Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Piribauer, Philipp"'
Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncert
Externí odkaz:
http://epub.wu.ac.at/6839/1/1805.10822.pdf
This paper uses a global vector autoregressive (GVAR) model to analyze the relationship between FDI inflows and output dynamics in a multi-country context. The GVAR model enables us to make two important contributions: First, to model international l
Externí odkaz:
http://epub.wu.ac.at/5427/3/wp239.pdf
The spatial autoregressive (SAR) model is extended by introducing a Markov switching dynamics for the weight matrix and spatial autoregressive parameter. The framework enables the identification of regime-specific connectivity patterns and strengths
Externí odkaz:
http://arxiv.org/abs/2310.19557
We propose an econometric framework to construct projections for per capita income growth and human capital for European regions. Using Bayesian methods, our approach accounts for model uncertainty in terms of the choice of explanatory variables, the
Autor:
Wanzenböck, Iris, Piribauer, Philipp
In this paper we estimate space-time impacts of the embeddedness in R&D networks on regional knowledge production by means of a dynamic spatial panel data model with non-linear effects for a set of 229 European NUTS-2 regions in the period 1999-2009.
Externí odkaz:
http://epub.wu.ac.at/4652/1/wp207.pdf
In this paper we present an econometric framework aimed at obtaining projections of income growth in Europe at the regional level. We account for model uncertainty in terms of the choice of explanatory variables, as well as the nature of the spatial
Externí odkaz:
http://epub.wu.ac.at/4583/1/wp198.pdf
This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal
Externí odkaz:
http://epub.wu.ac.at/4584/1/wp199.pdf
This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a dataset consisting of monthly data on global stock indices the BVAR model inherently incorporates co-
Externí odkaz:
http://epub.wu.ac.at/4318/1/wp184.pdf
This paper considers the most important aspects of model uncertainty for spatial regression models, namely the appropriate spatial weight matrix to be employed and the appropriate explanatory vari- ables. We focus on the spatial Durbin model (SDM) sp
Externí odkaz:
http://epub.wu.ac.at/4418/1/piribauer_fischer.pdf
This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatia
Externí odkaz:
http://epub.wu.ac.at/4013/1/wp158.pdf