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| Registros recuperados: 10 | |
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| Poi, Brian P.. |
| The two-stage least-squares (2SLS) instrumental variables estimator is commonly used to address endogeneity. However, the estimator suffers from bias that is exacerbated when the instruments are only weakly correlated with the endogenous variables and when many instruments are used. In this article, I discuss jackknife instrumental variables estimation as an alternative to 2SLS. Monte Carlo simulations comparing the jackknife instrument variables estimators to 2SLS and limited information maximum likelihood (LIML) show that two of the four variants perform remarkably well even when 2SLS does not. In a weak-instrument experiment, the two best performing jackknife estimators also outperform LIML. |
| Tipo: Journal Article |
Palavras-chave: Jive; 2SLS; LIML; JIVE; Instrumental variables; Endogeneity; Weak instruments; Research Methods/ Statistical Methods. |
| Ano: 2006 |
URL: http://purl.umn.edu/117586 |
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| Petrin, Amil; Poi, Brian P.; Levinsohn, James. |
| A key issue in the estimation of production functions is the correlation between unobservable productivity shocks and input levels. Profit-maximizing firms respond to positive productivity shocks by expanding output, which requires additional inputs. Negative shocks lead firms to pare back output, decreasing their input usage. Olley and Pakes (1996) develop an estimator that uses investment as a proxy for these unobservable shocks. More recently, Levinsohn and Petrin (2003a) introduce an estimator that uses intermediate inputs as proxies, arguing that intermediates may respond more smoothly to productivity shocks. This paper reviews Levinsohn and Petrin’s approach and introduces a Stata command that implements it. |
| Tipo: Journal Article |
Palavras-chave: Levpet; Production functions; Productivity; Endogeneity; GMM estimator; Research Methods/ Statistical Methods. |
| Ano: 2004 |
URL: http://purl.umn.edu/116231 |
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| Yasar, Mahmut; Raciborski, Rafal; Poi, Brian P.. |
| Productivity is often computed by approximating the weighted sum of the inputs from the estimation of the Cobb–Douglas production function. Such estimates, however, may suffer from simultaneity and selection biases. Olley and Pakes (1996, Econometrica 64: 1263–1297) introduced a semiparametric method that allows us to estimate the production function parameters consistently and thus obtain reliable productivity measures by controlling for such biases. This study first reviews this method and then introduces a Stata command to implement it. We show that when simultaneity and selection biases are not controlled for, the coefficients for the variable inputs are biased upward and the coefficients for the fixed inputs are biased downward. |
| Tipo: Article |
Palavras-chave: Opreg; Levpet; Production function; Bias; Simultaneity; Research Methods/ Statistical Methods. |
| Ano: 2008 |
URL: http://purl.umn.edu/122587 |
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| Registros recuperados: 10 | |
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