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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()