Modeling How Individual-Level Characteristics Impact Inflation Prediction Error

About

This project explores the determinants of inflation prediction errors using data from the Survey of Consumer Expectations (SCE) from 2013 to 2023, combined with monthly national and annual state-level Personal Consumption Expenditures (PCE). The analysis involves data merging, variable creation, and regression modeling, all implemented in Stata.

Repository Contents

1. Stata data/

Contains all .dta files used in the project:

• Raw Data/ – Individual files used during the merging process in Data Merging.do
• SCE 2013–2023 Merged Final.dta – Cleaned and fully merged dataset used in Models.do

2. Do/

Includes all Stata scripts used throughout the workflow:

• Data Merging.do – Merges, restructures, and appends the raw datasets
• Variable Creation.do – Generates derived variables and transformations for modeling
• Models.do – Runs linear regression, LOGIT, and Multinomial LOGIT models analyzing the determinants of inflation prediction error

3. Project Paper

• Erik Franke - Modeling Inflation Prediction Error.pdf**
  Full research write-up, including:
  • Introduction & Background
  • Literature Review
  • Conceptual Framework & Hypotheses
  • Data & Methodology
  • Results & Analysis
  • Conclusion & Policy Implications
  • References & Appendices

Usage Instructions

1. Open Stata.
2. Run Data Merging.do to generate the merged dataset (if starting from raw data).
3. Run Variable Creation.do to construct all necessary variables.
4. Run Models.do to execute regression analyses and produce output.
5. Refer to the PDF for detailed explanations, methodology, and interpretation of results.

Contact

Erik Franke
efranke@falcon.bentley.edu
Bentley University Academic Technology Center | Research Assistant