SOFTWARE RECOMMENDATION REPORT

Your Situation

• Field: Economics (PhD, starting next year)
• Programming Experience: Python basics
• Career Goal: Academic (PhD program)
• Institutional Access: Advisor uses Stata
• Analyses Needed: Econometric analysis, panel regression, time-series forecasting
• Timeline: 6 months to prepare

---

PRIMARY RECOMMENDATION: Stata (Primary) + R (Secondary)

Strategy: Dual-track approach with Stata priority

• Months 1-3: Focus on Stata fundamentals and econometric methods
• Months 3-4: Advance Stata for panel data and time-series
• Months 4-6: Add R for complementary capabilities and modern econometric packages
• Post-start: Use Stata as primary tool for advisor collaboration, R for cutting-edge methods

Rationale:

1. Advisor alignment is critical: Your advisor uses Stata. This means you’ll need to replicate their work, share code, debug together, and get meaningful help during your PhD. Starting with Stata removes this friction immediately.

2. Economics field standard: Stata dominates applied economics, especially for panel regression and traditional econometrics. Top economics journals overwhelmingly feature Stata code in replication packages.

3. Faster learning curve with Python background: With Python basics, you’ll grasp Stata’s command-line syntax in ~2 months rather than the typical 2-3 months for complete beginners. The programming logic transfers even though syntax differs.

4. R as strategic complement: R offers cutting-edge econometric packages (fixest, plm, fable) and superior data visualization (ggplot2). Learning R after Stata gives you broader methodological toolkit without abandoning department norms.

---

6-MONTH LEARNING ROADMAP

Month 1-2: Stata Fundamentals (60-80 hours total)

Objectives:

• Master Stata syntax, data manipulation (merge, reshape, collapse)
• Run basic regressions (OLS, IV, clustered standard errors)
• Understand do-files for reproducibility
• Learn data cleaning workflows

Resources:

• Primary Textbook: “Introductory Econometrics: A Modern Approach” by Wooldridge (has Stata companion)
• Free: Stata’s built-in tutorials (help tutorials)
• Video: Ben Lambert’s YouTube econometrics series (uses Stata)
• Practice: UCLA IDRE Stata tutorials (economics focus)

Time: 25-30 hours/month

Month 3-4: Advanced Stata for Econometrics (80-100 hours total)

Objectives:

• Panel data methods: fixed effects, random effects, difference-in-differences
• Time-series: ARIMA, VAR, cointegration tests
• Instrumental variables and 2SLS
• Robust inference: bootstrap, heteroskedastic-robust SE, clustering

Resources:

• Textbook: “Microeconometrics Using Stata” by Cameron & Trivedi
• Textbook: “Introduction to Time Series Using Stata” by Becketti
• Online: StataCorp YouTube channel (panel and time-series tutorials)
• Community: Statalist forum (active Q&A)

Time: 35-45 hours/month

Milestone: By end of Month 4, you should be able to replicate published economics papers using panel data.

Month 5-6: Add R for Modern Econometrics (60-80 hours total)

Objectives:

• R basics and tidyverse (data manipulation with dplyr)
• Panel regression with fixest package (faster than Stata for large datasets)
• Time-series forecasting with fable and forecast packages
• Data visualization with ggplot2

Resources:

• Free Book: “R for Data Science” by Wickham & Grolemund (Chapters 1-5, 12-16)
• Economics-Specific: “Introduction to Econometrics with R” (free online textbook)
• Package vignettes: fixest, plm, fable documentation
• Course: DataCamp “Introduction to R for Finance” (time-series focus)

Time: 25-35 hours/month

Why R as secondary?

