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Structural Estimation of Dynamic Macroeconomic Models using Higher-Frequency Financial Data

Max Ole Liemen, Michel van der Wel () and Olaf Posch ()
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Max Ole Liemen: Universität Hamburg

No 1049, 2018 Meeting Papers from Society for Economic Dynamics

Abstract: In this paper we show how high-frequency financial data can be used in a combined macro-finance framework to estimate the underlying structural parameters. Our formulation of the model allows for substituting macro variables by asset prices in a way that enables casting the relevant estimation equations partly (or completely) in terms of financial data. We show that using only financial data allows for identification of the majority of the relevant parameters. Adding macro data allows for identification of all parameters. In our simulation study, we find that it also improves the accuracy of the parameter estimates. In the empirical application we use interest rate, macro, and S&P500 stock index data, and compare the results using different combinations of macro and financial variables.

Date: 2018
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-mst
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