Publication Date: June 1999
Recent work by the author on methods of spatial density analysis for time series data with stochastic trends is reviewed and extended. The methods are illustrated in some empirical applications and simulations. The empirical applications include macroeconomic data on inflation, ﬁnancial data on exchange rates and political opinion poll data. It is shown how the methods can be used to measure empirical hazard rates for inflation and deflation. Empirical estimates based on historical US data over the last 60 years indicate that the predominant inflation risks are at low levels (2–6%) and low two-digit levels (10–12%), and that there is also a signiﬁcant risk of deflation around the –1% level.
descriptive statistics, hazard rate, kernel estimate, soujourn time, spatial density, spatial moments, unit root nonstationarity
See CFP: 1023