52 search results for "farmers' markets"

Suppose you have the following estimable equation:

(1) [math]y_{it} = \alpha_{i} + \beta {x}_{it} + \epsilon_{it}[/math].

This is a pretty standard equation when dealing with panel data: [math]i[/math] denotes an individual in the set [math]i \in \{1,…,N\}[/math], [math]t[/math] denotes the time period in the set [math]t \in \{1,…,T\}[/math], [math]y[/math] is an outcome of interest (say, wage), [math]x[/math] is a variable of interest (say, an indicator variable for whether someone has a college degree), [math]\alpha[/math] is an individual fixed effect, and [math]\epsilon[/math] is an error term with mean zero. Normally with longitudinal data, it is the case that [math]N > T[/math], so that there are more individuals in the data than there are time periods. (If [math]T > N[/math], you are likely dealing more with a time-series problem than with a typical applied micro problem.)

Though we are normally interested in estimating and identifying the relationship between the variable of interest [math]x[/math] and the outcome variable [math]y[/math], I wanted to focus today on heteroskedasticity.*

Last week I discussed U-shaped relationships, and how to test for them. This week, I would like to discuss higher-order nonlinear relationship, or relationships that are “more nonlinear” than U-shaped relationships.

There are many ways one can approach the estimation of nonlinear relationships. I will focus only on a handful of them in this post, from least to most nonlinear, and from semiparametric to nonparametric.

A good first step beyond the estimation of a U-shaped relationship would be to estimate the equation

Last month, Ben Chapman and Don Schaffner, who host the Food Safety Talk podcast, discussed my January Gray Matter column in the New York Times in January, in which I discussed my work on farmers markets and food-borne illness.

Their discussion was even-handed, and Don (I think it was him; I listened to the segment only once, over a month ago) demonstrated a surprising understanding of the working paper culture in economics, wherein we circulate working papers well ahead of submitting for publication so as to make our work better in view of publishing it in better journals. But the one part which made my ears perk up was when Ben asked Don (or the other way around; again, it’s been a while since I listened) why my coauthors and I had looked at the relationship between farmers markets and all those seemingly irrelevant illnesses, and Don said (and I’m paraphrasing), “I don’t know, it looks like data mining.”