Finite Memory Distributed Systems

A distributed system model is studied, where individual agents play repeatedly against each other and change their strategies based upon previous play. It is shown how to model this environment in terms of continuous population densities of agent types. A complication arises because the population densities of different strategies depend upon each other not only through game payoffs, but also through the strategy distributions themselves. In spite of this, it is shown that when an agent imitates the strategy of his previous opponent at a sufficiently high rate, the system of equations which governs the dynamical evolution of agent populations can be reduced to one equation for the total population. In a sense, the dynamics 'collapse' to the dynamics of the entire system taken as a whole, which describes the behavior of all types of agents. We explore the implications of this model, and present both analytical and simulation results.Fixed strategy, Prisoner's dilemma, Fokker-Plank, Distributed system

Risk Shocks and Housing Markets

This paper analyzes the role of uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production. The analysis demonstrates that risk shocks to the housing production sector are a quantitatively important impulse mechanism for the business cycle. Also, we demonstrate that bankruptcy costs act as an endogenous markup factor in housing prices; as a consequence, the volatility of housing prices is greater than that of output, as observed in the data. The model can also account for the observed countercyclical behavior of risk premia on loans to the housing sector.agency costs, credit channel, time-varying uncertainty, residential investment, housing production, calibration

Risk Shocks and Housing Markets

This paper analyzes the role of uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production. The analysis demonstrates that risk shocks to the housing production sector are a quantitatively important impulse mechanism for the business cycle. Also, we demonstrate that bankruptcy costs act as an endogenous markup factor in housing prices; as a consequence, the volatility of housing prices is greater than that of output, as observed in the data. The model can also account for the observed countercyclical behavior of risk premia on loans to the housing sector.Agency costs, credit channel, time-varying uncertainty, residential investment, housing production, calibration

Time-Varying Uncertainty and the Credit Channel

We extend the Carlstrom and Fuerst (1997) agency cost model of business cycles by including time varying uncertainty in the technology shocks that affect capital production. We first demonstrate that standard linearization methods can be used to solve the model yet second moment effects still influence equilibrium characteristics. The effects of the persistence of uncertainty are then analyzed. Our primary findings fall into three broad categories. First, it is demonstrated that uncertainty affects the level of the steady-state of the economy so that welfare analyses of uncertainty that focus entirely on the variability of output (consumption) will understate the true costs of uncertainty. A second key result is that time varying uncertainty results in countercyclical bankruptcy rates – a finding which is consistent with the data and opposite the result in Carlstrom and Fuerst. Third, we show that persistence of uncertainty affects both quantitatively and qualitatively the behavior of the economy.Agency costs, Credit channel, Time-varying uncertainty

Der touristische Arbeitsmarkt. Vor, während und nach Corona

Die Nachfrage nach touristischen Dienstleistungen ist in den Jahren von 1997 bis 2019 kontinuierlich gewachsen. Dieses Wachstum hat sich auch unmittelbar in den Beschäftigungszahlen niedergeschlagen. Die grundlegenden Beschäftigungsmuster der Branche sind in diesem Zeitraum weitgehend stabil geblieben, da sich die Charakteristika von Angebot und Nachfrage nicht geändert haben: Die Nachfrage nach touristischen Dienstleistungen schwankt im Tages-, Wochen- und Jahresverlauf. Im Moment, in dem die Nachfrage anfällt, muss sie durch personalintensive, just-in-time Produktion vor Ort befriedigt werden. Die saisonal bedingten Schwankungen im Personalbedarf bedingen auch eine besondere Beschäftigungsdynamik. Die (auch international) branchenüblichen Heraufforderungen temporäre und saisonale Arbeitskräfte zu finden und Personalabgänge zu kompensieren, wurde durch das Branchenwachstum verschärft. Der wachsende Arbeitskräftebedarf konnte in den letzten Jahren vor allem durch grenzüberschreitend mobile Beschäftigte gedeckt werden, wobei dies perspektivisch durch den demographischen Wandel und die Konkurrenz durch andere Branchen erschwert wird. Im Zuge der Corona Pandemie war der Tourismus jene Branche, die am stärksten von Lockdowns betroffen war. Der Beschäftigungsrückgang im Corona Zeitraum von 2020 bis Ende 2022 war im Tourismus (ÖNACE I) im Vergleich zu anderen Wirtschaftszweigen am stärksten. Die Frage der Deckung des Personalbedarfs wurde dadurch nicht erleichtert. Der vermeintlichen kurzfristigen Entlastung stand die Herausforderung gegenüber, den Abgang langfristiger Kernbeschäftigter zu vermeiden. Ab Sommer 2022 sind nicht nur behördliche Einschränkungen weggefallen, auch die Quarantänebestimmungen, die eine Einschränkung der Nachfrageseite bedeuteten, sind weitgehend gelockert worden. Auf Basis des kurzen Nachbetrachtungszeitraums des Sommers 2022 kann Corona als temporärer Einschnitt betrachtet werden. Wie weit sich in Folge der Pandemie mittel- und langfristige Änderungen ergeben, wird sich zeigen. Weder die Rahmenbedingungen noch die Produktionsweise haben sich im Tourismus in Folge von Corona fundamental geändert. Die Herausforderung, den Arbeitskräftebedarf adäquat zu decken, besteht auch nach Corona weiter

