Mehmet Caner and big data

Economics professor Mehmet Caner arrived at Ohio State in August as part of a group of newly hired faculty affiliated with Translational Data Analytics @ Ohio State, formed to create and apply data-based solutions to global challenges. He holds a courtesy appointment in the Department of Statistics.

An econometrician, Caner’s research focus is on econometric theory related to big data problems and international finance. His recent research emphasized high dimensional econometrics, which specializes in big data related estimation, testing.

Prior to coming to Ohio State, Caner held the position of Thurman-Raytheon Distinguished Professor of Economics at North Carolina State University.

In 2010, Caner, along with Thomas Grennes (North Carolina State University), and Friederike Köhler-Geib (The World Bank), published a paper, Finding the Tipping Point – When Sovereign Debt Turns Bad, in the book, Sovereign Debt and Financial Crisis, examining the debt-growth relationship for dataset of 99 developing and developed economies from 1980 to 2008.

Caner and his colleagues addressed the questions of whether a tipping point in public debt exists and if so, how severe would the impact of public debt be on growth beyond this threshold? What happens if debt stays above this threshold for an extended period of time?

Their findings: The economy loses .017 percent in growth for every percent of the public debt-to-GDP ratio above 77 percent. The effect is even more pronounced in emerging markets where the threshold is 64 percent debt-to-GDP ratio. In these countries, the loss in annual real growth with each additional percentage point in public debt amounts to 0.02 percentage points. The cumulative effect on real GDP could be substantial.

Their work was cited by The Economist in response to earlier published findings on debt-to-GDP ratio by Carmen Reinhart and Kenneth Rogoff.

So what do we need to know about big data and economics?

“Data sets are incredibly larger now than 10 years ago,” said Caner. “Whereas years ago we worked with two to three variables . . . now we are faced with 70. The tools we’ve been using to build models don’t suffice anymore; we need a new technique.”

By way of example, Caner points out that the method of least squares — the standard approach in regression analysis — presupposes that the number of variables is very limited.

“Least squares may not be optimal in non-linear problems or in forecasting,” explained Caner.

The LASSO (Least Absolute Shrinkage and Selection Operator) regression method — a relatively new tool — may revolutionize forecasting and that is something Caner is very excited about.

“Big data is high dimensional and may have tremendous implications for big policy.”

Currently, Caner is contemplating a big data project: forecasting the euro to dollar exchange rate. In the meantime, he has several papers in process and is teaching two PhD courses – one in high dimensional econometrics.

Caner is associate editor of the Journal of Econometrics where he is also a fellow. He is associate editor of the Journal of Business and Economics Statistics; Econometric Reviews; and Studies in Nonlinear Dynamics and Econometrics. He has published more than 30 articles, in journals such as Econometrica; Journal of Econometrics; Econometric Theory; Journal of Business and Economic Statistics; Journal of International Money and Finance; and World Economy.

Caner earned an MA and a PhD in economics at Brown University. He received his BS in business administration at Middle East Technical University (METU), Ankara, Turkey.

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