• Cutting-edge econometric methods appear in R first (e.g., high-dimensional fixed effects, machine learning for causal inference)
• Superior visualization for presentations and papers
• Industry data science roles value R + Python combo
• Complements Python skills you already have

---

PROS & CONS FOR YOUR SITUATION

Stata Pros

• ✅ Advisor compatibility - critical for PhD success
• ✅ Economics standard - journal replication packages, conference presentations
• ✅ Built for econometrics - panel data, time-series, IV methods are first-class features
• ✅ Learning curve - 2 months to productivity with your Python background
• ✅ Excellent documentation - economics-focused help files and examples

Stata Cons

• ❌ Cost - $595 for student perpetual license (Stata/MP), $395 for Stata/SE
• ❌ Closed-source - can’t inspect or modify underlying algorithms
• ❌ Data size limits - Stata/SE handles up to 2 billion observations, but slower than R/Python for very large datasets
• ❌ Graphics - basic plotting capabilities compared to R’s ggplot2

R Pros

• ✅ Free and open-source - always
• ✅ Cutting-edge methods - new econometric packages released continuously
• ✅ Data visualization - publication-quality graphics with ggplot2
• ✅ Complements Python - similar syntax, easy transition
• ✅ Industry marketability - valued outside academia

R Cons

• ❌ Not advisor’s tool - you’ll need to self-teach and debug alone
• ❌ Fragmented ecosystem - multiple packages for same task (plm, fixest, lfe)
• ❌ Steeper learning curve - 2-3 months with Python background

Why Not Python as Primary?

Python is excellent for computational economics, machine learning, and data engineering, but:

• Traditional econometrics (panel regression, IV, time-series) have better-maintained packages in Stata/R
• Economics journals rarely accept Python-only replication packages
• Your advisor can’t help debug Python econometric code
• Recommendation: Keep Python for data cleaning, simulation, and ML; use Stata/R for econometric inference

---

COST BREAKDOWN

Your Dual-Track Approach:

• Stata: $595 (Stata/MP student perpetual license) or $395 (Stata/SE)
  • Check if your university has lab access (may reduce cost to $0)
  • Perpetual license = no annual fees
• R: $0 (free forever)
• Textbooks: $100-200 (Wooldridge, Cameron & Trivedi)
  • Many available through university library
• Optional courses: $30-50/month (DataCamp subscription)

Total 6-month investment: $395-$645 (Stata) + $100-200 (books) = $495-$845

Cost-saving tip: Email your department to ask about Stata GradPlan (discounted multi-year license) or lab access.

---

NEXT STEPS

Week 1:

• Download Stata (30-day free trial) from stata.com
• Download R and RStudio (free) to have ready for Month 5
• Ask your advisor: “What Stata version do you use? Can you share sample do-files?”
• Join Statalist forum (statalist.org)

Week 2-4:

• Work through Stata’s built-in tutorials (help tutorials)
• Get Wooldridge textbook (library or purchase)
• Run first regression on practice dataset (Stata’s auto.dta)

Month 2-3:

• Replicate a simple economics paper from your field of interest
• Practice panel data cleaning and merging
• Learn about do-files and reproducible workflows

Month 4:

• Purchase Stata license (if not available through university)
• Master time-series commands: tsset, arima, var

Month 5-6:

• Install R packages: tidyverse, fixest, fable, ggplot2
• Re-run Stata analyses in R to compare workflows
• Build data visualization skills for presentations

---

QUESTIONS TO ASK YOUR ADVISOR

Before starting PhD:

• “What Stata version and packages do you recommend I install?”
• “Can you share example do-files from recent projects?”
• “Are there economics PhD students who could mentor me on Stata best practices?”
• “Does the department have computing resources or Stata lab access?”

During first semester:

• “Which econometric methods should I prioritize for my research area?”
• “Do you have opinions on using R alongside Stata?”

---

ALTERNATIVE SCENARIOS CONSIDERED

“What if I just use R?”

• Risk: Advisor can’t help debug, collaboration friction, journal norms
• Benefit: Free, modern methods, better graphics
• Verdict: Too risky for primary tool in economics PhD with Stata advisor

“What if I just use Python?”

• Risk: Limited econometric packages, journal acceptance issues, advisor mismatch
• Benefit: Leverage existing skills, industry marketability
• Verdict: Keep Python for data engineering, not primary econometric tool

“What if I just use Stata?”

• Risk: Missing out on R’s visualization and cutting-edge methods
• Benefit: Simpler, focused learning; advisor alignment
• Verdict: Safe choice, but adding R in Months 5-6 gives you competitive edge

---

Recommendation: Stata (Primary) + R (Secondary)
Confidence: 95% - This dual approach balances advisor collaboration, field norms, and career development while leveraging your Python background.