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SNP: A Program for Nonparametric Time Series Analysis. Version 8.4. User's Guide

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Author Info
Tauchen, George E.
Gallant, A. Ronald

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Abstract

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 (152.3.10.64) 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 statlib@lib.stat.cmu.edu. 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.

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Publisher Info
Paper provided by Duke University, Department of Economics in its series Working Papers with number 95-26.

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Date of creation: 1995
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Handle: RePEc:duk:dukeec:95-26

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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://www.econ.duke.edu/

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models

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  1. Diego Valderrama, 2002. "The impact of financial frictions on a small open economy: when current account borrowing hits a limit," Working Papers in Applied Economic Theory 2002-15, Federal Reserve Bank of San Francisco. [Downloadable!]
  2. Mikhail Chernov & Eric Ghysels, 1998. "What Data Should Be Used to Price Options?," CIRANO Working Papers 98s-22, CIRANO. [Downloadable!]
  3. Mikhail Chernov & A. Ronald Gallant & Eric Ghysels & George Tauchen, 1999. "A New Class of Stochastic Volatility Models with Jumps: Theory and Estimation," CIRANO Working Papers 99s-48, CIRANO. [Downloadable!]
  4. Mikhail Chernov & A. Ronald Gallant & Eric Ghysels & George Tauchen, 2002. "Alternative Models for Stock Price Dynamics," CIRANO Working Papers 2002s-58, CIRANO. [Downloadable!]
    Other versions:
  5. Diego Valderrama, 2002. "Nonlinearities in international business cycles," Working Papers in Applied Economic Theory 2002-23, Federal Reserve Bank of San Francisco. [Downloadable!]
  6. Diego Valderrama, 2003. "Statistical Nonlinearities in the Business Cycle," Computing in Economics and Finance 2003 219, Society for Computational Economics. [Downloadable!]
  7. Diego Valderrama, 2002. "Statistical nonlinearities in the business cycle: a challenge for the canonical RBC model," Working Papers in Applied Economic Theory 2002-13, Federal Reserve Bank of San Francisco. [Downloadable!]
    Other versions:
  8. O. G"Unther & R. M"Uller & A S. Weigend, . "The Desing of MMM: A Model Management System for Time Series Analysis," Sonderforschungsbereich 373 1995-5, Humboldt Universitaet Berlin.
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