Work detail

Can Bayesian econometric methods outperform traditional econometrics in inflation forecasting?

Author: Mgr. Stráský Josef
Year: 2011 - summer
Leaders: PhDr. Jaromír Baxa Ph.D.
Work type: Doctoral
Economic Theory
Language: English
Pages: 84
Awards and prizes:
Abstract: 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 [1]).
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 Vector autoregression (BVAR). One
of the aims of the thesis is to become familiar with principles of Bayesian
econometric and be able to use Bayesian approach in various models.
In this thesis, I compared the forecasting performance of various
models by applying the Theil U-statistics. Since VAR models were able
to outperform Random Walk in pseudo out-of-sample forecasts, I undertook
an experiment with the aim to identify the best inflation predictors, that
should be included within the VAR model. For this purpose I employed a
set of almost 80 time series covering various economic indicators including
forward looking variables extracted from surveys.
I have found that unemployment is never in the set of best predictors
(rejection of Phillips curve as useful relationship), GDP measure appears only
in the long term forecast, whereas forward looking indicators are important
for shorter forecast horizons. Employing of BVAR models instead of VAR
have brought mixed results. Out of sample predictions for years 2010 and
2011 are also provided. Variants of future research are briefly discussed.
Downloadable: Rigorous Thesis Stráský
August 2022




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