Model Estimation & Data Analysis: Robust, Semiparametric & Nonparametric Estimation
LIMDEP and NLOGIT offer a variety of procedures of robust, semiparametric and nonparametric estimation and inference tools.
Robust Covariance Matrix Estimators
- Cluster based asymptotic covariance matrices
- Bootstrapping standard errors for any estimator
- White and heteroscedasticity corrected estimators
- Newey-West estimators
- Choice based sampling discrete choice estimators
- Jackknife estimators of standard errors for any estimator
Robust Estimators
- GMM estimation for user specified models
- Kernel density estimation
- Spectral density estimation
- Random parameters models
- Kernel weights for estimation
Non- and Semiparametric Estimators
- Least absolute deviations linear regression
- Quantile regression, linear or count
- Maximum score for binary choice
- Klein and Spady estimator for binary choice
- Nonparametric, kernel density regression
- LOWESS regression
Stratified Data
- Cluster corrections
- Stratification and clustering
- Finite population weights
Robust Tests
- Rank correlation
- Coefficient of concordance
- Kolmogorov-Smirnov
- Normality test - chi-squared
- Box-Pierce and Box-Ljung
- Poe combinatorial comparison
Multiple Imputation
- Up to 30 variables imputed simultaneously
- Six types of imputation procedures for
- Continuous variables using multiple regression
- Binary variables using logistic regression
- Count variables using Poisson regression
- Likert scale (ordered outcomes) using ordered probit
- Fractional (proportional outcome) using logistic regression
- Unordered multinomial choice using multinomial logit
- No duplication of the base data set
- All models supported by built in procedures
- Any model written by the user with GMME, MAXIMIZE, NLSQ, etc.
- Estimate any number of models using each imputed data set