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Vector autoregressions have steadily gained in popularity since their introduction in econometrics 25 years ago. A drawback of the otherwise fairly well developed methodology is the inability to incorporate prior beliefs regarding the system's steady state in a satisfactory way. Such prior...
Persistent link: https://www.econbiz.de/10011585058
In 2016 the Central Bank of Argentina began to announce inflation targets. In this context, providing authorities with good estimates of relevant macroeconomic variables is crucial for making pertinent corrections in order to reach the desired policy goals. This paper develops a group of models...
Persistent link: https://www.econbiz.de/10011882797
During the year 2016, the Central Bank of Argentina has begun to announce inflation targets. In this context, providing the authorities of good estimates of relevant macroeconomic variables turns out to be crucial to make the pertinent corrections to reach the desired policy goals. This paper...
Persistent link: https://www.econbiz.de/10011846246
Forecast models with large cross-sections are often subject to overparameterization leading to unstable parameter estimates and hence inaccurate forecasts. Recent articles suggest that a large Bayesian vector autoregression (BVAR) with sufficient prior information dominates competing approaches....
Persistent link: https://www.econbiz.de/10010342246
Recent articles suggest that a Bayesian vector autoregression (BVAR) with shrinkage is a good forecast device even when the number of variables is large. In this paper we evaluate different variants of the BVAR with respect to their forecast accuracy for euro area real GDP growth and HICP...
Persistent link: https://www.econbiz.de/10010257225
COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that … volatility. For forecasting, the choice among outlier-robust error structures is less important, however, when a large cross …
Persistent link: https://www.econbiz.de/10013472790
that, in this case, adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR …
Persistent link: https://www.econbiz.de/10014470036
logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances … of the predictive densities. For illustrative purposes we apply it to combine White Noise and GARCH models to forecast …
Persistent link: https://www.econbiz.de/10010326049
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...
Persistent link: https://www.econbiz.de/10005002781
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...
Persistent link: https://www.econbiz.de/10005091090