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Feature
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Our Expert Human Analytics
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Command Script Customization
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Phase
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Step Description
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| Step |
Phase |
Key Actions / Commands |
| 1 |
Raw Data Input |
.csv / .xlsx |
| 2 |
Data Cleaning & Variable Labeling |
generate, replace, encode |
| 3 |
Stationarity & Diagnostic Checks |
dfuller, sktest, vce |
| 4 |
Core Model Execution |
regress, xtreg, ivregress |
| 5 |
Post-Estimation Diagnostics |
hettest, estat imtest, linktest |
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Inputs / Sources
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Final Service Standards
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Group of Eight (Go8) Rules
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Australian Bureau of Statistics
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Post-Estimation Testing Framework
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Heteroskedasticity
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Multicollinearity
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Specification Error
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• estat hettest
• Robust SE
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• estat vif
• Variable Drop
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• linktest
• Ramsey RESET
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Review our transparent script executions below to see how we handle advanced post-estimation modeling:
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Technical Stata Syntax Command
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Academic Purpose & Diagnostic Execution
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vce(robust)
Example: regress y x1 x2, vce(robust)
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Adjusts standard errors to account for heteroskedasticity violations. This preserves the validity of your $t$-statistics and $p$-values when residual variance is non-constant.
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estimates table
Example: estimates table m1 m2, b(%9.3f) p
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Organizes coefficients and significance levels across multiple regression models into a clean, side-by-side framework for easy reporting.
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hettest / estat imtest
Example: estat hettest
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Performs formal Breusch-Pagan and White tests to mathematically diagnose heteroskedasticity patterns within model residuals.
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vif
Example: estat vif
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Calculates Variance Inflation Factors to detect multicollinearity issues, identifying redundant variables that distort coefficients.
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