![]() ![]() Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006." Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases,"Ģ005-42, Board of Governors of the Federal Reserve System (U.S.). Domenico Giannone & Lucrezia Reichlin & David H.Journal of Multivariate Analysis, Elsevier, vol. " Autoregressive process modeling via the Lasso procedure," " Nowcasting: the real time informational content of macroeconomic data releases,"Ģ013/6409, ULB - Universite Libre de Bruxelles. Domenico Giannone & Lucrezia Reichlin & David Small, 2008.Journal of the American Statistical Association, American Statistical Association, vol. Lanne, Markku & Saikkonen, Pentti, 2010.Ģ3717, University Library of Munich, Germany.Lanne, Markku & Saikkonen, Pentti, 2009.īank of Finland Research Discussion Papers.Journal of Monetary Economics, Elsevier, vol. " Nowcasting: The real-time informational content of macroeconomic data," Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008.One-Sided Estimation and Forecasting,"Ģ003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.ġ43, Society for Computational Economics. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003." The generalised dynamic factor model: one sided estimation and forecasting,"Ģ013/10129, ULB - Universite Libre de Bruxelles. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005." The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Lippi, Marco & Reichlin, Lucrezia & Hallin, Marc & Forni, Mario, 2002.Estimation procedures with their theoretical properties are presented and demonstrated with simulated and real examples. Probabilistic properties of the models are investigated. ![]() In this paper we follow the autoregression for modeling time series and propose a novel matrix autoregressive model in a bilinear form that maintains and utilizes the matrix structure to achieve a substantial dimensional reduction, as well as more interpretability. Although it is natural to turn the matrix observations into long vectors, then use the standard vector time series 2 models for analysis, it is often the case that the columns and rows of the matrix represent different types of structures that are closely interplayed. Dynamic transport networks with observations generated on the edges can be formed as a matrix observed over time. The observations at each quarter neatly form a matrix and are observed over consecutive quarters. For example, a set of key economic indicators are regularly reported in different countries every quarter. In finance, economics and many other fields, observations in a matrix form are often generated over time. ![]()
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