Lilia Maliar is professor of economics at the Graduate Center of the City University of New York (CUNY). She and her coauthors have proposed a novel class of numerical methods that combines projection and stochastic-simulation techniques and that can accurately and reliably solve dynamic economic models with a large number of state variables. The applications include large-scale new Keynesian models, life-cycle models, heterogeneous-agent models, asset pricing models, multi-sector models, multi-country models, default risk models, and models of climate change, among others. Her research interests also include economic theory, macroeconomics, machine learning, deep learning, growth and development, dynamic games, transition economies, and international trade.

Lilia Maliar’s research on numerical methods for dynamic economic models has appeared in numerous economic journals, including Econometrica, Journal of Business and Economic Statistics, Journal of Economic Dynamic and Control, Journal of Money Banking and Credit, Quantitative Economics, Review of Economic Dynamics, and Computational Economics; she contributed a chapter to Handbook of Computational Economics; she currently serves as associate editor for the Quantitative Economics journal of the Econometric Society. She advises the Bank of Canada on its large-scale ToTEM macroeconomic model for projection and policy analysis, and she serves as a research fellow of the Center for Economic Policy Research.

Maliar came to the Graduate Center, CUNY, from Stanford University, where she was a visiting associate frofessor in the Department of Economics and a visiting fellow at the Hoover Institution. She has taught at the University of Alicante and University of Pompeu Fabra. Maliar received a BS in economics from Dnepropetrovsk State University in 1991, an MA from Central European University, and a PhD in economics from University of Pompeu Fabra in 1999.

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