I have aggregate data on $L_t, K_t$ and $X_t$, and want to estimate elasticity of substitution parameters, $\gamma$ and $\sigma$ for these factors. Assuming the production function takes the following form: $$Y_t=(A_lL^{\gamma}+[A_kK_t^{\sigma} +A_xX_t^{\sigma}]^\frac{\gamma}{\sigma})^{\frac{1}{\gamma}} $$

Technology parameters, $A$s are not observable and hence need to be controlled for in an econometric specification. I am thinking of first estimating the parameter, $\sigma$, on the inner CES function combining $K$ and $X$. Then I should be able to estimate the outer CES parameter, $\gamma$. In a way, the two parameters are not jointly estimated. Would this method be valid, I mean, in statistical sense? Will I get consistent and unbiased estimates? I've read papers on non-linear regression methods, but was wondering if this simple approach is feasible. Thanks.

  • $\begingroup$ This is related to the question I asked. The linked reference therein estimates a normalized nested CES production function. Might be of interest. $\endgroup$ May 1 '16 at 16:24
  • 2
    $\begingroup$ Possible duplicate of CES production function estimation $\endgroup$
    – luchonacho
    Feb 25 '17 at 15:20
  • $\begingroup$ Doesn't look like a duplicate. That linked question is a two input production function and the above is a three input production function that is nested. $\endgroup$
    – BKay
    Feb 27 '17 at 13:08

Neglecting technology parameters and assuming constant returns to scale, the parameters $\sigma$ and $\gamma$ are jointly estimable via dynamic (least squares) programming. See this paper.

  • $\begingroup$ I cannot ignore the technology parameters. Thanks. $\endgroup$
    – london
    Mar 31 '19 at 12:00

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