This paper proposes a reconciliation between two approaches to structural estimation and inference: Using a complete - yet approximate model versus imposing a set of credible behavioral conditions. This is done by distorting the approximate model to satisfy these conditions. We provide the asymptotic theory and Monte Carlo evidence, and illustrate that counterfactual experiments are possible. We apply the methodology to the model of long run risks in aggregate consumption (Bansal and Yaron, 2004), where the complete model is generated using the Campbell and Shiller (1988) approximation. Using US data, we investigate the empirical importance of the neglected non-linearity. We find that distorting the model to satisfy the original equilibrium condition is strongly preferred by the data and substantially improves the identification of the structural parameters. Tilting also restores key qualitative predictions of the non-linear model such as the endogenous time variation in risk premia, a property which is lost with linearization .
We propose a method that reconciles two popular approaches to structural estimation and inference: Using a complete - yet approximate model versus imposing a set of credible behavioral conditions. This is done by distorting the approximate model to satisfy these conditions. We provide the asymptotic theory and Monte Carlo evidence, and illustrate that counterfactual experiments are possible. We apply the methodology to the model of long run risks in aggregate consumption (Bansal and Yaron, 2004), where the complete model is generated using the Campbell and Shiller (1988) approximation. Using US data, we investigate the empirical importance of the neglected non-linearity. We find that distorting the model to satisfy the non-linear equilibrium condition is strongly preferred by the data while the quality of the approximation is yet another reason for the downward bias to estimates of the intertemporal elasticity of substitution and the upward bias in risk aversion .
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This paper proposes a reconciliation between two
We propose a method that reconciles two popular
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original
non-linear
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and substantially improves the identification of the structural parameters. Tilting also restores key qualitative predictions of the non-linear model such as the endogenous time variation in risk premia, a property which is lost with linearization
while the quality of the approximation is yet another reason for the downward bias to estimates of the intertemporal elasticity of substitution and the upward bias in risk aversion