Impulse responses can be estimated to analyze the effects of a shock to a variable over time. Typically, (vector) autoregressive models are estimated and the impulse responses implied by the coefficients calculated. In general, however, there is no knowledge of the correct autoregressive order. In fact, when models are seen as approximations to the data generating process (DGP), all models are imperfect and there is no a priori difference in their validity. Hence, a lag length should be chosen by a sensible method, for instance an information criterion. In Monte Carlo simulations, this paper studies what characteristics influence the optimal autoregressive order when all models are only approximations to the DGP. It finds that the precise coefficients in the DGP, the sample size, and the impulse response horizon to be estimated all influence the mean squared error-minimizing lag length. Furthermore, it evaluates the performance of model selection and averaging methods for estimating impulse responses. Across the characteristics found to be relevant, averaging outperforms model selection, and in particular Mallows' Model Averaging and a smoothed Hannan-Quinn Information Criterion perform best. Finally, the study is extended to vector autoregressive models. In addition to the characteristics relevant in the univariate case, the optimal lag length also depends on which (cross) impulse response is to be estimated. Many issues remain for vector autoregressive models, however, and more work is necessary
