These data are from the pre-deregulation days of the U.S. domestic airline industry. The data are an extension of Caves, Christensen, and Trethaway (1980) and Trethaway and Windle (1983). The original raw data set is a balanced panel of 25 firms observed over 15 years (1970-1984). After removing observations because of strikes, mergers, and missing values, the panel becomes an unbalanced one wi...

In most social, information, and collaboration systems the complex activity of agents generates rapidly evolving time-varying networks. Temporal changes in the network structure and the dynamical processes occurring on its fabric are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset describing the activity of millions...

We consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random ...

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this paper investigates the forecasting performance of different time-varying bvar models for iranian inflation. forecast accuracy of a bvar model with litterman’s prior compared with a time-varying bvar model (a version introduced by doan et al., 1984); and a modified time-varying bvar model, where the autoregressive coefficients are held constant and only the deterministic components are allo...

this paper studies the effect of considering time varying skewness and kurtosis on the estimation of value at risk (var) for both long and short positions using the hyaparch model and daily data for tehran stock exchange price index (tepix). our results show that applying conditional distributions with time varying or constant skewness and degrees of freedom is able to capture the asymmetry app...

In this paper, we present a new method for modeling timeevolving correlation networks, using a Mean Reversion Autoregressive Model, and apply this to stock market data. The work is motivated by the assumption that the price and return of a stock eventually regresses back towards their mean or average. This allows us to model the stock correlation time-series as an autoregressive process with a ...