Essays in Bayesian Macroeconometrics and Forecasting

Forecasting macroeconomic conditions in real-time is a crucial prerequisite for the conduct of economic policy. The interest in and importance of timely and accurate forecasts have led to active research in the areas of macroeconomics and econometrics. My dissertation makes a contribution to three strands in this literature:

i) the improvement of macroeconometric forecasting models by considering the question
of variable selection in the context of large factor models,
ii) the econometric methodology by extending so-called precision samplers to applications
with missing observations and
iii) the incorporation of external information into macroeconometric models by evaluating forecasts from a large macroeconometric model conditional on professional forecasters’ view on key variables like the growth rate of the gross domestic product (GDP) and consumer price index (CPI) inflation.

Common to all my papers and in line with a growing trend in the field is the use of Bayesian estimation techniques. Moreover, the forecast evaluations are conducted in real-time, i.e. they exactly replicate the information set available at the time the forecasts were made, thus yielding a more accurate measure of the predictive performance.


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