Getting economics analysis done without burning your weekends
Economics Hacks Easy
I spent three semesters manually calculating regression standard errors in Excel because my grad advisor didn't trust Stata. I still have the spreadsheet trauma. What I ended up building was a practical workflow that lets you crunch basic econometric work without getting bogged down in software learning curves. People call it Economics Hacks Easy, though it's really just a set of habits and shortcuts I accumulated over about eight years of actual research. The core idea is simple. You stop trying to run every model through a full statistical package when a simpler approach gets you 90% of the way there. Most economics problems don't require panel data fixes, bootstrap confidence intervals, or complex instrument construction. They require clear thinking about identification and a reasonable approximation. The tricks are all in the preprocessing and the output formatting, not in the estimation itself. Here's how I actually use this day-to-day. First, I normalize my data before anything else. That means converting everything to consistent decimal precision, handling missing values with forward-fill rather than deletion, and flagging outliers at plus or minus four standard deviations from the mean. The outlier handling is where most people go wrong. They either trim aggressively and lose sample, or they leave everything and let one bad observation drive their results. I cap outliers at the 99th and 1st percentiles and keep a separate flag variable so I can run the sensitivity check in under two minutes.
For the actual estimation, I use OLS as the default and only escalate to IV or GMM when the first-stage F-statistic drops below 10. I know that sounds obvious but I've seen too many papers where the researcher uses two-stage least squares on a variable that's clearly exogenous, which inflates standard errors for no reason. The rule of thumb is to start with OLS, check the residuals for heteroskedasticity using White's test, and then move to robust standard errors if the p-value is below 0.05. That alone accounts for about sixty percent of the econometric work I do. There's a specific edge case I ran into recently that shows why this matters. I was working on a difference-in-differences project evaluating a state-level policy change, and the parallel trends assumption looked fine visually but failed the pre-trend test at the five percent level. The standard workaround would be to drop the treatment group entirely or switch to synthetic control methods, both of which are time-consuming. Instead, I added state-specific linear time trends to the model. This corrected for diverging pre-trends without changing the coefficient on the treatment indicator by more than three percent. It took about twenty minutes to implement after the initial model was already coded. That's the kind of thing you can't learn from a textbook, but it comes up more often than you'd expect. The output formatting is where people waste the most time. I never manually retype tables. I use the estout package in Stata or comparable export functions in R to generate LaTeX-ready tables with one command. A typical output includes coefficients, standard errors, significance stars, and observations in the correct format. This usually cuts the reporting phase from an hour of copy-pasting into about four minutes of cleanup at most.
What this approach leaves out
This method works well for cross-sectional and panel data with straightforward identification strategies. It breaks down when you're dealing with clustered randomization at the unit level, discrete choice models with many alternatives, or dynamic panel estimators like Arellano-Bond. In those cases, the hacks become actual restrictions and you're better off investing time in proper software. Also, the outlier capping approach can mask genuine structural breaks. If you're studying a crisis period or a regime change, those "outliers" might be the signal you're actually trying to measure. I've had to redo analyses twice because the capping hid meaningful variation in the dependent variable. The main bottleneck is that this approach assumes your data is reasonably clean to begin with. If you're starting from raw survey microdata with inconsistent response formats across waves, you're going to spend more time on data cleaning than on any shortcut can save. In those situations, the honest recommendation is to just learn Python pandas or R tidyverse properly. The upfront time investment pays off after about three large datasets. What I can give you is a starting template that handles the common cases. For data normalization, I use a three-step process. Convert all currency variables to real terms using the appropriate GDP deflator rather than CPI when possible, since CPI understates long-run price changes. Standardize continuous variables using z-scores for comparability across studies. Encode categorical variables as dummies only when the categories have clear economic meaning rather than arbitrary groupings.
Get the Full Details
For the estimation workflow, the shortcut is to always report heteroskedasticity-robust standard errors by default, even if White's test doesn't flag them. It costs nothing computationally and protects you from reviewer complaints. Then run a specification search by adding controls in blocks. Start with no fixed effects, add year dummies, add entity dummies, and finally add interactions. This hierarchy makes your robustness checks transparent and avoids the appearance of p-hacking. I've found that reviewers tend to trust models where the coefficient stability across specifications is visible rather than hidden in an appendix. Another practical shortcut I use extensively is the placebo test. Before presenting any result, I run the same model on a fake treatment date or a subset of the data that should show no effect. If you get a significant coefficient there, something is wrong with your specification. I do this for roughly half my models and it has prevented me from publishing at least two incorrect findings. The time cost is minimal because you're just copying the code and changing one parameter. The hardest part about all of this is knowing when to stop simplifying. There's a sweet spot somewhere between oversimplifying your model and overcomplicating it past the point of useful interpretation. My heuristic is that if I can't explain the main result to a non-economist in two sentences, the model has too many moving parts. I've seen good research killed by elegant but uninterpretable specifications, and I've also seen mediocre research pass because the authors avoided that trap through restraint.
If you're just getting started, focus on mastering the OLS baseline, robust standard errors, and clean table output. Everything else builds on those three skills. The field doesn't need more people who can run dynamic stochastic general equilibrium models on second-order perturbation methods. It needs more people who can correctly estimate a fixed effects model and tell a coherent causal story about the result.
