Gains:
- Being able to distinguish where in the empirical workflow (data cleaning, code, visualization, draft interpretation) artificial intelligence saves real time and where decisions such as model selection, causality claim and publication decision are left to the expert, according to the task risk level
- Ability to apply a discipline that verifies each artificial intelligence output (number, coefficient, code, comment) through the steps of recalculation, linking to the source and statistical filtering.
- Ability to safely process micro data and confidential/licensed data sets within the scope of KVKK/GDPR and data usage agreements and acquire the habit of choosing appropriate tools.
The same questions recur every day on an econometrician or applied statistician's desk: Is this data set reliable; What to do with these missing values? What does the coefficient of this regression really say? Is this relationship causal or merely correlational? Because this p-value is significant, can I write it in the article or policy brief? Some of these questions are repetitive, code-intensive, and time-consuming: cleaning data, plotting the same graph ten times, debugging R or Python code, stringing the results into a table. Others directly determine the validity of a finding, the honesty of a publication, or the accuracy of a policy decision.
Artificial intelligence (AI in short, AI - computer systems that can produce text and code like humans, recognize patterns, summarize data and write comments; the most used form today are large language models, that is, systems that are trained on huge text and build sentences by predicting the next word) sits right in the middle of this table. When used correctly, it reduces hours of data cleaning code to minutes, reminds you of the syntax of a complex statistical test, or drafts the interpretation of a coefficient. When used incorrectly, it presents a seemingly sure but completely fabricated number, a false significance, or a non-causal relationship as a "finding".
This first unit is not a software introduction. Its purpose is to clarify where to put AI in your empirical workflow and where never to put it. Econometrics is both a "method-critical" and indirectly "decision-critical" field: the coefficient of a model you build can be the input to a central bank's interest rate decision, a regulator's policy change, or a firm's million-dollar investment. Let's lay out the basic principle from the beginning: Artificial intelligence is an analysis assistant, not a researcher. Responsibility for and final approval of model selection, identification strategy, assertion of causality, publication and policy decisions rests with the competent expert.
Layers of the empirical workflow and the place of AI
It is useful to divide an empirical study into three layers. The execution layer is the day-to-day technical work: data cleaning code, graph drawing, table formatting, syntax debugging. The method layer is the analytical decision: which model, which identification strategy, which test, which assumption checking. The interpretation and decision layer is the meaning of the findings: what the coefficient says, is it causal, what does it mean for policy, should it be published. AI touches all three layers, but with different authority in each. At the execution layer, AI quickly drafts and generates code; At the interpretation and decision layer, only input is given and the expert makes the decision.
Let's define a few basic terms from the beginning. Econometrics is the branch that analyzes economic and social data with statistical methods and measures relationships. Regression is a method that numerically models the relationship of an outcome variable (dependent variable) with one or more explanatory variables (independent variables). The coefficient is the number that shows how many units of change on average a one-unit change in an explanatory variable is associated with in the outcome variable. The p-value is the probability that the observed outcome (or a more extreme outcome) would occur by chance when no effect actually exists. We will explain these concepts one by one in the following units; For now, know this: In all of these, AI gives you the blueprint, code, and explanation, but it doesn't make scientific decisions.
The following table summarizes the role and risk level of AI by mission:
Quest
Role of AI
Risk level
Who approves
Writing data sanitization code
code generator
low
Analyst himself (with code testing)
Chart/table draft
sketch generator
low
analyst
Test/model syntax reminder
Reference assistant
low-medium
analyst
Draft coefficient interpretation
draft writer
medium
econometrician
Model/identification strategy selection
auxiliary input
high
expert researcher
Causation claim
Just a draft, never the final word
very high
Expert + peer review
Publication/policy decision
input only
very high
Responsible investigator/institution
Keep in mind the one line in this table: as risk rises, the role of AI shrinks and expert approval grows.
Why "verification" is the heart of this business
Language models seem confident in their answer, but they may not be sure. In technical language, this is called hallucination: it is the model's fabrication of non-existent information in a fluent sentence, just as if it were true. For an econometrician, this is a deadly trap. The model can give you an exact number such as "The correlation between unemployment and inflation in Türkiye between 2015-2020 is 0.62"; However, he has never seen your data and this number is completely made up. Or it might say, “White test p-value was 0.03”; whereas it did not run any tests. It can call an R function with an argument that doesn't actually exist. He sings all three with the same fluency; The only thing that separates right from wrong is your knowledge and your habit of verifying.
The verification discipline consists of three steps:
- Recalculate / run in your own environment: Calculate every number, ratio, test result and coefficient given by the AI in R/Python with your own data, not from the AI's memory. Use the AI to write the code, not to remember the result. Run the code, you produce the output.
- Link to source: Rely on textbooks, methods articles, and official documents for method rules, formulas, and definitions. Confirm the AI's statement "the assumption of this test is" with a reliable source.
- Statistical filter: Test with expert eyes whether the output contradicts statistical logic (assumptions, sample, effect size, uncertainty). Reject a "meaningful but absurd" result.
Caution: Putting an AI-generated coefficient, test, or interpretation in an article, thesis, or policy note without validating it is like publishing an unreviewed finding. Just because the output is fluent is not true; Just because the number seems certain, it is not real.
