In this note we develop a Taylor rule based empirical exchange rate model for eleven major currencies that endogenously determines the number of structural breaks in the coefficients. Using a constant parameter specification and a standard time-varying parametermodel as competitors reveals that our flexible modeling framework yields more precise density forecasts for all major currencies under scrutiny over the last 24 years. / Series: Department of Economics Working Paper Series

We propose a large-scale Bayesian VAR model with factor stochastic volatility to investigate the macroeconomic consequences of international uncertainty shocks on the G7 countries. The factor structure enables us to identify an international uncertainty shock by assuming that it is the factor most correlated with forecast errors related to equity markets and permits fast sampling of the model. Our findings suggest that the estimated uncertainty factor is strongly related to global equity price volatility, closely tracking other prominent measures commonly adopted to assess global uncertainty. The dynamic responses of a set of macroeconomic and financial variables show that an international uncertainty shock exerts a powerful effect on all economies and variables under consideration. / Series: Department of Economics Working Paper Series

This paper provides an empirical assessment of whether the world economy has become smaller in terms of economic distance over the last decades. We adopt a cross-sectional spatial econometric approach, relating domestic output volatility to (distance-weighted averages of) other countries' output volatility, using a sample of 135 countries and rolling 10-year time windows over the period 1955 to 2006. Using descriptive measures, test statistics, and spatial econometric estimates, we find that cross-country interdependence was virtually insignificant in the early post-war period but has increased strongly from the mid-1960s to the mid-1980s and remained at a high level since then. Results for the most recent period suggest that common shocks to output volatility have a magnified impact and roughly quadruplicate through international spillover effects, which are transmitted through both trade and financial openness.

Bitcoin is an unregulated digital currency originally introduced in 2008 without legal tender status. Based on a decentralized peer-to-peer network to confirm transactions and generate a limited amount of new bitcoins, it functions without the backing of a central bank or any other monitoring authority. In recent years, Bitcoin has seen increasing media coverage and trading volume, as well as major capital gains and losses in a high volatility environment. Interestingly, an analysis of Bitcoin returns shows remarkably low correlations with traditional investment assets such as other currencies, stocks, bonds or commodities such as gold or oil. In this paper, we shed light on the impact an investment in Bitcoin can have on an already well-diversified investment portfolio. Due to the non-normal nature of Bitcoin returns, we do not propose the classic mean-variance approach, but adopt a Conditional Value-at-Risk framework that does not require asset returns to be normally distributed. Our results indicate that Bitcoin should be included in optimal portfolios. Even though an investment in Bitcoin increases the CVaR of a portfolio, this additional risk is overcompensated by high returns leading to better return-risk ratios.

This paper analyzes the link between relative market value of representative subsets of athletes in the National Basketball Association (NBA) and individual wages. NBA athletes are categorized with respect to multiple performance characteristics utilizing the k-means algorithm to cluster observations and a group's market value is calculated by averaging real annual salaries. Employing GMM estimation techniques to a dynamic wage equation, we find a statistically significant and positive effect of one-period lagged relative market value of an athlete's representative cluster on individual wages after controlling for past individual performance. This finding is consistent with the theory of prototype heuristic, introduced by Kahneman and Frederick (2002), that NBA teams' judgment about an athlete's future performance is based on a comparison of the player to a prototype group consisting of other but comparable athletes. / Series: Department of Economics Working Paper Series

During the past decade, renewable energy sources have become an indispensable pillar in European electricity generation. This paper aims at examining if the increasing importance of renewables stimulates investment in European power transmission networks. The question of interest is addressed by an error correction investment model that builds on Neoclassical theory and is further augmented by recent literary findings. Under the proposed threefold estimation strategy, the share of renewables is not found to significantly influence investment spending when the full set of transmission system operators are considered. However, a slight and justified sample restriction leads to the conclusion that a rising share of renewable energy sources substantially increases investment in power transmission networks. / Series: Department of Economics Working Paper Series

This paper considers Bayesian estimation of the threshold vector error correction (TVECM) model in moderate to large dimensions. Using the lagged cointegrating error as a threshold variable gives rise to additional difficulties that are typically solved by relying on large sample approximations. Relying on Markov chain Monte Carlo methods we circumvent these issues by avoiding computationally prohibitive estimation strategies like the grid search. Due to the proliferation of parameters we use novel global-local shrinkage priors in the spirit of Griffin and Brown (2010). We illustrate the merits of our approach in an application to five exchange rates vis-á-vis the US dollar and assess whether a given currency is over or undervalued. Moreover, we perform a forecasting comparison to investigate whether it pays off to adopt a non-linear modeling approach relative to a set of simpler benchmark models. / Series: Department of Economics Working Paper Series

Export, migration and costs of trade: evidence from Central European firms, Regional Studies. This article analyses the link between immigration and trade at the firm level, utilizing information on the export activities of 8300 firms located in different Central European countries (Austria, Czech Republic, Slovakia and Hungary) for various export markets as well as regional data on immigration. The empirical analysis suggests a strong, economically meaningful and statistically significant impact of immigration on the export propensity (extensive margin), whereas the influence on firms' export volumes (intensive margin) is much smaller. This leads to the conclusion that immigrants promote export activities to their home countries mainly by reducing fixed costs of trade.

We highlight the importance of "centrality" for pricing. Firms characterized by a more central position in a spatial network are more powerful in terms of having a stronger impact on their competitors' prices and on equilibrium prices. These propositions are derived from a simple theoretical model and investigated empirically for the retail gasoline market of Vienna, Austria. We compute a measure of network centrality based on the locations of gasoline stations in the road network. Results from a spatial autoregressive model show that prices of gasoline stations are more strongly correlated with prices of central competitors.

The paper estimates a dynamic macroeconometric model for the US economy that captures two important features commonly observed in the study of the US business cycle, namely the strong co-movement of key macroeconomic quantities, and the distinction between expansionary and recessionary phases. The model extends the factor-augmented vector autoregressive model of Bernanke et al. (2005) by combining Markov switching with factor augmentation, modeling the Markov switching probabilities endogenously, and adopting a full Bayesian estimation approach which uses shrinkage priors for several parts of the parameter space. Exploiting a large data set for the US economy ranging from 1971:Q1 to 2014:Q2, the model is applied to measure not only the dynamic effects of unconventional monetary policy within distinct stages of the business cycle, but also the dynamic response of the recession probabilities, based on conducting counterfactual simulations. The results obtained provide new insights on the effect of monetary policy under changing business cycle phases, and highlight the importance of discriminating between expansionary and recessionary phases of the business cycle when analyzing the impact of monetary policy on the macroeconomy. (authors' abstract) / Series: Working Papers in Regional Science