SNP: Nonparametric Time Series Analysis
SNP is a method of nonparametric multivariate time series analysis. It employs an expansion in Hermite functions to approximate the conditional density of a multivariate process. An appealing feature of the expansion is that it is a nonlinear nonparametric model that directly nests the Gaussian VAR model, the semiparametric ARCH model, the Gaussian GARCH model, and the semiparametric GARCH model. The unrestricted SNP expansion is more general than any of these models. The SNP model is fitted using conventional maximum likelihood together with a model selection strategy that determines the appropriate order of expansion.
KeywordsARCH models Bootstrap Density GARCH models Kalman filter Kernels Maximum likelihood Method of moment estimation Nonparametric time series analysis Semi-nonparametric (SNP) models Splines Statistical inference Vector autoregressions
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