7th Vienna Workshop on High Dimensional Time Series
in Macroeconomics and Finance
IHS, Vienna | 28-29 May 2026
Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
| 9:00am - 10:00am |
P3: Plenary 3 - Serena Ng Location: Lecture Room E02 |
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| 10:00am - 10:30am |
Coffee Break |
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| 10:30am - 12:00pm |
PS-4A Location: Lecture Room E02 Chair: Leopold Sögner, Institute for Advanced Studies Nowcasting Low-Income Countries Through Global Linkages 1: Johns Hopkins University; 2: International Moentary Fund Quantifying Demand Shocks in the Green and Digital Transition 1: University of Milan Bicocca + FEEM, Italy; 2: University of Milan + FEEM, Italy Unfolding Regional Business Cycles: Factor Models for Three-Way State-Level Tensors University of Alberta, Canada |
PS-4B Location: Lecture Room SR 101 Bridging Dense and Sparse Models in High-Dimensional Quantile Regression Universitat Pompeu Fabra, Spain High-Dimensional Nonparametric Local Projections 1: The Hong Kong University of Science and Technology; 2: UC Davis and San Francisco FED; 3: Bocconi University; 4: Università Cattolica Convex validation of kernel ridge regression 1: EPFL; 2: Universita' della Svizzera Italiana, Switzerland |
| 12:00pm - 1:30pm |
Lunch Break |
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| 1:30pm - 3:00pm |
PS-5A Location: Lecture Room E02 Common factors in large panels of equity options 1: Neoma Business School, France; 2: Erasmus University Rotterdam; 3: Luiss University; 4: IESEG School of Management The US yield curve with cointegration and a time-varying factor structure 1: Università degli Studi dell'Insubria, Italy; 2: Fondazione Eni Enrico Mattei, Italy; 3: Università Politecnica delle Marche, Italy Common Factors in Currency Characteristics University of Innsbruck, Austria |
PS-5B Location: Lecture Room SR 101 Global Interdependencies in Time Series: Bringing together GVAR and Matrix Autoregressive Models Bielefeld University, Germany Score Autoregressive Models 1: Department of Economics, Ca' Foscari University of Venice; 2: Faculty of Economics, Cambridge University; 3: Department of Economics, Ca' Foscari University of Venice Moderate Time-Varying Parameter VARs 1: Örebro University School of Business, Örebro, Sweden.; 2: Fondazione ENI Enrico Mattei, Milan, Italy |
| 3:00pm - 3:45pm |
Poster 2 Location: Lecture Room E02 The Predictive Power of Fedspeak 1: Bloomberg, United States; 2: Bloomberg, Germany Are there asymmetries in euro area monetary policy? 1: WU Vienna; 2: Oesterreichische Nationalbank, Austria Building Climate Indices of Maximal Macroeconomic Relevance via the Assemblage VAR 1: University Bocconi, Italy; 2: Université du Québec à Montréal; 3: ETH Zurich Per capita Income Convergence in Central Europe: Does the 2004 Accession to the European Union matter? University of Warsaw, Poland Forecast Combination for Tail Risk with Regulator-Aware Decision Trees 1: Erasmus University Rotterdam; 2: University of Birmingham, United Kingdom Monthly GDP estimates for the euro area and its countries 1: European Central Bank, Germany; 2: Deutsche Bundesbank; 3: University of Leicester Comparison in Dynamic Forecasting: A Bayesian LASSO State-Space and Bayesian Factor VAR Analysis University College Dublin, Ireland Exploiting common volatility dynamics in high-dimensional portfolio selection University of Regensburg, Germany Behind the Curve: How the Fed Missed Inflation Risks Using a High-Dimensional Distributional VAR (HiDVAR) 1: Institute for Monetary and Financial Stability, Germany; 2: Goethe University Frankfurt, Germany Nelson–Siegel Autoencoders for Global Yield Curve Forecasting 1: Erasmus School of Economics, Netherlands, The; 2: Neoma Business School, France Signal-Selected Sparse Equal-Weight Portfolios 1: University of Hagen, Germany,; 2: Aalborg University Business School, Denmark |
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| 3:45pm - 5:45pm |
PS-6A Location: Lecture Room E02 GENERALLY SHIFTING COEFFICIENTS IN INTERACTIVE EFFECTS PANEL DATA MODELS AND THE UNIQUE COVID-19 REGIME 1: Örebro University, Sweden; 2: Lund University, Sweden Change Point and Bubble Detection in Time Series Using Mixed Integer Programming 1: HSE University, Russian Federation; 2: University of Sydney; 3: University of South Florida Determinants and forecasting of German day-ahead electricity prices and volatility University of Cagliari, Italy |
PS-6B Location: Lecture Room SR 101 Fast Factor Extraction for Mixed Data Types TU Dortmund University, Germany A geometric approach to factor model identification Study Center Gerzensee and University of Basel, Switzerland Infinite Dimensional Factor Spaces by Subspace Methods 1: Department of Statistics and Operations Research, University of Vienna, Austria; 2: Department of Economics, Università di Bologna |
| 5:30pm - 6:30pm |
P4: Plenary 4 - Domenico Giannone Location: Lecture Room E02 |
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| 7:00pm - 9:00pm |
Reception: Reception Location: Plutzer Bräu |
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