Features ofShazam Econometrics Software

Features of Shazam Econometrics Software

A free, downloadable Trial Version of SHAZAM is available to try out.

All features are enabled although the amount of memory available for calculations (PAR) is limited to be sufficient to run all but a few of the largest included examples.

There is no time limit on the use of these Trial Versions.

These are the features of Shazam Econometrics Software.

1. ARIMA Models: A time-series forecasting method using autoregression, differencing, and moving averages. Application: Forecasting stock prices, sales trends, and economic data.
2. Beta Regression: A regression model for variables constrained between 0 and 1. Application: Modeling probabilities, proportions, and rates.
3. Data Sorting: Organizing data in ascending or descending order. Application: Enhancing data preprocessing and analysis.
4. Data Transformations: Adjusting data to stabilize variance or improve normality. Application: Used in regression and machine learning.
5. Descriptive Statistics: Summarizing data using measures like mean and variance. Application: Understanding and exploring datasets.
6. Diagnostic Tests: Validating model assumptions like normality or heteroskedasticity. Application: Ensuring model reliability.
7. Dickey-Fuller Unit Root Tests: Checks for stationarity in time-series data. Application: Time-series modeling and forecasting.
8. Distributed Lag Models: Captures effects of independent variables over time lags. Application: Policy impact analysis in econometrics.
9. Exact Durbin Watson Tests: Tests for autocorrelation in regression residuals. Application: Model diagnostics in time-series analysis.
10. Financial Time Series: Analyzes financial data trends over time. Application: Modeling stock prices and market indices.
11. Forecasting: Predicting future trends using historical data. Application: Business planning and demand forecasting.
12. Full Information Maximum Likelihood (FIML) Models: Estimates parameters in simultaneous equation models. Application: Complex econometric modeling.
13. Granger Causality: Tests whether one time-series predicts another. Application: Analyzing causal relationships in economics.
14. Heteroskedasticity Tests: Identifies variance inconsistencies in regression models. Application: Improving model validity in econometrics.
15. Heteroskedastic Consistent Covariance Matrices: Adjusts standard errors for heteroskedasticity. Application: Reliable hypothesis testing.
16. Logit Models: Regression models for binary outcome variables. Application: Predicting probabilities, such as customer churn.
17. Moving Averages: Smooths time-series data by averaging over time windows. Application: Identifying trends in stock prices.
18. Nonlinear Least Squares: Fits nonlinear models to data. Application: Modeling complex relationships in econometrics.
19. Poisson Regression: Models count data based on Poisson distribution. Application: Event occurrence predictions like insurance claims.
20. Principal Components Analysis: Reduces data dimensions by identifying principal components. Application: Used in feature reduction and exploratory analysis.
21. Probability Distributions: Computes probabilities for various distributions. Application: Risk modeling and simulations.
22. Probit Models: Regression for binary data using normal distribution. Application: Binary classification in econometrics.
23. Generating Variables: Creates or modifies variables for analysis. Application: Data preparation for modeling.
24. Graphing: Visualizes data trends and relationships. Application: Communicating results effectively.
25. Exponential Regression: Fits exponential growth or decay models. Application: Population growth and decay modeling.
26. Gamma Regressions: Fits models with gamma-distributed errors. Application: Modeling skewed data, such as incomes.
27. Hypothesis Testing: Tests assumptions about data parameters. Application: Statistical significance analysis.
28. Heteroskedastic Models: Models variable errors across observations. Application: Corrects heteroskedasticity in regressions.
29. Factor Analysis: Identifies latent variables in datasets. Application: Psychometrics and market research.
30. Linear Programming: Optimizes linear objective functions subject to constraints. Application: Resource allocation problems.
31. Matrix Manipulation: Performs operations on matrices for analysis. Application: Essential in linear algebra-based models.
