Estimating true changes when categorical panel data are affected by uncorrelated and correlated classification errors - An application to unemployment data

Conclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classifi cation errors, usually lead to over-estimation of the total amount of gross change, whereas systematic, correlated errors usually cause underestimatio n of the transitions. Furthermore, the patterns of true change may be serio usly distorted by independent or systematic classification errors. Latent c lass models and directed log-linear analysis are excellent tools to correct for both independent and correlated measurement errors. An extensive examp le on labor market stares taken from the Survey of Income and Program Parti cipation panel is presented.