{"id":59,"date":"2023-04-25T06:52:36","date_gmt":"2023-04-25T10:52:36","guid":{"rendered":"https:\/\/sites.nd.edu\/nmark\/?page_id=59"},"modified":"2023-05-01T20:16:08","modified_gmt":"2023-05-02T00:16:08","slug":"background-gauss-procedures","status":"publish","type":"page","link":"https:\/\/sites.nd.edu\/nmark\/graduate-textbook\/background-gauss-procedures\/","title":{"rendered":"Background Gauss Procedures"},"content":{"rendered":"\n

This page contains background Gauss Procedures that are called by the estimation programs.<\/strong><\/h2>\n\n\n\n
    \n
  1. HPFILT.SET<\/a> computes the Hodrick-Prescott filter for a univariate time series.<\/li>\n\n\n\n
  2. AMOEBA.SET<\/a>. Nelder-Mead simplex algorithm for finding the minimum of a function. Translated from Numerical Recipes.<\/em><\/li>\n\n\n\n
  3. NRMIN.SET<\/a>. Bo Honore and Ekaterini Kyriazidou’s Gauss translation of several very useful optimization routines from Numerical Recipes. <\/em>This procedure contains the methods of Broyden-Fletcher-Goldfarb-Shanno, Powell, and Davidon-Fletcher-Powell.<\/li>\n\n\n\n
  4. AUTOLAG.SET<\/a>. Computes an estimate of the spectral density matrix of a vector of orthogonality conditions at frequency 0 using the method of Newey and West (Econometrica<\/em>1987) using their automatic lag selection procedure (Review of Economic Studies<\/em>1994).<\/li>\n\n\n\n
  5. CORRELGM.SET<\/a>. Computes the correlegram of a stationary time series.<\/li>\n\n\n\n
  6. OLS.SET<\/a>. A quick procedure to do OLS.<\/li>\n\n\n\n
  7. COMPAN.SET<\/a>. Sets up the companion matrix for a VAR(p).<\/li>\n\n\n\n
  8. RBSTSE.SET<\/a>. Computes Newey-West or Hansen-Hodrick standard errors for regression problems.<\/li>\n\n\n\n
  9. UROOT.SET<\/a>. Does augmented Dickey-Fuller and Phillips-Perron unit root tests.<\/li>\n\n\n\n
  10. ADF.SET<\/a>. Computes studentized coefficient to do the augmented Dickey-Fuller test.<\/li>\n\n\n\n
  11. VRATIO.SET<\/a>. Computes the variance ratio statistic.<\/li>\n\n\n\n
  12. NLSYS.SET<\/a> Solves a system of nonlinear equations.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    This page contains background Gauss Procedures that are called by the estimation programs.<\/p>\n","protected":false},"author":4499,"featured_media":0,"parent":13,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-59","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/pages\/59","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/users\/4499"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/comments?post=59"}],"version-history":[{"count":2,"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/pages\/59\/revisions"}],"predecessor-version":[{"id":61,"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/pages\/59\/revisions\/61"}],"up":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/pages\/13"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/nmark\/wp-json\/wp\/v2\/media?parent=59"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}