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Summary Brady (Journal of Applied Econometrics, 2011, 26(2), 213–231) studies how fast and how long a change in housing prices in one region affects its neighbors by estimating the impulse response functions using a spatial autoregressive model (SAR). This paper replicates Brady's empirical results, but reports different SAR test statistics. Additional robustness checks are conducted by analyzing three different housing price indexes covering a more extensive period. Analysis shows that the model specifications and model estimates vary with the housing price indexes.

This paper uses a difference‐in‐differences strategy and an event‐study analysis to evaluate the effect of Uber entries on vehicle miles travelled in 346 US Metropolitan statistical areas between 2011 and 2017. Empirical results demonstrate that Uber entries have no significant effect on the total vehicle miles travelled in a city. However, Uber is found to decrease the vehicle miles travelled on highways but increase them on collector roads. All these effects come mainly from large metropolitan statistical areas. A simulation exercise suggests that Uber entries cause longer travel times if the share of travel time spent on collector roads is high.

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