On Modeling Risk Shocks

Within the context of a financial accelerator model, we model time-varying uncertainty (i.e. risk shocks) through the use of a mixture Normal model with time variation in the weights applied to the underlying distributions characterizing entrepreneur productivity. Specifically, we model capital producers (i.e. the entrepreneurs) as either low-risk (relatively small second moment for productivity) and high-risk (relatively large second moment for productivity) and the fraction of both types is time-varying. We show that a small change in the fraction of risky types (a change from 1% to 2% of the population) can result in a large quantitative effect or a risk shock relative to standard models. The bankruptcy rate and the risk premium in the economy are very sensitive to a change in the composition of agents and is countercyclical

Housing and Macroeconomy: The Role of Credit Channel, Risk -, Demand - and Monetary Shocks

This paper demonstrates that risk (uncertainty) along with the monetary (interest rates) shocks to the housing production sector are a quantitatively important impulse mechanism for the business and housing cycles. Our model framework is that of the housing supply/banking sector model as developed in Dorofeenko, Lee, and Salyer (2014) with the model of housing demand presented in Iacoviello and Neri (2010). We examine how the factors of production uncertainty,financial intermediation, and credit constrained households can affect housing prices and aggregate economic activity. Moreover, this analysis is cast within a monetary framework which permits a study of how monetary policy can be used to mitigate the deleterious effects of cyclical phenomenon that originates in the housing sector. We provide empirical evidence that large housing price and residential investment boom and bust cycles in Europe and the U.S. over the last few years are driven largely by economic fundamentals and financial constraints.We also find that, quantitatively, the impact of risk and monetary shocks are almost as great as that from technology shocks on some of the aggregate real variables. This comparison carries over to housing market variables such as the price of housing, the risk premium on loans, and the bankruptcy rate of housing producers

Risk Shocks and Housing Supply: A Quantitative Analysis

This paper analyzes the role of stochastic uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production and provide estimates of the time-series properties of risk shocks by using firm level productivity data. The analysis demonstrates that risk shocks to the housing production sector are a quantitatively important impulse mechanism for understanding housing price movements. Specifically, the model can match the volatility of housing prices observed in the data. It is also demonstrated that adjustment costs are important in replicating the contemporaneous correlation of housing prices with GDP and residential investment. Critically, bankruptcy costs act as an endogenous markup factor in housing prices and are an important determinant of house price volatility. However, in comparison to housing demand shocks, risk shocks have low explanatory power for real quantities. (authors' abstract

Rationale Erklärungen für Immobilienpreis Bubbles: Die Auswirkungen von Risikoschocks auf die Wohnimmobilienpreisvolatilität und die Volatilität von Investitionen in Wohnimmobilien

AbstractThe dramatic world-wide housing boom and bust cycles during the last few years are often described in the media as “bubbles” and were largely caused by irrational exuberance due to the liberalization of housing finance (i.e. credit market irregularities in the U.S.: the subprime markets and mortgage structured products). Following Dorofeenko et al (2011), this paper, however, argues that many of the business and housing stylized facts, especially, the U.S. housing price and residential investment volatilites can be explained by analyzing the role of uncertainty (risk) in the framework of a Real Business Cycle model that includes a housing sector with financial information frictions. Consequently, we show for the U.S., these large housing price and residential investment boom and bust cycles are at least were driven largely by economic fundamentals with irrationality (or psychology) at most in the background.</jats:p