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This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox, Kim, Ryan, and Bajari (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing...
Persistent link: https://www.econbiz.de/10012099239
This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox, Kim, Ryan, and Bajari (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing...
Persistent link: https://www.econbiz.de/10012109678
Persistent link: https://www.econbiz.de/10011894391
frequencies and numerical simulations illustrate this point. In addition, this estimation methodology shows that the elasticity of … frequency data generate better estimates of output- employment elasticity while high frequency data generate better estimates of … output-average hours elasticity. This result comes from the fact that time aggregation increases (decreases) the bias in the …
Persistent link: https://www.econbiz.de/10014084107
There is a long standing controversy over the magnitude of the Frisch labor supply elasticity. Macro economists using … of the OLS bias, which is negative. As a result, Frisch elasticity estimates near zero appear (spuriously) precise, while … positive Frisch elasticity. Fortunately, the Anderson-Rubin (AR) test does not su ff er from this power asymmetry problem. The …
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