We present non-parametric identification results for panel models in the presence of a vector of unobserved heterogeneity that is not additively separable in the structural function. We exploit the time-invariance and finite dimension of the heterogeneity to achieve identification of a number of objects of interest with the panel length fixed. Identification does not require that the researcher have access to an instrument that is uncorrelated with the unobserved heterogeneity. Instead the identification strategy relies on an assumption that some lags and leads of observables are independent conditional on the unobserved heterogeneity and some controls. The identification strategy motivates an estimation procedure based on penalized sieve minimum distance estimation in the non-parametric instrumental variables framework. We give conditions under which the estimator is consistent and derive its rate of convergence . We present Monte Carlo evidence of its efficacy in finite samples .
We present a novel approach to nonparametric identification and consistent estimation in economic models using `proxy controls'. Our approach is particularly well-suited to the context of panel data with a fixed time-dimension but also applies in cross-sectional settings. Proxy controls are proxies for unobserved `perfect controls', where perfect controls are variables that are sufficient for the association between potential outcomes and treatments. Our identification strategy requires that the set of available proxy controls be split into two subsets, one subset acting as an instrument for the other. In the panel case, observations from different periods can be used as proxy controls and our key identifying assumptions follow from restrictions on the serial dependence of the data. Our estimation problem is (under our assumptions) `well-posed' and our estimator is straight-forward to implement. The key step is a penalized sieve minimum distance estimator. We derive simple convergence rates under high-level assumptions .
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non-parametric identification results for panel models in the presence of a vector of unobserved heterogeneity that is not additively separable in the structural function. We exploit the time-invariance and finite dimension of the heterogeneity to achieve identification of a number of objects of interest with the panel length fixed. Identification does not require that the researcher have access to an instrument that is uncorrelated with the unobserved heterogeneity. Instead the identification strategy relies on an assumption that some lags and leads of observables are independent conditional on the unobserved heterogeneity and some controls. The identification strategy motivates an estimation procedure based on
a novel approach to nonparametric identification and consistent estimation in economic models using `proxy controls'. Our approach is particularly well-suited to the context of panel data with a fixed time-dimension but also applies in cross-sectional settings. Proxy controls are proxies for unobserved `perfect controls', where perfect controls are variables that are sufficient for the association between potential outcomes and treatments. Our identification strategy requires that the set of available proxy controls be split into two subsets, one subset acting as an instrument for the other. In the panel case, observations from different periods can be used as proxy controls and our key identifying assumptions follow from restrictions on the serial dependence of the data. Our estimation problem is (under our assumptions) `well-posed' and our estimator is straight-forward to implement. The key step is a
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estimation in the non-parametric instrumental variables framework. We give conditions under which the estimator is consistent and derive its rate of convergence . We present Monte Carlo evidence of its efficacy in finite samples
estimator. We derive simple convergence rates under high-level assumptions