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Choose estimators for panel rent regressions

Last updated: Mar 29, 2026

Quick Overview

This question evaluates expertise in panel-data econometrics and time-series diagnostics, testing skills in fixed-effects specification, robust standard-error selection, unit-root and cointegration testing, error-correction interpretation, and multicollinearity handling for elasticity estimation.

  • hard
  • Freddie Mac
  • Statistics & Math
  • Data Scientist

Choose estimators for panel rent regressions

Company: Freddie Mac

Role: Data Scientist

Category: Statistics & Math

Difficulty: hard

Interview Round: Onsite

You have monthly panel data for properties i in MSAs m from 2010-01 to 2025-06: rent_it, vacancy_it, amenities_i, msa_unemp_mt, CPI_t, interest_t. You observe serial correlation and heteroskedasticity. (1) Write a regression to estimate the elasticity of rent to vacancy with property and month fixed effects; justify log transforms and seasonal terms. (2) Specify which standard errors you would use and why: two-way clustering by property and month vs Driscoll–Kraay vs Newey–West; discuss finite-sample implications. (3) Test for unit roots and cointegration in ln(rent) and ln(vacancy); if both are I(1), outline Engle–Granger or Johansen steps to fit an ECM and avoid spurious regression. (4) Interpret beta = -0.35 on ln(vacancy): compute the predicted percent change in rent for a 2 percentage-point vacancy increase from 8% to 10% at the MSA level. (5) Diagnose multicollinearity among macro regressors; propose remedies (orthogonalization, ridge, Bayesian priors) and how they affect inference.

Quick Answer: This question evaluates expertise in panel-data econometrics and time-series diagnostics, testing skills in fixed-effects specification, robust standard-error selection, unit-root and cointegration testing, error-correction interpretation, and multicollinearity handling for elasticity estimation.

Freddie Mac logo
Freddie Mac
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Statistics & Math
4
0

Panel Rent–Vacancy Elasticity and Inference: Design, SEs, and Time-Series Diagnostics

Context

You have a monthly property-level panel from 2010-01 to 2025-06. For property i in MSA m at month t, you observe:

  • rent_it (asking rent), vacancy_it (vacancy rate), amenities_i (time-invariant),
  • msa_unemp_mt (MSA unemployment), CPI_t (national CPI), interest_t (national rate).

You observe serial correlation and heteroskedasticity. Answer the following:

Tasks

  1. Model specification: Write a regression to estimate the elasticity of rent with respect to vacancy using property and month fixed effects. Justify log transforms and seasonal terms.
  2. Standard errors: Specify which SEs you would use and why. Compare two-way clustering by property and month versus Driscoll–Kraay versus Newey–West, including finite-sample implications.
  3. Nonstationarity: Test for unit roots and cointegration in ln(rent) and ln(vacancy). If both are I(1), outline Engle–Granger or Johansen steps to fit an Error Correction Model (ECM) to avoid spurious regression.
  4. Interpretation: With an estimated coefficient β = −0.35 on ln(vacancy), compute the predicted percent change in rent when vacancy increases by 2 percentage points (from 8% to 10%) at the MSA level.
  5. Multicollinearity: Diagnose multicollinearity among macro regressors and propose remedies (orthogonalization, ridge, Bayesian priors). Explain how these affect inference.

Solution

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