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<TITLE> Regress function library </TITLE><BODY> <A HREF = "../html/view.html" target="FRP">[Return to Master Index]</A></BR> </BR> <pre> -------- Regression functions -------- </pre> <pre> <A HREF = "ar1_like.m ">ar1_like </A>: evaluate ols model with AR1 errors log-likelihood<A HREF = "ar_g.m ">ar_g </A>: MCMC estimates Bayesian heteroscedastic AR(k) model <A HREF = "ar_gd.m ">ar_gd </A>: An example using ar_g(),<A HREF = "box_lik.m ">box_lik </A>: evaluate Box-Cox model likelihood function<A HREF = "boxc_trans.m ">boxc_trans </A>: compute box-cox transformation<A HREF = "boxcox.m ">boxcox </A>: box-cox regression using a single scalar transformation<A HREF = "boxcox_d.m ">boxcox_d </A>: An example using box_cox(),<A HREF = "demo_reg.m ">demo_reg </A>: demo using most all regression functions<A HREF = "felogit.m ">felogit </A>: computes binomial logistic regression with a one-dimensional fixed effect:<A HREF = "felogit_demo.m ">felogit_demo </A>: demonstrate use of felogit.m<A HREF = "felogit_lik.m ">felogit_lik </A>: Compute probabilities and value of log-likelihood<A HREF = "garch_like.m ">garch_like </A>: log likelihood for garch model<A HREF = "garch_sigt.m ">garch_sigt </A>: generate garch model sigmas over time <A HREF = "garch_trans.m ">garch_trans </A>: function to transform garch(1,1) a0,a1,a2 garch parameters<A HREF = "ham_itrans.m ">ham_itrans </A>: inverse transform Hamilton model parameters<A HREF = "ham_like.m ">ham_like </A>: log likelihood function for Hamilton's model<A HREF = "ham_trans.m ">ham_trans </A>: transform Hamilton model parameters<A HREF = "hwhite.m ">hwhite </A>: computes White's adjusted heteroscedastic<A HREF = "hwhite_d.m ">hwhite_d </A>: An example of hwhite(),<A HREF = "ksmooth.m ">ksmooth </A>: Kim's smoothing for Hamilton() model<A HREF = "lad.m ">lad </A>: least absolute deviations regression<A HREF = "lad_d.m ">lad_d </A>: An example using lad(),<A HREF = "lmtest.m ">lmtest </A>: computes LM-test for two regressions<A HREF = "lmtest_d.m ">lmtest_d </A>: demo using lmtest() <A HREF = "lo_like.m ">lo_like </A>: evaluate logit log-likelihood<A HREF = "logit.m ">logit </A>: computes Logit Regression<A HREF = "logit_d.m ">logit_d </A>: An example of logit(),<A HREF = "mlogit.m ">mlogit </A>: multinomial logistic regression <A HREF = "mlogit_d.m ">mlogit_d </A>: An example of mlogit(),<A HREF = "mlogit_lik.m ">mlogit_lik </A>: Calculates likelihood for multinomial logit regression model.<A HREF = "multilogit.m ">multilogit </A>: implements multinomial logistic regression<A HREF = "multilogit_demo.m">multilogit_demo </A>: demonstrates the use of multilogit.m<A HREF = "multilogit_lik.m ">multilogit_lik </A>: Computes value of log likelihood function for multinomial logit regression<A HREF = "nwest.m ">nwest </A>: computes Newey-West adjusted heteroscedastic-serial<A HREF = "nwest_d.m ">nwest_d </A>: An example using nwest(),<A HREF = "ols.m ">ols </A>: least-squares regression <A HREF = "ols_d.m ">ols_d </A>: An example using ols(),<A HREF = "ols_g.m ">ols_g </A>: MCMC estimates for the Bayesian heteroscedastic linear model<A HREF = "ols_gcbma.m ">ols_gcbma </A>: MC^3 x-matrix specification for homoscedastic OLS model<A HREF = "ols_gcbmad.m ">ols_gcbmad </A>: Demo of ols_gcbma() model comparison function<A HREF = "ols_gd.m ">ols_gd </A>: demo of ols_g() <A HREF = "ols_gv.m ">ols_gv </A>: MCMC estimates for the Bayesian heteroscedastic linear model<A HREF = "ols_gvd.m ">ols_gvd </A>: demo of ols_g() <A HREF = "olsar1.m ">olsar1 </A>: computes maximum likelihood ols regression for AR1 errors<A HREF = "olsar1_d.m ">olsar1_d </A>: demonstrate olsc, olsar1 routines<A HREF = "olsc.m ">olsc </A>: computes Cochrane-Orcutt ols Regression for AR1 errors<A HREF = "olsc_d.m ">olsc_d </A>: demonstrate ols_corc roc <A HREF = "olse.m ">olse </A>: OLS regression returning only residual vector<A HREF = "olsrs.m ">olsrs </A>: Restricted least-squares estimation<A HREF = "olsrs_d.m ">olsrs_d </A>: An example using olsrs(),<A HREF = "olst.m ">olst </A>: ols with t-distributed errors<A HREF = "olst_d.m ">olst_d </A>: An example using olst(),<A HREF = "panel_d.m ">panel_d </A>: Demonstrates use of panel data estimation<A HREF = "pfixed.m ">pfixed </A>: performs Fixed Effects Estimation for Panel Data<A HREF = "phaussman.m ">phaussman </A>: prints haussman test, use for testing the specification of the fixed or<A