Add to ORKGCo-integration and common trends are studied for time series variables, by introducing the new t-QVARMA (quasi-vector autoregressive moving average) model. t-QVARMA is an outlier-robust nonlinear score-driven model for the multivariate t-distribution. In t-QVARMA, the I(0) and I(1) components of the variables are separated in a way that is similar to the Granger-representation of VAR models. The relationship between the co-integrated federal funds effective rate and United States (US) inflation rate variables is studied for the period of July 1954 to January 2019. The in-sample statistical and out-of-sample forecasting performances of t-QVARMA are superior to those of the classical Gaussian-VAR modelBlazsek and Licht acknowledge fundi...
The purpose of this paper is to evaluate the accuracy of ex ante econometric model forecasts of four...
Relevant works from the literature on crude oil market use structural vector autoregressive(SVAR) mo...
We propose a new class of score-driven time series models that allows for a more flexible weighting ...
Co-integration and common trends are studied for time series variables, by introducing the new t-QVA...
We study co-integration and common trends for time series variables, by introducing a new nonlinear ...
In this paper, we introduce a new model by extending the dynamic conditional score(DCS) model of the...
We suggest a new mechanism to detect stochastic seasonality of multivariate macroeconomic variables,...
We introduce a new joint model of expected return and volatility forecasting, namely the two-compone...
This article presents a new Qual VAR model for incorporating information from qualitative and/or dis...
We introduce the Seasonal-QVAR (quasi-vector autoregressive) model that we apply to study the relati...
We introduce the Seasonal-QVAR (quasi-vector autoregressive) model for world crude oil production an...
This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MST...
After the introductory chapter, this thesis comprises two further chapters. The main chapters i...
For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA h...
The purpose of this paper is to evaluate the accuracy of ex ante econometric model forecasts of four...
Relevant works from the literature on crude oil market use structural vector autoregressive(SVAR) mo...
We propose a new class of score-driven time series models that allows for a more flexible weighting ...
Co-integration and common trends are studied for time series variables, by introducing the new t-QVA...
We study co-integration and common trends for time series variables, by introducing a new nonlinear ...
In this paper, we introduce a new model by extending the dynamic conditional score(DCS) model of the...
We suggest a new mechanism to detect stochastic seasonality of multivariate macroeconomic variables,...
We introduce a new joint model of expected return and volatility forecasting, namely the two-compone...
This article presents a new Qual VAR model for incorporating information from qualitative and/or dis...
We introduce the Seasonal-QVAR (quasi-vector autoregressive) model that we apply to study the relati...
We introduce the Seasonal-QVAR (quasi-vector autoregressive) model for world crude oil production an...
This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MST...
After the introductory chapter, this thesis comprises two further chapters. The main chapters i...
For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA h...
The purpose of this paper is to evaluate the accuracy of ex ante econometric model forecasts of four...
Relevant works from the literature on crude oil market use structural vector autoregressive(SVAR) mo...
We propose a new class of score-driven time series models that allows for a more flexible weighting ...