Může Bayesovská ekonometrie překonat tradiční ekonometrické modely v předpovídání inflace?

Stráský, Josef

Forecasting of inflation has become crucial for both policy makers and private agents who try to understand and react to Central Bank decisions because many Central Banks implemented inflation targeting rules instead of control of monetary aggregates. Inflation forecasting is considered to be very complicated issue because univariate regression models and structural macroeconomic models are usually outperformed by naive random walk model. This work is intended for forecasting inflation in the Czech Republic by employing Bayesian econometric method (namely Bayesian vector autoregression - BVAR). Bayesian methods proved to be useful in inflation forecasting in developed countries (Fabio Canova: G-7 Inflation Forecasts: Random Walk, Phillips Curve or What Else?, 2007). Bayesian econometrics is one of the fast developing fields of econometrics for past two decades. In the centre of the approach is Bayesian probabilistic theory based on conditional probabilities. This probabilistic approach is, however, computationally demanding. Fast computer evolution enables wide applications of Bayesian models. Model estimations are based on combining information from some prior beliefs and from the data. Many different sorts of models have their Bayesian variants (e.g. OLS) but the emphasis in this work is on Bayesian...

host :: přihlásit Digitální repozitář
		Hledej		Nový záznam		Nápověda		O repozitáři