Using the stochastic integration/cointegration framework of Harris, McCabe and Leybourne (2002) we revisit the problem of assessing the empirical evidence for or against the present value class of models in the bond and stock markets. This framework allows for volatility in excess of that...

This paper considers model selection, estimation and forecasting for a class of integer autoregressive models suitable for use when analysing time series count data. Any number of lags may be entertained, and estimation may be performed by likelihood methods. Model selection is enhanced by the...

In this paper, we extend earlier work of Freeland and McCabe (2004) and develop a general framework for maximum likelihood (ML) estimation of higher-order integer-valued autoregressive (INAR(p)) processes. Our exposition includes the case where the innovation sequence has a Poisson distribution...

We present a test of the null hypothesis of stationarity against unit root alternatives for panel data that allows for arbitrary cross- sectional dependence. We treat the short run time series dynamics non- parametrically and thus avoid the need to fit separate models for the individual series....