Non-linear dimension reduction in factor-augmented vector autoregressions

This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. I argue that controlling for non-linearities between a large-dimensional dataset and the latent factors is particularly useful during turbulent times of the business cycle. In simulations, I show that non-linear dimension reduction techniques yield good forecasting performance, especially when data is highly volatile. In an empirical application, I identify a monetary policy as well as an uncertainty shock excluding and including observations of the COVID-19 pandemic. Those two applications suggest that the non-linear FAVAR approaches are capable of dealing with the large outliers caused by the COVID-19 pandemic and yield reliable results in both scenarios.Comment: JEL: C11, C32, C40, C55, E37. Keywords: Dimension reduction, machine learning, non-linear factor-augmented vector autoregression, monetary policy shock, uncertainty shock, impulse response analysis, COVID-1

Enhanced Bayesian Neural Networks for Macroeconomics and Finance

We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities and time variation for (possibly large sets of) macroeconomic and financial variables. From a methodological point of view, we allow for a general specification of networks that can be applied to either dense or sparse datasets, and combines various activation functions, a possibly very large number of neurons, and stochastic volatility (SV) for the error term. From a computational point of view, we develop fast and efficient estimation algorithms for the general BNNs we introduce. From an empirical point of view, we show both with simulated data and with a set of common macro and financial applications that our BNNs can be of practical use, particularly so for observations in the tails of the cross-sectional or time series distributions of the target variables, which makes the method particularly informative for policy making in uncommon times.Comment: JEL: C11, C30, C45, C53, E3, E44. Keywords: Bayesian neural networks, model selection, shrinkage priors, macro forecastin

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic

Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower dimensional set of latent factors. We model the relationship between inflation and these latent factors using state-of-the-art time-varying parameter (TVP) regressions with shrinkage priors. Using monthly real-time data for the US, our results suggest that adding such non-linearities yields forecasts that are on average highly competitive to ones obtained from methods using linear dimension reduction techniques. Zooming into model performance over time moreover reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle

The Prebisch-Singer theorem in the course of time : brazilian terms of trade in focus

Das Prebisch-Singer-Theorem datiert aus dem Jahr 1950, als Raul Prebisch und Hans W. Singer ihre Thesen über den Verfall der Terms of Trade und die damit einhergehende beeinträchtigte Entwicklung von unterentwickelten Ländern formulierten. Die Welt war zweigeteilt in Entwicklungsländer, die Primärgüter und Rohstoffe herstellten, und Industrieländer, welche komplexe Industriegüter erzeugten. Aufgrund der säkularen Verschlechterung der Terms of Trade und den strukturellen Unterschieden zwischen Entwicklungs- und Industrieländern war ein fairer Handel laut Prebisch und Singer nicht möglich. Mitte des 20. Jahrhunderts begannen Entwicklungsländer, darunter Lateinamerika, ihre Produktion zu industrialisieren, jedoch am langsamen Entwicklungsprozess schien sich nichts zu ändern. Eine Gegenüberstellung der Argumente von Prebisch, Singer und Lewis über die Zeit zeigt überwiegend gleichbleibende Standpunkte, angepasst an veränderte Umstände.Vor den 2000er Jahren waren Primärgüter- relativ zu Industriegüterpreisen durch einen negativen Trend gekennzeichnet. Mit der Jahrtausendwende setzte ein Aufwärtstrend ein, der mit hohen Wachstumsraten in Entwicklungsländern einherging. Im Jahre 2011 folgten die relativen Primärgüterpreise wieder ihrem allgemein negativen Trend und auch die Wachstumsraten verlangsamten sich.Diese Masterarbeit untersucht den Zusammenhang zwischen dem Bruttoinlandsprodukt und den Terms of Trade anhand Brasiliens, um den Effekt des Verfalls der Terms of Trade auf den ökonomischen Erfolg eines Landes zu erfassen. Eine Zeitreihenanalyse unter der Anwendung eines ARMAX Modells zeigt, dass beinahe 40 Prozent der Fluktuation im brasilianischen Bruttoinlandsprodukt mithilfe von Schwankungen in den Terms of Trade erklärt werden können. Die wichtigste Antriebskraft für die außergewöhnlich gute wirtschaftliche Entwicklung in den frühen 2000er Jahren war jedoch die expansive Politik, die von der brasilianischen Regierung verfolgt wurde.The Prebisch-Singer Theorem dates back to the year 1950 when Raul Prebisch and Hans W. Singer postulated their theories concerning deteriorating terms of trade and the slow development of underdeveloped countries. The world was divided into producers of primary products and raw materials versus producers of manufactured goods. According to Prebisch, Singer and Lewis the deterioration in relative commodity prices and structural differences between developed and underdeveloped countries hindered a fair division of the fruits of trade. In the mid-20th century, underdeveloped countries, such as the Latin American states, industrialised and extended their production into the field of manufactures. Still, their terms of trade deteriorated and their development process seemed to be slow. A comparison of the three economists arguments over time shows that the economists stuck to their theories, but extended them to encompass changing circumstances.Before the 2000s, the common trend for commodity prices was a decline relative to manufactured goods prices. With the turn of the millennium, an upward trend in relative commodity prices set in, together with high economic growth rates in developing countries. This again was followed by a decline in relative commodity prices and a slowdown in terms of growth rates in 2011.This thesis examines the relationship between the gross domestic product and the terms of trade for Brazil to capture the effect of deteriorating terms of trade on the economic performance of a country. Brazil belongs to the emerging market economies and is a large exporter of primary products and importer of capital goods. A time series analysis with the application of an ARMAX model reveals that nearly 40 per cent of GDP fluctuation in Brazil can be explained by movements in the terms of trade. The main trigger for the extraordinarily good economic performance in the early 2000s, however, was the expansionary policy undertaken by the Brazilian government.Karin Klieber, B.A. (Econ.)Zusammenfassungen in Deutsch und EnglischKarl-Franzens-Universität Graz, Masterarbeit, 2018(VLID)267951

