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Can Bayesian econometric methods outperform traditional econometrics in inflation forecasting?
Stráský, Josef ; Baxa, Jaromír (advisor) ; Netuka, Martin (referee)
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...
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Three essays on empirical Bayesian econometrics
Adam, Tomáš ; Komárek, Luboš (advisor) ; Feldkircher, Martin (referee) ; Herrala, Risto (referee) ; Melecký, Martin (referee)
The dissertation consists of three papers which apply Bayesian econometric techniques to monitoring macroeconomic and macro-financial developments in the economy. Its aim is to illustrate how Bayesian methods can be employed in standard areas of economic research (estimating systemic risk in the banking sectors, nowcasting GDP growth) and also in a more original area (monitoring developments in sovereign bond markets). In the first essay, we address a task which analytical departments in central banks or commercial banks face very often - nowcasting foreign demand of a small open economy. On the example of the Czech economy, we propose an approach to nowcast foreign GDP growth rates for the Czech economy. For presentation purposes, we focus on three major trading partners: Germany, Slovakia and France. We opt for a simple method which is very general and which has proved successful in the literature: the method based on bridge equation models. A battery of models is evaluated based on a pseudo-real- time forecasting exercise. The results for Germany and France suggest that the models are more successful at backcasting, nowcasting and forecasting than the naive random walk benchmark model. At the same time, the various models considered are more or less successful depending on the forecast horizon....
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Three essays on empirical Bayesian econometrics
Adam, Tomáš ; Komárek, Luboš (advisor) ; Feldkircher, Martin (referee) ; Herrala, Risto (referee) ; Melecký, Martin (referee)
The dissertation consists of three papers which apply Bayesian econometric techniques to monitoring macroeconomic and macro-financial developments in the economy. Its aim is to illustrate how Bayesian methods can be employed in standard areas of economic research (estimating systemic risk in the banking sectors, nowcasting GDP growth) and also in a more original area (monitoring developments in sovereign bond markets). In the first essay, we address a task which analytical departments in central banks or commercial banks face very often - nowcasting foreign demand of a small open economy. On the example of the Czech economy, we propose an approach to nowcast foreign GDP growth rates for the Czech economy. For presentation purposes, we focus on three major trading partners: Germany, Slovakia and France. We opt for a simple method which is very general and which has proved successful in the literature: the method based on bridge equation models. A battery of models is evaluated based on a pseudo-real- time forecasting exercise. The results for Germany and France suggest that the models are more successful at backcasting, nowcasting and forecasting than the naive random walk benchmark model. At the same time, the various models considered are more or less successful depending on the forecast horizon....
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Can Bayesian econometric methods outperform traditional econometrics in inflation forecasting?
Stráský, Josef ; Baxa, Jaromír (advisor) ; Netuka, Martin (referee)
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...
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