Sentence examples for incidental parameter from inspiring English sources

We show that this recursive-design residual-based bootstrap fixed effects OLS estimator contains a built-in bias correction term that mimics the incidental parameter bias.

First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling because differencing eliminates fixed effects and, in the case of a unit root, differencing transforms the data to stationarity, thereby addressing both incidental parameter problems and the possible effects of nonstationarity.

Whereas the focus of the existing literature has been on bias correcting the standard fixed effects OLS estimator (due to the well known incidental parameter bias), our focus here is on improving the quality of inference by relying on the bootstrap instead of the standard normal distribution when computing critical values for test statistics.

However, there are no available fixed-effects Tobit estimators due to an incidental parameter problem in maximum likelihood methods [37].

The transformation method removes the potential issues caused by the incidental parameter problem (Wong, Ho and Singh 2007).

However, this approach suffers from the so-called incidental parameter problem as expressed in Neyman and Scott (1948).

In assessing the performance of universities, the most recent literature underlined that the efficiency scores may suffer from the presence of incidental parameters or time-invariant, often unobservable, effects that lead to biased efficiency estimates.

In the first case (Marginal Maximum Likelihood estimation, MML, cf. Baker and Kim 2004, ch. 6), the incidental parameters θ v are replaced by assuming a proper distribution G in the population (e.g. the normal), requiring only the hyperparameters τ of G to be determined (i.e. the mean and the variance of G in our example).

We use linear probability models rather than, say, probits, because our most saturated specifications use large numbers of fixed effects (one for each job ad), raising computational issues as well as concerns with consistency in the presence of a large number of incidental parameters.

Here, we provide a more detailed statistical argument describing the framework's extreme sensitivity to incidental parameters.

Greene (2005a) argued that when a fixed-effect model is used for data in a relatively short period, and a period of three years is short in this context, the estimated parameters may be biased and there may be an "incidental parameters" problem [ 6].