Abstract: This paper proposes LASSO estimation specific for panel vector autoregressive (PVAR) models. The penalty term allows for shrinkage for different lags, for shrinkage towards homogeneous coeficients across panel units, for penalization of lags of variables belonging to another cross-sectional unit, and for varying penalization across equations. The penalty parameters therefore build on time series and cross-sectional properties that are commonly found in PVAR models. Simulation results point towards advantages of using the proposed LASSO for PVAR models over ordinary least squares in terms of forecast accuracy. An empirical forecasting application with five countries support these findings.
presented at (selected): EEA-ESEM 2017, Lisbon, Portugal; Barcelona GSE Summer Forum Time Series Econometrics and Applications for Macroeconomics and Finance, Barcelona, Spain; Econometrics Seminar, University of Sydney, Australia; Econometrics Seminar, University of Melbourne, Australia; Spring Meeting of Young Economist 2017, Halle, Germany; VfS Annual Conference 2017, Vienna, Austria
Abstract: The paper introduces the stochastic search variable selection for panel vector autoregressive models (SSVSP). The proposed selection prior allows for a data-based restriction search ensuring the estimation-feasibility. The SSVSP differentiates between domestic and foreign variables, thereby allowing a flexible panel structure and extending Koop and Korobilis's S4 to a restriction search on single elements. Absent a matrix structure for restrictions, a Monte Carlo simulation shows that SSVSP outperforms S4 in terms of deviation from the true values. Furthermore, a forecast exercise for G7 countries demonstrates that forecast performance improves for SSVSP focusing on sparsity in form of no dynamic interdependencies.
presented at (selected): IAAE 2016 Annual Conference, Milan, Italy; European Seminar on Bayesian Econometrics, Venice, Italy; CFE, Seville, Spain; BAM RG Seminar, University of Melbourne, Australia; VfS Annual Conference 2016, Augsburg, Germany
Abstract: This paper assesses the macroeconomic effects of international monetary policy transmission for the United States, the United Kingdom and the Euro area. We use a Bayesian Proxy Panel SVAR in order to capture international linkages and to trace the dynamic responses of the macroeconomic variables. A specific selection prior incorporating the panel dimension allows the estimation of the large number of parameters in the PVAR model. The monetary policy shocks of the three regions are identified via changes in daily future contracts around policy announcement dates. We use changes in stock prices as second proxies combined with sign restrictions to disentangle central bank information shocks from the monetary policy surprises.
presented at (selected): ESOBE 2019, St Andrews, Scotland; CFE, London, England; FU Berlin Research Seminar