-------- 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
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.
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         : demo file for pfixed, prandom, ppooled, phaussman
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()