32. Monte Carlo Experiments: Simulates random sampling for statistical analysis. Application: Risk assessment and probability estimation.
33. Ridge Regression: Regularizes regression models to reduce overfitting. Application: Predictive modeling with multicollinearity.
34. Seasonal Adjustment: Removes seasonal effects from data. Application: Analyzing underlying trends in time-series.
35. ARCH and GARCH Models: Models time-series data with changing volatility. Application: Financial market risk analysis.
36. Bootstrapping: Resampling technique for estimating statistics. Application: Confidence intervals in non-normal datasets.
37. Box-Cox Models: Transforms data to stabilize variance. Application: Preprocessing for linear regression.
38. CUSUM Tests: Detects structural changes in time-series. Application: Quality control and monitoring.
39. Chow Test and Goldfeld-Quandt Test: Tests for structural breaks and heteroskedasticity. Application: Model diagnostics and robustness checks.
40. Cointegration Testing: Checks long-term relationships between non-stationary variables. Application: Economic time-series analysis.
41. Combined Box-Cox and Box-Tidwell Model: Applies complex transformations to improve model fit. Application: Nonlinear regression adjustments.
42. Computing the Power of a Test: Measures the ability to reject false null hypotheses. Application: Statistical experiment design.
43. Confidence Intervals and Ellipse Plots: Visualizes and quantifies parameter uncertainty. Application: Model diagnostics and hypothesis testing.
44. Cross-Section Heteroskedasticity and Time-Wise Autoregression: Models varying error variance and temporal relationships. Application: Time-series and panel data analysis.
45. Cumulative Distribution Function Computation: Computes probabilities up to a given value. Application: Statistical analysis and decision-making.
46. Data Smoothing and Seasonal Adjustments: Reduces noise and removes seasonal effects. Application: Time-series forecasting.
47. Derivatives and Integrals Evaluation: Calculates changes and accumulations. Application: Economic optimization problems.
48. Dynamic Simultaneous Equation Models: Captures interdependencies in dynamic systems. Application: Policy simulation in macroeconomics.
49. Estimation of Systems of Linear and Nonlinear Equations: Solves interrelated models simultaneously. Application: Complex econometric modeling.
50. Estimation using Regression Quantiles: Models conditional quantiles of data. Application: Robust regression for outliers.
51. Fuzzy Set Models: Deals with imprecise or ambiguous data. Application: Decision-making in uncertain conditions.
52. Generalized Entropy Models: Measures distribution inequality. Application: Income inequality and welfare economics.
53. Generalized Least Squares: Estimates models with correlated or heteroskedastic errors. Application: Time-series and panel data analysis.
54. Generalized Method of Moments (GMM) Estimation: Solves models using moment conditions. Application: Asset pricing and econometric analysis.
55. Hausman Specification Tests: Compares model consistency and efficiency. Application: Model selection in econometrics.
56. Hodrick Prescott Filtering: Separates trends from cycles in data. Application: Economic trend analysis.
57. Index Number Series Splicing: Combines multiple index series. Application: Creating continuous economic indices.
58. Instrumental Variable Estimation: Addresses endogeneity in regression models. Application: Causal inference in econometrics.
59. Iterative Cochrane-Orcutt Estimation: Corrects for autocorrelation in regression. Application: Improving time-series model accuracy.
60. Jackknife Estimators: Resampling method to reduce bias in estimates. Application: Small sample data analysis.
61. Johansen Maximum Eigenvalue Tests: Tests for cointegration between variables. Application: Economic relationships in time-series.
62. Johansen Trace Tests: Determines the number of cointegrated vectors. Application: Long-run equilibrium modeling.
63. Joint Confidence Region Computation: Calculates joint uncertainty intervals. Application: Multi-parameter hypothesis testing.
64. Least Absolute Errors Estimation: Minimizes absolute residuals in regression. Application: Robust regression under outliers.
65. Limited Information Maximum Likelihood (LIML) Models: Estimates overidentified models. Application: Simultaneous equation modeling.
66. Newey-West Autocorrelation Consistent Covariance Matrix: Adjusts for heteroskedasticity and autocorrelation. Application: Time-series regression robustness.
