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- W4387608970 abstract "Abstract We study identification in a binary choice panel data model with a single predetermined binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter $$theta $$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mi>θ</mml:mi> </mml:math> , whereas the distribution of unit-specific heterogeneity, as well as the feedback process that maps lagged outcomes into future covariate realizations, is left unrestricted. We provide a simple condition under which $$theta $$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mi>θ</mml:mi> </mml:math> is never point-identified, no matter the number of time periods available. This condition is satisfied in most models, including the logit one. We also characterize the identified set of $$theta $$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mi>θ</mml:mi> </mml:math> and show how to compute it using linear programming techniques. While $$theta $$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mi>θ</mml:mi> </mml:math> is not generally point-identified, its identified set is informative in the examples we analyze numerically, suggesting that meaningful learning about $$theta $$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mi>θ</mml:mi> </mml:math> may be possible even in short panels with feedback. As a complement, we report calculations of identified sets for an average partial effect and find informative sets in this case as well." @default.
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- W4387608970 date "2023-10-13" @default.
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- W4387608970 title "Identification in a binary choice panel data model with a predetermined covariate" @default.
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- W4387608970 doi "https://doi.org/10.1007/s13209-023-00290-2" @default.
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