In response to escalating business travel costs, many firms have formed corporate travel management departments to help control these expenses. This study examines how efficiently these departments are operating by employing a stochastic frontier technique in addition to a linear programming procedure. Both of these methods construct efficient frontiers that represent the minimum costs that need to be allocated to corporate travel given the number of trips that firms take. Any costs incurred beyond the efficient frontiers are deemed excess, and the firms are classified as inefficient. The results using both procedures indicate that the corporate travel management departments are relatively efficient. In a competitive market, it is expected that more firms will begin to use in-house travel management departments to help control the rise of travel-related expenses.
This paper examines the developments in the production efficiency of the Austrian insurance market for the period 1994-1999 using firm-specific data on life/health and non-life insurers obtained from the Austrian insurance regulatory authority. The article uses a Bayesian stochastic frontier to obtain aggregate and firm-specific estimates of production efficiency across insurer types and time. The study provides strong evidence that the process of deregulation had positive effects on the production efficiency of Austrian insurers. The life/health and non-life firms showed similar patterns of development in that they were less efficient during the years 1994-1996 and significantly more efficient in 1997-1999. If the Austrian experience is representative, similar benefits from deregulation may be expected for the Central and Eastern European countries that prepare for the accession to the European Union. Copyright The Journal of Risk and Insurance.
The US housing market has experienced significant cyclical volatility over the last twenty-five years due to major structural changes and economic fluctuations. In addition, the housing market is generally considered to be weak form inefficient. Houses are relatively illiquid, exceptionally heterogeneous, and are associated with large transactions costs. As such, past research has shown that it is possible to predict, at least partially, the time path of housing prices. The ability to predict housing prices is important such that investors can make better asset allocation decisions, including the pricing and underwriting of mortgages. Most of the prior studies examining the US housing market have employed constant coefficient approaches to forecast house price movements. However, this approach is not optimal as an examination of data reveals substantial sub-sample parameter instability. To account for the parameter instability, we employ alternative estimation methodologies where the estimated parameters are allowed to vary over time. The results provide strong empirical evidence in favor of utilizing the rolling Generalized Autoregressive Conditional Heteroskedastic (GARCH) Model and the Kalman Filter with an Autoregressive Presentation (KAR) for the parameters’ time variation. Lastly, we provide out-of-sample forecasts and demonstrate the precision of our approach. Copyright Springer Science + Business Media, Inc. 2004house prices, Kalman filter, rolling GARCH, rolling VECM,
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