Recent Econometric Techniques for Macroeconomic and Financial Data (Hardcover, 1st ed. 2021)

The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Dynamic Modeling and Econometrics in Economics and Finance, 27
Release date:	November 2020
First published:	2021
Editors:	Gilles Dufrenot • Takashi Matsuki
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	387
Edition:	1st ed. 2021
ISBN-13:	978-3-03-054251-1
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Economics > Macroeconomics > General Books > Business & Economics > Economics > Econometrics > General Books > Business & Economics > Finance & accounting > Finance > General Books > Money & Finance > General
LSN:	3-03-054251-3
Barcode:	9783030542511

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