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-------- Regression functions -------- 
 
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(),
bma_g            : Bayes model averaging estimates of Raftery, Madigan and Hoeting
bma_gd           : An example using bma_g(),
bma_gd2          : An example using bma_g(),
bma_gd3          : An example using bma_g(),
box_lik          : evaluate Box-Cox model likelihood function
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 the use of felogit.m
felogit_lik      : Compute log-likelihood felogit
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(),
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  : multilogit_demo.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_gd           : 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          : Panel demo from Introduction to the Theory and Practice of 
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_eqs          : Prints output from mutliple equation regressions
prt_gibbs        : Prints output from Gibbs sampler regression models
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()