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