This paper deals with a variety of dynamic issues in the analysis of time-series–cross-section (TSCS) data raised by recent papers; it also more briefly treats some cross-sectional issues. Monte Carlo analysis shows that for typical TSCS data that fixed effects with a lagged dependent variable performs about as well as the much more complicated Kiviet estimator, and better than the Anderson-Hsiao estimator (both designed for panels). It is also shown that there is nothing pernicious in using a lagged dependent variable, and all dynamic models either implicitly or explicitly have such a variable; the differences between the models relate to assumptions about the speeds of adjustment of measured and unmeasured variables. When adjustment is quick it is hard to differentiate between the models, and analysts may choose on grounds of convenience (assuming that the model passes standard econometric tests). When adjustment is slow it may be the case that the data are integrated, which means that no method developed for the stationary case is appropriate. At the cross-sectional level, it is argued that the critical issue is assessing heterogeneity; a variety of strategies for this assessment are discussed.