Paul T. Scott
NYU Stern School of Business
Kaufman Management Center, 7-77
New York University
New York, NY 10012
Institutional Affiliation: New York University
Information about this author at RePEc
NBER Working Papers and Publications
|March 2020||Comment on "Quantifying Heterogeneous Returns to Adoption of Genetic Technology: The Case of the Dairy Industry"|
in Economics of Research and Innovation in Agriculture, Petra Moser, editor
|October 2018||Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models|
with , : w25134
In structural dynamic discrete choice models, unobserved and mis-measured state variables may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address such measurement problems when they relate to state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent's entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computation- ally light. We provide constructive arguments for the identification o...
|December 2016||Estimating market power Evidence from the US Brewing Industry|
with : w22957
While inferring markups from demand data is common practice, estimation relies on difficult-to-test assumptions, including a specific model of how firms compete. Alternatively, markups can be inferred from production data, again relying on a set of difficult-to-test assumptions, but a wholly different set, including the assumption that firms minimize costs using a variable input. Relying on data from the US brewing industry, we directly compare markup estimates from the two approaches. After implementing each approach for a broad set of assumptions and specifications, we find that both approaches provide similar and plausible markup estimates in most cases. The results illustrate how using the two strategies together can allow researchers to evaluate structural models and identify problema...
|September 2015||Identification of Counterfactuals in Dynamic Discrete Choice Models|
with , : w21527
Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non-identification of models does not necessarily imply the non-identification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non- additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry/exit problem numerically, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and non-identified counterfactuals of interest.