We develop a uniform test for detecting and dating the integrated or mildly explosive behaviour of a strictly stationary generalized autoregressive conditional heteroskedasticity (GARCH) process. Namely, we test the null hypothesis of a globally stable GARCH process with constant parameters...

A direct utility approach can handle multiple discrete/continuous choice outcomes. However, there is a trade-off between allowing correlations between unobserved perceived qualities of two alternatives and computational burden. If we allow the correlations, then we have to do numerical...

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...

The linear Gaussian state space model, also known as dynamic linear model in the Bayesian literature, has become one of the standard parametric modeling forms with parameters changing over time in the time series analysis. It provides a unified and flexible framework for describing, modeling and...

Recent work by Said and Dickey (1984 ,1985) , Phillips (1987), and Phillips and Perron(1988) examines tests for unit roots in the autoregressive part of mixed autoregressive-integrated-moving average (ARIHA) models (tests for stationarity). Monte Carlo experiments show that these unit root tests...

This article shows that, under some common assumptions, the pointwise synthetic control estimator would be unbiased if the outcome variable follows an autoregressive process. However, if such autoregressive process is nonstationary, then the variance of the pointwise estimator would be sensitive...

In a world that is increasingly connected on-line, cyber risks become critical. Cyber risk management is very difficult, as cyber loss data are typically not disclosed. To mitigate the reputational risks associated with their disclosure, loss data may be collected in terms of ordered severity...

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm...

We examine the finite sample properties of the variance ratio test of the random walk hypothesis via Monte Carlo simulations under two null and three alternative hypotheses. These results are compared to the performance of the Dickey-Fuller t and the Box-Pierce Q statistics. Under the null...

Survival data often include an “immune” or cured fraction of units that will never experience an event and conversely, an “at risk” fraction that can fail or die. It is also plausible that spatial clustering (i.e., spatial autocorrelation) in latent or unmeasured risk factors among...