SHAZAM 11
經濟時間序列
軟體代號:352
瀏覽次數:2199
Features
SHAZAM Analytical Features
The primary strength of SHAZAM is the estimation and testing of many types of econometric and statistical models.
Build and estimate models by choosing variables and selecting techniques using your mouse, or code them quickly using the powerfully simple SHAZAM Command Language.
SHAZAM also provides capabilities for both matrix programming and the programming of reusable procedures.
With SHAZAM you can do:
- ARCH and GARCH Models
- ARIMA Models
- Autocorrelation Models
- Beta Regression
- Bootstrapping
- Box-Cox Models
- Chow Test and Goldfeld-Quandt Test
- Cointegration Testing
- Combined Box-Cox and Box-Tidwell Model
- Computing the Power of a Test
- Confidence Intervals and Ellipse Plots
- Cross-Section Heteroskedasticity and Time-Wise Autoregression
- Data Smoothing and Seasonal Adjustments
- Cumulative Distribution Function Computation
- CUSUM Tests
- Data Sorting
- Data Transformations
- Derivatives and Integrals Evaluation
- Descriptive Statistics
- Diagnostic Tests
- Dickey-Fuller Unit Root Tests
- Distributed Lag Models
- Dynamic Simultaneous Equation Models
- Estimation of Systems of Linear and Nonlinear Equations
- Estimation using Regression Quantiles
- Exact Durbin Watson Tests
- Exponential Regression
- Factor Analysis
- Financial Time Series
- Forecasting
- Full Information Maximum Likelihood (FIML) Models
- Fuzzy Set Models
- Gamma Regressions
- Generalized Entropy Models
- Generalized Least Squares
- Generalized Method of Moments (GMM) Estimation
- Generating Variables
- Granger Causality
- Graphing
- Hausman Specification Tests
- Hetereskedasticity Tests
- Heteroskedastic Consistent Covariance Matrices
- Heteroskedastic Error Estimation
- Heteroskedastic Models
- Heteroskedasticity Tests
- Hodrick Prescott Filtering
- Hypothesis Testing (Linear and Nonlinear)
- Hypothesis Testing of Nonlinear Functions of Coefficients
- Index Number Series Splicing
- Instrumental Variable Estimation
- Iterative Cochrane-Orcutt Estimation
- Jackknife Estimators
- Johansen Maximum Eigenvalue Tests
- Johansen Trace Tests
- Joint Confidence Region Computation
- Least Absolute Errors Estimation
- Limited Information Maximum Likelihood (LIML) Models
- Linear Programming
- Linear Regressions with Inequality Restrictions
- Logit Models
- Lognormal Regression
- MAD Estimation
- Matrix Manipulation
- Maximum Likelihood Estimation with Non-Normal Errors
- Minimization and Maximization of Nonlinear Functions
- Missing Observation Handling
- Monte Carlo Experiments
- Moving Averages
- Multivariate Kernel Method
- Newey-West Autocorrelation consistent covariance matrix
- Nonlinear Least Squares
- Nonlinear Least Squares by the Rank One Correlation Method
- Nonlinear Seemingly Unrelated Regression (SUR)
- Nonlinear Sets of Equations
- Nonparametric Density Estimation
- Nonparametric Methods
- Nonparametric Regression with Kernel Estimation
- Ordinary Least Squares Models
- Poisson Regression
- Phillips-Perron Unit Root Test
- Polynomial Inverse Lags
- Pooling Cross-Section Time-Series with Balanced or Unbalanced Panels
- Power of Statistical Tests Computation
- Price Indices
- Principal Components Analysis
- Probability Distributions
- Probit Models
- Quadratic Programming
- Random Number Generation
- Recursive Residuals
- Regression with Non-Normal Errors
- Regression with Time Varying Coefficients
- RESET Specification Error Tests
- Restricted Least Squares
- Restricted Seemingly Unrelated Regression Models (SUR)
- Ridge Regression
- Robust Regression
- Seasonal Adjustment
- Seemingly Unrelated Regression (SUR)
- Simulated Annealing
- Simultaneous Equation Models (Linear and Nonlinear)
- Solving Nonlinear Sets of Equations
- Splicing Index Number Series
- Stepwise Regression
- Stochastic Frontier Models
- Tests for Autocorrelation
- Three Stage Least Squares
- Time Varying Linear Regression
- Tobit Models
- Two Stage Least Squares
- Univariate Kernel Method
- Vector AutoRegressive (VAR) Models
- Weighted Least Squares Regression
..and much more...