HREF = "plt_eqs.m ">plt_eqs </A>: plots regression actual vs predicted and residuals for:<A HREF = "plt_gibbs.m ">plt_gibbs </A>: Plots output from Gibbs sampler regression models<A HREF = "plt_reg.m ">plt_reg </A>: plots regression actual vs predicted and residuals<A HREF = "plt_tvp.m ">plt_tvp </A>: Plots output using tvp regression results structures<A HREF = "ppooled.m ">ppooled </A>: performs Pooled Least Squares for Panel Data(for balanced or unbalanced data)<A HREF = "pr_like.m ">pr_like </A>: evaluate probit log-likelihood<A HREF = "prandom.m ">prandom </A>: performs Random Effects Estimation for Panel Data<A HREF = "probit.m ">probit </A>: computes Probit Regression<A HREF = "probit_d.m ">probit_d </A>: demo of probit()<A HREF = "probit_g.m ">probit_g </A>: MCMC sampler for the Bayesian heteroscedastic Probit model <A HREF = "probit_gd.m ">probit_gd </A>: demo of probit_g<A HREF = "prt_bmao.m ">prt_bmao </A>: print results from ols_gcbma function<A HREF = "prt_eqs.m ">prt_eqs </A>: Prints output from mutliple equation regressions<A HREF = "prt_felogit.m ">prt_felogit </A>: Prints output from felogit function<A HREF = "prt_gibbs.m ">prt_gibbs </A>: Prints output from Gibbs sampler regression models<A HREF = "prt_multilogit.m ">prt_multilogit </A>: Prints output from multilogit function<A HREF = "prt_panel.m ">prt_panel </A>: Prints Panel models output<A HREF = "prt_reg.m ">prt_reg </A>: Prints output using regression results structures<A HREF = "prt_swm.m ">prt_swm </A>: Prints output from Switching regression models<A HREF = "prt_tvp.m ">prt_tvp </A>: Prints output using tvp() regression results structures<A HREF = "ridge.m ">ridge </A>: computes Hoerl-Kennard Ridge Regression<A HREF = "ridge_d.m ">ridge_d </A>: An example using ridge(), bkw()<A HREF = "ridge_d2.m ">ridge_d2 </A>: An example using ridge(), bkw()<A HREF = "robust.m ">robust </A>: robust regression using iteratively reweighted<A HREF = "robust_d.m ">robust_d </A>: An example using robust(),<A HREF = "rtrace.m ">rtrace </A>: Plots ntheta ridge regression estimates <A HREF = "sur.m ">sur </A>: computes seemingly unrelated regression estimates<A HREF = "sur_d.m ">sur_d </A>: An example using sur(),<A HREF = "switch_em.m ">switch_em </A>: Switching Regime regression (EM-estimation)<A HREF = "switch_emd.m ">switch_emd </A>: Demo of switch_em<A HREF = "theil.m ">theil </A>: computes Theil-Goldberger mixed estimator<A HREF = "theil_d.m ">theil_d </A>: An example using theil(),<A HREF = "thsls.m ">thsls </A>: computes Three-Stage Least-squares Regression<A HREF = "thsls_d.m ">thsls_d </A>: An example using thsls(),<A HREF = "to_llike.m ">to_llike </A>: evaluate tobit log-likelihood<A HREF = "to_rlike.m ">to_rlike </A>: evaluate tobit log-likelihood<A HREF = "tobit.m ">tobit </A>: computes Tobit Regression<A HREF = "tobit_d.m ">tobit_d </A>: An example using tobit()<A HREF = "tobit_d2.m ">tobit_d2 </A>: An example using tobit()<A HREF = "tobit_g.m ">tobit_g </A>: MCMC sampler for Bayesian Tobit model <A HREF = "tobit_gd.m ">tobit_gd </A>: An example using tobit_g()<A HREF = "tobit_gd2.m ">tobit_gd2 </A>: An example using tobit_g()<A HREF = "tsls.m ">tsls </A>: computes Two-Stage Least-squares Regression<A HREF = "tsls_d.m ">tsls_d </A>: An example using tsls(),<A HREF = "tvp.m ">tvp </A>: time-varying parameter maximum likelihood estimation<A HREF = "tvp_d.m ">tvp_d </A>: An example using tvp(),<A HREF = "tvp_garch.m ">tvp_garch </A>: time-varying parameter estimation with garch(1,1) errors<A HREF = "tvp_garch_like.m ">tvp_garch_like </A>: log likelihood for tvp_garch model<A HREF = "tvp_garchd.m ">tvp_garchd </A>: An example using tvp_garch(),<A HREF = "tvp_like.m ">tvp_like </A>: returns -log likelihood function for tvp model<A HREF = "tvp_markov.m ">tvp_markov </A>: time-varying parameter model with Markov switching error variances<A HREF = "tvp_markov_lik.m ">tvp_markov_lik </A>: log-likelihood for Markov-switching TVP model <A HREF = "tvp_markovd.m ">tvp_markovd </A>: An example using tvp_markov(),<A HREF = "tvp_markovd2.m ">tvp_markovd2 </A>: An example using tvp_markov(), and tvp_garch()<A HREF = "tvp_zglike.m ">tvp_zglike </A>: returns -log likelihood function for tvp model with Zellner's g-prior<A HREF = "waldf.m ">waldf </A>: computes Wald F-test for two regressions<A HREF = "waldf_d.m ">waldf_d </A>: demo using waldf() </pre> </BODY> ⌨️ 快捷键说明
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