The article develops tests to detect structural changes in panel data with common and individual trends. The researchers show that their tests are effective in detecting breaks of different sizes and when changes occur at different times for different units. They also extend their framework to handle correlated variables and endogeneity. Monte Carlo simulations confirm that the tests have the right size and good power to detect breaks in the data.