The prisoners' dilemma: A game theoretic approach to vehicle safety

This paper assessed the policy implications of the changing demand for passenger vehicles in Australia and debunked the myth that bigger vehicles are safer. In particular, we examined the increasing demand for small cars and four-wheel drive using the classic prisoners' dilemma framework in game theory. We found that the current emphasis on occupant protection may result in a pareto inferior outcome whereas a shift in the emphasis towards non-aggressiveness of a vehicle would result in a pareto superior outcome. Among the pure strategy equilibria, the one with only small cars provides the lowest overall level of road trauma. Furthermore, we found no mixed strategy equilibrium that would produce a lower level of trauma than the pure strategy equilibria, implying that mixing vehicle type would definitely increase road trauma. In a mixed fleet, however, medium cars produced the least trauma and thus were the safest type of passenger vehicle

Financial Variables as Predictors of Real Output Growth

We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We discover that adding low frequency stock returns (up to annual returns, depending on forecast horizon) to a quarterly AR(1) model improves forecasts of output growth.Forecasting, Mixed Frequencies, Functional linear regression

Mixing Frequencies : Stock Returns as a Predictor of Real Output Growth

We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We find that our mixed frequency models perform well in forecasting real output growth.Forecasting, Mixed Data Sampling, Functional linear regression, Test for Superior Predictive Ability

Dynamic Regressions with Variables Observed at Different Frequencies

We consider the problem of formulating and estimating dynamic regression models with variables observed at different frequencies. The strategy adopted is to define the dynamics of the model in terms of the highest available frequency, and to apply certain lag polynomials to transform the dynamics so that the model is expressed solely in terms of observed variables. A general solution is provided for models with monthly and quarterly observations. We also show how the methods can be extended to models with quarterly and annual observations, and models combining monthly and annual observations.

Global and Regional Sources of Risk in Equity Markets: Evidence from Factor Models with Time-Varying Conditional Skewness

This study examines the influence of global and regional factors on the conditional distribution of stock returns from six Asian markets, using factor models in which unexpected returns comprise global, regional and local shocks. Besides conditional heteroskedasticity, the models allow shocks to have time-varying conditional skewness. The global factor appears less important for market volatility in models that permit time-varying conditional skewness. The influence of regional and global factors on risk is small in most of the markets, except in the late 1990s during which the regional factor accounted for a substantial portion of negative skewness in the markets' returns distribution.Asymmetries, Skewness, Volatility, Spillover, Stock returns

Global and Regional Sources of Risk in Equity Markets: Evidence from Factor Models with Time-Varying Conditional Skewness

We examine the influence of global and regional factors on the conditional distribution of stock returns from six Asian markets, using factor models in which unexpected returns comprise global, regional and local shocks. The models allow for conditional heteroskedasticity and time-varying conditional skewness, and permit mean, variance and skewness spillovers to be measured. We find that the pattern of spillovers changed in the late 1990s. When spillovers are allowed to vary with the type of news arriving in a market, we find that local news reduces mean spillovers but increases variance spillovers. News about regional countries increases skewness spilloversAsymmetries, Skewness, Volatility, Spillover, Stock returns, News.

Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments

We consider the problem of sensor localization in a wireless network in a multipath environment, where time and angle of arrival information are available at each sensor. We propose a distributed algorithm based on belief propagation, which allows sensors to cooperatively self-localize with respect to one single anchor in a multihop network. The algorithm has low overhead and is scalable. Simulations show that although the network is loopy, the proposed algorithm converges, and achieves good localization accuracy

Non-Fundamental Expectations and Economic Fluctuations: Evidence from Professional Forecasts

It is theoretically possible that non-fundamental idiosyncratic shocks to agents’ rational expectations are a source of economic fluctuations. Studies using data on consumer and investor sentiment suggest that this is indeed a significant source of fluctuations. We present the results of a study that uses forecasts from professional forecasters to extract non-fundamental shocks to expectations. In contrast to previous studies, we show that non-fundamental expectations are not a significant source of output fluctuations.Non-fundamental expectations; Sunspots; Economic fluctuations; Survey of Professional Forecasters; Vector autoregressions

Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange

We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast "calibration" can be used to improve deficient density forecasts. Finally, motivated by recent advances in financial risk management, we provide a detailed application to multivariate high-frequency exchange rate density forecasts. Copyright © 1998 F.X. Diebold, J. Hahn, and A.S. Tay. This paper is also available at