67. Nonlinear Seemingly Unrelated Regression (SUR): Estimates interrelated nonlinear models. Application: Econometric model integration.
68. Nonlinear Sets of Equations: Solves complex nonlinear systems. Application: Engineering and econometric applications.
69. Nonparametric Density Estimation: Estimates data distributions without assumptions. Application: Descriptive statistics and exploratory analysis.
70. Nonparametric Methods: Statistical methods not assuming data distribution. Application: Flexible modeling in diverse fields.
71. Nonparametric Regression with Kernel Estimation: Fits data without assuming linearity. Application: Smoothing and forecasting in noisy data.
72. Ordinary Least Squares Models: Standard regression for estimating linear relationships. Application: Widely used in predictive modeling.
73. Phillips-Perron Unit Root Test: Checks stationarity in time-series data. Application: Time-series preprocessing.
74. Polynomial Inverse Lags: Models complex lag structures in time-series. Application: Time-series forecasting with lagged effects.
75. Pooling Cross-Section Time-Series with Balanced or Unbalanced Panels: Combines data across dimensions. Application: Panel data econometrics.
76. Power of Statistical Tests Computation: Evaluates the likelihood of rejecting false hypotheses. Application: Statistical test planning.
77. Price Indices: Calculates weighted price measures. Application: Economic and market analysis.
78. Quadratic Programming: Optimizes quadratic objective functions. Application: Portfolio optimization.
79. RESET Specification Error Tests: Checks model misspecifications. Application: Regression diagnostics.
80. Random Number Generation: Creates random samples for simulations. Application: Monte Carlo experiments.
81. Recursive Residuals: Analyzes regression stability over time. Application: Detecting structural breaks.
82. Regression with Non-Normal Errors: Fits models when errors deviate from normality. Application: Robust regression.
83. Regression with Time Varying Coefficients: Models changing relationships over time. Application: Dynamic forecasting.
84. Restricted Least Squares: Imposes constraints on regression coefficients. Application: Hypothesis testing with restrictions.
85. Restricted Seemingly Unrelated Regression Models (SUR): Estimates restricted SUR models. Application: Multi-equation model optimization.
86. Seemingly Unrelated Regression (SUR): Fits multiple equations with correlated errors. Application: Econometric model integration.
87. Simulated Annealing: Optimization technique mimicking thermodynamics. Application: Solving nonlinear optimization problems.
88. Simultaneous Equation Models (Linear and Nonlinear): Models interdependent equations simultaneously. Application: Economic system analysis.
89. Solving Nonlinear Sets of Equations: Finds solutions to nonlinear systems. Application: Engineering and economic modeling.
90. Splicing Index Number Series: Combines and adjusts index series. Application: Economic index continuity.
91. Stepwise Regression: Automated model selection by adding/removing predictors. Application: Predictive model optimization.
92. Stochastic Frontier Models: Models efficiency and productivity. Application: Performance analysis in economics.
93. Tests for Autocorrelation: Checks dependence in residuals. Application: Time-series model validation.
94. Three Stage Least Squares: Estimates simultaneous equation models. Application: Complex econometric system modeling.
95. Time Varying Linear Regression: Models linear relationships that evolve over time. Application: Financial time-series forecasting.
96. Tobit Models: Estimates censored regression models. Application: Analyzing limited dependent variables.
97. Two Stage Least Squares: Handles endogeneity in regression. Application: Causal inference in econometrics.
98. Univariate Kernel Method: Estimates probability densities for single variables. Application: Data smoothing in univariate analysis.
99. Vector AutoRegressive (VAR) Models: Models interdependent time-series. Application: Macroeconomic policy analysis.
100. Weighted Least Squares Regression: Accounts for unequal variances in regression. Application: Robust linear modeling.
101. Robust Regression: Handles outliers and heteroskedasticity in regression. Application: Reliable prediction in noisy data.
102. Seasonal Adjustment: Removes seasonal effects from time-series data. Application: Trend and cycle analysis in economic data.