Hey there, let's walk through how to tackle your binary self-rated health regression in Stata, given your panel data with waves at Year 0, Year 5, and Year 10 (we'll ignore Year 10 per your note). First, quick recap: Year 0 has 1095 respondents reporting binary_health_y, while Year 5 has 558—we'll need to account for that sample shrinkage too.

1. 数据导入与面板结构设置 #

First up, get your Excel data into Stata and set up the panel structure properly:

• Import the Excel file (make sure your first row has variable names):

  import excel "your_data_file.xlsx", firstrow clear
  

• Define the panel with your unique respondent ID and wave variable (assuming you have respondent_id for individuals and wave coded as 0/5/10):

  xtset respondent_id wave
  

• Filter to keep only the waves you care about:

  keep if wave == 0 | wave == 5
  

2. 处理样本流失问题 #

The big gap between Year 0 (1095) and Year 5 (558) respondents needs checking first. Let's diagnose the issue:

• Run a cross-tab to see if missingness or attrition is the cause:

  tabulate wave binary_health_y, miss
  

You have two main options for sample selection:

• Balanced panel: Only keep respondents who have data in both waves (this will cap your sample at 558, assuming Year 5 respondents are a subset of Year 0):

  xtset respondent_id wave
  keep if e(sample) // Retains only individuals with non-missing data across both waves
  

• Unbalanced panel: Keep all respondents with valid data in either wave—Stata's panel commands support this by default, but you'll want to note potential selection bias in your write-up.

3. 二元自评健康的回归分析 #

Since binary_health_y is a binary outcome (good/bad), here are the most appropriate methods for panel data:

固定效应Logit模型（推荐，控制个体固定异质性） #

This model accounts for unobserved, time-invariant individual characteristics (like genetic predisposition) that might affect health:

xtlogit binary_health_y your_time_varying_covariates, fe

Note: Fixed effect logit automatically drops individuals who have the same binary_health_y value in both waves (e.g., always healthy or always unhealthy)—these cases can't help identify the effect of your covariates.

To make results easier to interpret, add the or option to get odds ratios instead of log-odds:

xtlogit binary_health_y your_time_varying_covariates, fe or

随机效应Logit模型（if you assume individual heterogeneity is unrelated to covariates） #

Use this if you believe unobserved individual traits don't correlate with your independent variables:

xtlogit binary_health_y your_time_varying_covariates, re

You can test whether fixed or random effects is better with a Hausman test:

estimates store fe_model
estimates store re_model
hausman fe_model re_model

混合Logit模型（simpler, no panel adjustments） #

If you don't want to account for individual heterogeneity, treat the data as a pooled cross-section:

logit binary_health_y your_time_varying_covariates, robust

Make sure your covariates are matched to the correct wave (e.g., use Year 0 behavior data for Year 0 health, Year 5 behavior for Year 5 health).

4. 稳健性检验 #

• Check for attrition bias: Test whether Year 0 characteristics predict whether a respondent dropped out by Year 5:

  gen attrition = (wave == 0 & missing(binary_health_y[wave=5]))
  probit attrition your_year0_covariates if wave == 0
  

• Sensitivity analysis: Compare results from balanced vs unbalanced panels to see if sample selection changes your conclusions.

内容的提问来源于stack exchange，提问作者John