Asger Lunde is Director at Copenhagen Economics and Professor of Economics at Aarhus University.
As a consultant, Asger Lunde helps clients in data science, competition, damage claims and Market Abuse cases. He acts as an expert witness in court. He is an expert in econometrics with particular focus on harnessing big data for competition and financial economics. His sectoral experience spans industrial production, transport, energy and financials. In his academic research, Asger has done extensive work in high frequency financial econometrics. Based on his contributions he listed among the world’s 100 most influential scientific minds within economics and business in 2014 (Thomson Reuters 2014). His main contributions to econometrics include the Model Confidence Set and the Realized Kernel Estimator. He is an associate editor for the Journal of Business and Economic statistics and the Journal of Financial Econometrics.
Including news data in forecasting the macroeconomic performance
This paper studies forecasting of Chinese macroeconomic time series using a large number of prediction variables. We investigate what is the extent of improvement of forecasts when news sentiment indexes are included among the predictors. Due to large number of predictors we summarize them with a smaller subset of indexes that are build with principal component analysis. An approximate dynamic factor model is then fit on these indexes and used for 3-, 6- and 12-month-ahead forecasts for 4 Chinese macroeconomic time series (Balance of Payments, Exchange rate with US dollar, GDP and Unemployment rate). In total we use 132 predictors from various sources ranging from 2000 through 2017. The results suggest that forecasts obtained with this method outperform univariate autoregressions and in shorter prediction horizon news indexes improve the forecasts.