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Unit root tests: The role of the univariate models implied by multivariate time series

Cappuccio, Nunzio; Lubian, Diego - In: Econometrics 4 (2016) 2, pp. 1-11

In cointegration analysis, it is customary to test the hypothesis of unit roots separately for each single time series. In this note, we point out that this procedure may imply large size distortion of the unit root tests if the DGP is a VAR. It is well-known that univariate models implied by a...

Persistent link: https://www.econbiz.de/10011755326

Selecting the lag length for the MGLS unit root tests with structural change: A warning note for practitioners based on simulations

Quineche, Ricardo; Rodríguez, Gabriel - In: Econometrics 5 (2017) 2, pp. 1-10

This is a simulation-based warning note for practitioners who use the MGLS unit root tests in the context of structural change using different selection lag length criteria. With T=100 , we find severe oversize problems when using some criteria, while other criteria produce an undersizing...

Persistent link: https://www.econbiz.de/10011755373

Testing for Unit Roots: Mexico's GDP

Diaz-Bautista, Alejandro; Ponce, Ramon A. Castillo - EconWPA - 2003

The study presents an analysis of the stochastic nature of the gross domestic product of Mexico for the period 1900-2001. Several specifications to test for the existence of unit roots are presented. The conventional tests, Dickey Fuller, Augmented Dickey Fuller and Phillips Perron, indicate...

Persistent link: https://www.econbiz.de/10005556262

A fast, accurate method for value-at-risk and expected shortfall

Krause, Jochen; Paolella, Marc S. - In: Econometrics 2 (2014) 2, pp. 98-122

exhibiting leptokurtosis, asymmetry and conditional heteroskedasticity. It is based on a GARCH-type process driven by noncentral …

Persistent link: https://www.econbiz.de/10010421303

Selection criteria in regime switching conditional volatility models

Chuffart, Thomas - In: Econometrics 3 (2015) 2, pp. 289-316

Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and …

Persistent link: https://www.econbiz.de/10011755282

Right on target, or is it? The role of distributional shape in variance targeting

Anatolyev, Stanislav A.; Khrapov, Stanislav - In: Econometrics 3 (2015) 3, pp. 610-632

Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance … volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions …

Persistent link: https://www.econbiz.de/10011755296

Generalized fractional processes with long memory and time dependent volatility revisited

Peiris, M. Shelton; Asai, Manabu - In: Econometrics 4 (2016) 3, pp. 1-21

follow GARCH and stochastic volatility (SV). Under certain regularity conditions, we give asymptotic results for the … approximate maximum likelihood estimator for the GARMA-GARCH model. We discuss a Monte Carlo likelihood method for the GARMA …

Persistent link: https://www.econbiz.de/10011755341

Copula-based vMEM specifications versus alternatives: The case of trading activity

Cipollini, Fabrizio; Engle, Robert F.; Gallo, Giampiero M. - In: Econometrics 5 (2017) 2, pp. 1-24

We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past...

Persistent link: https://www.econbiz.de/10011755372

Measuring Tail Thickness under GARCH and an Application to Extreme Exchange Rate Changes

Wagner, Niklas; Marsh, Terry A. - EconWPA - 2004

properties of adaptive tail index estimators under the class of student-t marginal distribution functions including GARCH and …

Persistent link: https://www.econbiz.de/10005407899

One-Factor-GARCH Models for German Stocks - Estimation and Forecasting -

Kaiser, Thomas - EconWPA - 1996

factor models with GARCH dynamics (GARCH(1,1)-M, IGARCH(1,1)-M, Nonlinear Asymmetric GARCH(1,1)-M and Glosten …-Jagannathan-Runkle GARCH(1,1)-M) and three different distributions for the disturbances (Normal, Student's t and Generalized Error Distribution … compared with forecasts based on individual GARCH(1,1)-M models, static factor models, naive, random walk and exponential …

Persistent link: https://www.econbiz.de/10005407963