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A Stable Model for Euro Area Money Demand : Revisiting the Role of Wealth

Beyer, Andreas - 2009

In this paper we present an empirically stable money demand model for Euro area M3. We show that housing wealth is an important explanatory variable of long-run money demand that captures the trending behaviour of M3 velocity, in particular its shift in the first half of this decade. We show...

Persistent link: https://www.econbiz.de/10013155103

A Cointegration Model of Money and Wealth

Assenmacher, Katrin - 2020

Extending the data set used in Beyer (2009) from 2007 to 2017, we estimate I(1) and I(2) money demand models for euro area M3. We find that the elasticities in the money demand and the real wealth relations identified previously in Beyer (2009) have remained remarkably stable throughout the...

Persistent link: https://www.econbiz.de/10012844181

VAR for VaR : Measuring Tail Dependence Using Multivariate Regression Quantiles

White, Jr., Halbert L. - 2015

This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently...

Persistent link: https://www.econbiz.de/10013020592

Price Dividend Ratio and Long-Run Stock Returns : A Score Driven State Space Model

Delle Monache, Davide - 2020

In this paper we develop a general framework to analyze state space models with time-varying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

Persistent link: https://www.econbiz.de/10012842441

Bayesian Inference in Cointegrated VAR Models : With Applications to the Demand for Euro Area M3

Warne, Anders - 2021

The paper considers a Bayesian approach to the cointegrated VAR model with a uniform prior on the cointegration space …. Building on earlier work by Villani (2005b), where the posterior probability of the cointegration rank can be calculated … the marginal likelihood identity is used for calculating these probabilities, a point estimator of the cointegration space …

Persistent link: https://www.econbiz.de/10013317369

Estimation of Impulse Response Functions When Shocks are Observed at a Higher Frequency than Outcome Variables

Chudik, Alexander; Georgiadis, Georgios - 2021

This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable),...

Persistent link: https://www.econbiz.de/10013315353

Statistical Tests and Estimators of the Rank of a Matrix and their Applications in Econometric Modelling

Camba-Méndez, Gonzalo; Kapetanios, George - 2021

Testing and estimating the rank of a matrix of estimated parameters is key in a large variety of econometric modelling scenarios. This paper describes general methods to test for and estimate the rank of a matrix, and provides details on a variety of modelling scenarios in the econometrics...

Persistent link: https://www.econbiz.de/10013316643

A Stable Model for Euro Area Money Demand: Revisiting the Role of Wealth

Beyer, Andreas - 2009

Persistent link: https://www.econbiz.de/10011605157

Large Bayesian VARs

Banbura, Marta - 2008

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

Persistent link: https://www.econbiz.de/10012769281

Forecasting Using a Large Number of Predictors : Is Bayesian Regression a Valid Alternative to Principal Components?

De Mol, Christine; Giannone, Domenico; Reichlin, Lucrezia - 2021

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

Persistent link: https://www.econbiz.de/10013317338