Real Time Macroeconomics LLC is a group of economic researchers and statisticians creating new economic indicators using online data such as job openings, layoff announcements and self-reported wages.
Government economic data has historically been released at a lag of the actual reporting period due to operational issues around collecting offline data primarily through surveys.
We aim to supplement this government economic data with similar online indicators (which are highly correlated with their government counterparts) and provide such data to financial market participants, economists and policymakers.
Real-Time Economic Data For Forecasting
Our data is used in combination with other data sources in proprietary econometric models to forecast macroeconomic releases such as the monthly Bureau of Labor Statistics non-farm payroll (NFP) numbers.
Real Time Macroeconomic Data Collection
We scrape online economic data on a daily basis in an attempt to replicate various indicates provided by the Bureau of Labor Statistics.
BLS Non-Farm Payroll (NFP) Model Forecast
We provide a monthly forecast of the Bureau of Labor Statistics non-farm payroll (NFP) release to our subscribers.
Our data is webscraped at the firm level and then aggregated to create macroeconomic indicators. Granular data and the ability to drill-down into the microdata underlying macroeconomic indicators is one significant advantage of webscraped data over conventional government survey driven indices.
We also provide consulting services on innovative ways to measure and detect macroeconomic trends in real-time.
Jon Hartley is a Forbes economics contributor and a World Economic Forum Global Shaper. He previously worked at Goldman Sachs in their Quantitative Investment Strategies group and as a research assistant at the Federal Reserve Bank of Chicago. He graduated with a B.A. from the University of Chicago in mathematics and economics (with honors) and is an M.B.A. candidate at The Wharton School of the University of Pennsylvania.
Matt Olson is a PhD candidate at UPenn's Wharton School Statistics Department. His previously did research at the Federal Reserve Bank of Chicago and for a physician analytics start-up. He graduated from the University of Chicago with degrees in mathematics and economics.
Michael Ruston is Python and R developer with prior experience in financial modeling at J.P. Morgan, Citibank and Goldman Sachs. He graduated with a Master's in Computational Finance from Carnegie Mellon University and as well as a Master's in Applied Mathematics from University of Colorado, Boulder.