Results 21 to 30 of about 3,856 (236)

Model uncertainty in Panel Vector Autoregressive models [PDF]

open access: yesEuropean Economic Review, 2014
We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units.
Koop, Gary, Korobilis, Dimitris
openaire   +8 more sources

A Hybrid VARX–SVM Framework for Financial Time Series Forecasting: Evidence from the EGX30 Index [PDF]

open access: yesالمجلة العلمية للدراسات والبحوث المالية والتجارية
Traditional econometric models, such as Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR) and Vector Autoregression with Exogenous Variables (VARX) models, have long been the cornerstone of time series analysis.
Tarek Yehia Yousef Elorbany   +1 more
doaj   +1 more source

Are life insurance futures a safe haven during COVID-19?

open access: yesFinancial Innovation, 2023
This study aims to examine whether life insurance futures can serve as a hedge against the COVID-19 pandemic and whether they have the characteristics of a safe haven under the impact of the health shocks of the COVID-19 pandemic.
Kuan-Min Wang, Yuan-Ming Lee
doaj   +1 more source

Opportunities for modelling inflation processes in Lithuania

open access: yesLietuvos Matematikos Rinkinys, 2009
Inflation is a constant and consistent increase in the general price level in the country, due to which the purchasing power of a national currency unit decreases. In practice, the measures of inflation are various price indices, such as a consumer price
Ana Čuvak, Žilvinas Kalinauskas
doaj   +1 more source

ANALISIS KEBIJAKAN MONETER DALAM MODEL MAKROEKONOMETRIK STRUKTURAL JANGKA PANJANG: STRUCTURAL COINTEGRATING VECTOR AUTOREGRESSION

open access: yesBuletin Ekonomi Moneter dan Perbankan, 2006
The paper analyzes the monetary policy behavior by developing a long-run structural macroeconometric model; the Structural Cointegrating Vector Autoregression. The model is empirically proposed by Garratt et. al.
Solikin M. Juhro
doaj   +1 more source

Bayesian Nonparametric Vector Autoregressive Models [PDF]

open access: yesSSRN Electronic Journal, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kalli, M, Griffin, JE
openaire   +3 more sources

Perilaku Overconfidence Di Bursa Efek Indonesia (BEI) (Studi kasus pada Index LQ45 periode 2014-2016)

open access: yesMedia Ekonomi dan Manajemen, 2018
This study purposed to identify overconfidence behavior investor in Indonesia Stock Exchange from 2014 until 2016. Overconfidence is a psychological bias that can cause investors to excessive trading as the effect of the belief that they have specific ...
Indri Hartiyaningsih, Yanuar Rachmansyah
doaj   +1 more source

Panel Vector Autoregressive Models: A Survey [PDF]

open access: yesSSRN Electronic Journal, 2013
This paper provides an overview of the panel VAR models used in macroeconomics and finance. It discusses what are their distinctive features, what they are used for, and how they can be derived from economic theory. It also describes how they are estimated and how shock identification is performed, and compares panel VARs to other approaches used in ...
Canova, Fabio, Ciccarelli, Matteo
openaire   +4 more sources

Inflation Forecasting: The Practice of Using Synthetic Procedures

open access: yesМир новой экономики, 2019
The article contains a review of inflation forecasting models, including the most popular class of models as one-factor models: random walk, direct autoregression, recursive autoregression, stochastic volatility with an unobserved component and of the ...
E.  V.  Balatskiy, M. A. Yurevich
doaj   +1 more source

A Time-Varying Bayesian Compressed Vector Autoregression for Macroeconomic Forecasting

open access: yesIEEE Access, 2020
This paper presents macroeconomic forecasting by using a time-varying Bayesian compressed vector autoregression approach. We apply a random compression by using projection matrix to randomly select predictive variables in vector autoregression (VAR), and
Nattapol Aunsri   +1 more
doaj   +1 more source

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