As an example, I implement specification tests for dependence across dyads, such as reciprocity or transitivity.
I also show how the jackknife can be used to bias-correct fixed effect averages over functions that depend on multiple nodes, e.g. Additionally, since the jackknife estimates all parameters in the model, including fixed effects, it allows researchers to construct estimates of average effects and counterfactual outcomes. In contrast to previous proposals, the jackknife approach is easily adaptable to different models and allows for non-binary outcome variables. I develop a jackknife bias correction to deal with the incidental parameters problem that arises from fixed effect estimation of the model. Hughes (Boston College), " Estimating Nonlinear Network Data Models with Fixed Effects", (11/2021 PDF)Ībstract: This paper considers estimation of a directed network model in which outcomes are driven by dyad-specific variables (such as measures of homophily) as well as unobserved agent-specific parameters that capture degree heterogeneity. "Machine Learning: An Applied Econometric Approach.1058. We hope to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble-and thus where they can be most usefully applied. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. So applying machine learning to economics requires finding relevant tasks. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. Machine learning not only provides new tools, it solves a different problem. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. Machines are increasingly doing "intelligent" things.
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