This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial
(univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over
the period 1976:Q1 to 1994:Q4; and then forecasts one-to-four quarters ahead real house price growth
over the out-of-sample horizon of 1995:Q1 to 2006:Q4. The forecasts are then evaluated by comparing
them with the ones generated from an unrestricted classical Vector Autoregressive (VAR) model and
the corresponding univariate variant the same. Finally, the models that produce the minimum average
Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth
over the recent period of 2007:Q1 to 2008:Q1. The results show that the BVARs, in whatever form
they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair
job in predicting the downturn in 18 of the 19 states, however, they always under-predict the size of the
decline in the real house price growth rate – an indication of the need to incorporate the role of
fundamentals in the models.
Olivier, Laurentz Eugene; Craig, Ian K.(Elsevier, 2013-02)
The performance of a model predictive controller depends on the quality of
the plant model that is available. Often parameters in a run-of-mine (ROM)
ore milling circuit are uncertain and inaccurate parameter estimation ...
Sekgota, Mpolaeng Gilbert(University of Pretoria, 2013-05-27)
The Sustainable Restitution Support – South Africa (SRS-SA) program aimed at the development of a post-settlement support model that could be used to support beneficiaries of land reform in South Africa, especially those ...
Since the emergence of systematic science it has been recognized that a natural phenomenon can be described
by different models that vary in their complexity and their ability to capture the details of the features