Privacy: micro data and licensed data are protected privately
Microdata (single records at the individual, household, firm or patient level) are frequently used in econometrics. Most of this data is personal data and is protected under KVKK (Personal Data Protection Law) in Türkiye and GDPR in Europe. Additionally, TurkStat, central banks, panel data providers and commercial sources often provide data with a data usage agreement; These agreements prohibit transferring data to third parties. Pasting a table containing identity information, income, health or company secrets into a public AI chat tool is both a KVKK/GDPR violation and a contract violation; because the data goes to an external server and can be used in model training in some tools.
The rule is simple: keep the data in its own safe environment, ask the AI only for code and methods. The ideal workflow is this: ask the AI for the analysis code, you run the code with the real data on your own computer/enterprise server. If you really must share data, anonymize (remove ID fields), aggregate (average/count rather than individual), and choose tools that are institutional, have a data processing agreement, and do not use your data in education.
three mini cases
Case 1 — Safe and efficient use. One researcher first spent 6 hours manually cleaning 40,000 rows of household budget data. Without sharing the data at all, he just described the variable names and structure to the AI and asked for a cleaning code. The AI generated an R script; The researcher ran the code in his own environment, checked the number of lines and summary statistics of each step, and fixed two logic errors. Duration: 70 minutes instead of 6 hours. The data never went out; The responsibility remained with the researcher.
Case 2 — Unverified number trap. “What is the elasticity between GDP growth and foreign direct investment,” a graduate student asked AI. The AI confidently gave a number of "about 0.34" even though it had no access to any data. The student wrote this in the literature summary; When his advisor asked about the source, it turned out that the number had no basis and was completely fabricated. Mistake: Expecting concrete statistics from AI without giving data and resources to it.
Case 3 — Confidentiality and breach of contract. An analyst uploaded Excel, which he received from a licensed company panel, containing the company name and turnover, to a publicly available AI tool and said "do panel regression." The data provider's contract prohibited the transfer of data to third-party systems; The incident prompted a compliance review. The right way was to replace the company names with anonymous codes or to share no data and only request the panel regression code and run it in its own environment.
Weak prompt / Strong prompt
Weak prompt:
Analyze the relationship between inflation and unemployment and state the coefficient.
This claim is wrong: No data was given to the AI, it is not clear which country, which period, which model. AI can only answer with a made-up coefficient.
Powerful prompt:
Your role: you are an analysis assistant assisting the econometrics researcher. I DO NOT share the data with you; I just want RUNNABLE R code. I have annual panel: variables country, year, unemployment, inflation (all numeric). Task: 1) write a fixed effects panel regression code for unemployment ~ inflation (plm package), 2) explain each step in the code with a comment line, 3) note that the coefficient should be interpreted as relational, not CAUSAL, 4) make up the function argument that you are not sure about; I will run and verify the result in my own environment.
In this request, no data is shared, the role, packages, expectation of comments and the prohibition of "fabrication" are clear. You still run and verify the output yourself.
The following table summarizes the one-sentence rules in this lesson:
principle
What does it mean
AI is an assistant
Scientific decision and responsibility belongs to the expert
Request the code, produce the result
The AI doesn't remember the number; you run it and calculate
keep data
Micro/licensed data does not leave the secure environment
verify
Every issue, code, comment is checked; fabrication is rejected
Common mistakes
- Expecting concrete statistics from AI without giving data. The model cannot access the data; The coefficient, correlation and p-value it gives are fake. Always request the code and produce the result yourself.
- Relying on hallucinatory functions. AI may suggest an R/Python function or argument that does not exist. Do not accept the code without running and verifying it.
- Uploading microdata to public tool. It is a violation of KVKK/GDPR and data usage agreement. Anonymize data or don't share it at all.
- Mistaking fluency for accuracy. A well-written review may be inaccurate. The beauty of the sentence is not proof.
- Accepting causal language without control. YZ often says “X increases Y”; whereas most regressions only show relationships. Defend the causal claim with design.
Tip: When working with AI, ask yourself this question: “If a referee or auditor asks for this output, can I show the source and account?” If the answer is no, the output is not yet ready for use.
In summary
AI provides a real and massive acceleration in the execution layer (code, cleanup, graph, draft) of the econometrics and statistics workflow; however, model selection, causality, interpretation, publication and policy decisions belong to the expert. Three basic disciplines: don't remind the AI of the number, ask for the code and generate and verify the result yourself; do not remove micro/licensed data from the secure environment; statistical filter each output. An unverified AI output is just as risky as an unreviewed finding.
Application task
Consider your own (or an example) data set. Ask the AI for R or Python code that produces descriptive statistics and a simple graph by simply describing the variable structure, without sharing the data. Run the incoming code in your own environment. Then keep a checklist: (1) did the code run without errors, (2) is the number of lines/observations as you expected, (3) which of the AI's comment sentences uses causal language and should be fixed. In one paragraph, write about the time AI saved you and at least one bug you caught.
checklist
- [ ] I didn't make the AI ask for concrete statistics; I requested the code and produced the result myself.
- [ ] I recalculated every number and test result it gave in my own environment.
- [ ] I verified that the functions/arguments it uses actually exist.
- [ ] I did not upload micro/personal/licensed data to a public tool.
- [ ] I marked the comments containing causal language and moved them to relational language.
- [ ] I am able to show the source and accounting of the output to an auditor.