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E ¢ cient Inference on Fractionally Integrated Panel Data Models with Fixed E ¤ ects
| Content Provider | Semantic Scholar |
|---|---|
| Author | Robinson, Peter M. |
| Copyright Year | 2013 |
| Abstract | A dynamic panel data model is considered that contains possibly stochastic individual components and a common fractional stochastic time trend. We propose four di¤erent ways of coping with the individual e¤ects so as to estimate the fractional parameter. Like models with autoregressive dynamics, ours nests a unit root, but unlike the nonstandard asymptotics in the autoregressive case, estimates of the fractional parameter can be asymptotically normal. Establishing this property is made di¢ cult due to bias caused by the individual e¤ects, or by the consequences of eliminating them, and requires the number of time series observations T to increase, while the cross-sectional size, N; can either remain xed or increase with T: The biases in the central limit theorem are asymptotically negligible only under stringent conditions on the growth of N relative to T; but these can be relaxed by bias correction. For three of the estimates the biases depend only on the fractional parameter. In hypothesis testing, bias correction of the estimates is readily carried out. We evaluate the biases numerically for a range of T and parameter values, develop and justify feasible bias-corrected estimates, and briey discuss simpli ed but less e¤ective corrections. A Monte Carlo study of nite-sample performance is included. JEL Classi cations: C12, C13, C23 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://economics.ku.edu/sites/economics.drupal.ku.edu/files/files/dec4.pdf |
| Alternate Webpage(s) | http://econ.as.nyu.edu/docs/IO/30574/Robinson.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
