Moment Inequalities and Partial Identification in Industrial Organization /
Kline, Brendan.
Moment Inequalities and Partial Identification in Industrial Organization / Brendan Kline, Ariel Pakes, Elie Tamer. - Cambridge, Mass. National Bureau of Economic Research 2021. - 1 online resource: illustrations (black and white); - NBER working paper series no. w29409 . - Working Paper Series (National Bureau of Economic Research) no. w29409. .
October 2021.
We review approaches to identification and inference on models in Industrial Organization with partial identification and/or moment inequalities. Often, such approaches are intentionally built directly on assumptions of optimizing behavior that are credible in Industrial Organization settings, while avoiding the use of strong modeling and measurement assumptions that may not be warranted. The result is an identified set for the object of interest, reflecting what the econometrician can learn from the data and assumptions. The chapter formally defines identification, reviews the assumptions underlying the identification argument, and provides examples of their use in Industrial Organization settings. We then discuss the corresponding statistical inference problem paying particular attention to practical implementation issues.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.
Moment Inequalities and Partial Identification in Industrial Organization / Brendan Kline, Ariel Pakes, Elie Tamer. - Cambridge, Mass. National Bureau of Economic Research 2021. - 1 online resource: illustrations (black and white); - NBER working paper series no. w29409 . - Working Paper Series (National Bureau of Economic Research) no. w29409. .
October 2021.
We review approaches to identification and inference on models in Industrial Organization with partial identification and/or moment inequalities. Often, such approaches are intentionally built directly on assumptions of optimizing behavior that are credible in Industrial Organization settings, while avoiding the use of strong modeling and measurement assumptions that may not be warranted. The result is an identified set for the object of interest, reflecting what the econometrician can learn from the data and assumptions. The chapter formally defines identification, reviews the assumptions underlying the identification argument, and provides examples of their use in Industrial Organization settings. We then discuss the corresponding statistical inference problem paying particular attention to practical implementation issues.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.