Volume 236, Issue 3 -

Results of multi-party bargaining are usually described by concepts from cooperative game theory, in particular by the core. In one-on-one matching, core allocations are stable in the sense that no pair of unmatched or otherwise matched players can improve their incomes by forming a match. Because of incomplete information and bounded rationality, it is difficult to adopt a core allocation immediately. Theoretical investigations cope with the problem of whether core allocations can be adopted in a stochastic process with repeated re-matching. In this paper, we investigate sequences of matching with data from an experimental 2×2 labor market with wage negotiations. This market has seven possible matching structures (states) and is additionally characterized by the negotiated wages and profits. First, we describe the stochastic process of transitions from one state to another including the average transition times. Second, we identify different influences on the process parameters as, for example, the difference of incomes in a match. Third, allocations in the core should be completely durable or at least more durable than comparable out-of-core allocations, but they are not. Final bargaining results (induced by a time limit) appear as snapshots of a stochastic process without absorbing states and with only weak systematic influences.

This paper compares determinants of banking sector crises using an early warning system approach in a diverse cross-country panel. We cover 152 countries using annual data from 1990 to 2011. We provide three main contributions: (i) accommodating different influences on banking sector fragility (banking sector, macroeconomic and structural features) in a wide country panel; (ii) introducing a variable accounting for geographical contagion effects; and (iii) cross country comparison based on two subsamples: advanced and developing countries. We find systematic differences between country groups and evidence for the influence of geographical contagion effects. The specification produces reliable early warning signals to detect crisis events on the banking sector level.

Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, nonlinear models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are extended by means of nonlinear transformations (quadratic, cubic, sine, exponential functions) of lagged time series observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and financial time series for 10 European countries. Findings suggest that these models, including nonlinear transformation of lagged autoregression residuals, are able to provide better forecasting results than simple linear models. Thus, it may be possible to improve the forecasting accuracy of linear models by including nonlinear components. This is especially true for time series that are positively tested for nonlinear characteristics and longer forecast horizons.