How the (Econometric) Sausage is Made

Structural breaks. The stuff of time series. You cannot be a time series econometrician and not be well-versed in the importance of allowing for, testing for, and dealing with structural breaks (or so I am told). However, surely there is something to to be learned from this literature that applied microeconometricians can utilize, no? Spoiler: Yes, there is! Applied microeconometricians, who may be unaware of the tremendous advances in the literature on structural breaks, would be wise to take notice. While break dancing may not have advanced since last century and may have little to offer to today's generation, testing for structural breaks has advanced and has much to offer. To understand, let us review. A structural break refers to any change in the underlying data-generating process (DGP). Some fraction of the sample is drawn from one DGP; some other fraction is drawn from another DGP. Of course, there may be more than one structural break and, hence, more th...

If you think this post is about inflation of the macro variety, you have come to the wrong place! This is about inflation of the econometric (and typically micro) variety. When modeling discrete data -- count data or otherwise -- we often find that one value occurs much more frequently than the rest. Standard models for discrete outcomes (usually referred to as limited dependent variable models) do not fit the data well in such situations because they have some type of "smoothness" (implicitly) built in to the assumed data-generating process. This causes the estimated model to under-predict the frequently occurring outcome and over-predict other outcomes. To better fit the data in such situations, so-called "inflated" limited dependent variable models were created. As far as I know, early work in this area cites Cragg (1971) and Mullahy (1986). The literature started with count data models when zeros occurred with high frequency in the sample. This led t...