Factors Influencing and Contributing to Perceived Safety of Passengers during Driverless Shuttle Rides

This study investigates the perceived safety of passengers while being on board of a driverless shuttle without a steward present. The aim of the study is to draw conclusions on factors that influence and contribute to perceived safety of passengers in driverless shuttles. For this, four different test rides were conducted, representing aspects that might challenge passengers’ perceived safety once driverless shuttles become part of public transport: passengers had to ride the shuttle on their own (without a steward present), had to interact with another passenger, and had to react to two different unexpected technical difficulties. Passengers were then asked what had influenced their perceived safety and what would contribute to it. Results show that perceived safety of passengers was high across all different test rides. The most important factors influencing the perceived safety of passengers were the shuttle’s driving style and passengers’ trust in the technology. The driving style was increasingly less important as the passengers gained experience with the driverless shuttle. Readily available contact with someone in a control room would significantly contribute to an increase in perceived safety while riding a driverless shuttle. For researchers, as well as technicians in the field of autonomous driving, our findings could inform the design and set-up of driverless shuttles in order to increase perceived safety; for example, how to signal passengers that there is always the possibility of contact to someone in a control room. Reacting to these concerns and challenges will further help to foster acceptance of AVs in society. Future research should explore our findings in an even more natural setting, e.g., a controlled mixed traffic environment

Factors Influencing and Contributing to Perceived Safety of Passengers during Driverless Shuttle Rides

This study investigates the perceived safety of passengers while being on board of a driverless shuttle without a steward present. The aim of the study is to draw conclusions on factors that influence and contribute to perceived safety of passengers in driverless shuttles. For this, four different test rides were conducted, representing aspects that might challenge passengers’ perceived safety once driverless shuttles become part of public transport: passengers had to ride the shuttle on their own (without a steward present), had to interact with another passenger, and had to react to two different unexpected technical difficulties. Passengers were then asked what had influenced their perceived safety and what would contribute to it. Results show that perceived safety of passengers was high across all different test rides. The most important factors influencing the perceived safety of passengers were the shuttle’s driving style and passengers’ trust in the technology. The driving style was increasingly less important as the passengers gained experience with the driverless shuttle. Readily available contact with someone in a control room would significantly contribute to an increase in perceived safety while riding a driverless shuttle. For researchers, as well as technicians in the field of autonomous driving, our findings could inform the design and set-up of driverless shuttles in order to increase perceived safety; for example, how to signal passengers that there is always the possibility of contact to someone in a control room. Reacting to these concerns and challenges will further help to foster acceptance of AVs in society. Future research should explore our findings in an even more natural setting, e.g., a controlled mixed traffic environment