifies an intrusion surveillance framework which provides an analyst with the ability to investigate and monitor cyber-attacks in a covert manner. Where cyber-attacks are perpetrated for the purposes of espionage the ability to understand an adversary's techniques and objectives are an important element in network and computer security. With the appropriate toolset, security investigators would be permitted to perform both live and stealthy counter-intelligence operations by observing the behaviour and communications of the intruder. Subsequently a more complete picture of the attacker's identity, objectives, capabilities, and infiltration could be formulated than is possible with present technologies. This research focused on developing an extensible framework to permit the covert investigation of malware. Additionally, a Universal Serial Bus (USB) Mass Storage Device (MSD) based covert channel was designed to enable remote command and control of the framework. The work was validated through the design, implementation and testing of a toolset.Comment: In Proceedings AIDP 2014, arXiv:1410.322

About the CMALT Development Group (CDG)

This document introduces the CMALT Development Group (CDG) and its founding members

Draft Minutes of the CMALT Development Group Meeting on 19 May 2010

Note that the group has approved these as draft minutes ONLY