Duke Economics Working Paper #95-26
SNP is a method of nonparametric time series analysis. The method employs a polynomial series expansion to approximate the conditional density of a multivariate process. An appealing feature of the expansion is that it directly nests familiar models such as a pure VAR, a pure ARCH, a nonlinear process with homogeneous innovations, etc. An SNP model is fitted using conventional maximuml likelihood together with a model selection strategy that determines the appropriate degree of the polynomial.
A Fortran program implementing the SNP method is available via anonymous ftp at ftp.econ.duke.edu (220.127.116.11) in directory ~ftp/home/arg/snp or from Carnegie-Mellon University e-mail server by sending a one-line e-mail message "send snp from general" to email@example.com. The cose is provided at no charge for research purposes without warranty.
The program has switches that allow direct computation of functionals of the fitted density such as conditional means, conditional variances, and points for plotting the density. Other switches generate simulated sample paths which can be used to compute nonlinear functionals of the density by Monte Carlo integration, notably the nonlinear analogs of the impulse-response mean and volatility profiles used in traditional VAR and ARCH analysis. Simulated sample paths can also be used to set bootstrapped sup-norm confidence bands on these and other functionals.
The purpose of this Guide is to provide an expositional review of the underlying methodology and to walk the user through an application. Our hope is that the Guide will be essentially self contained and that very little reference to the cited literature will be required to use the program and the SNP method.
Home Page for SNP