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- W2138413723 abstract "In my original paper (Nickell, I982), r analyse a theoretical model concerned with flows into and out of unemployment. Consequent on this analysis, I estimate equations which explain the quarterly inflow and outflow rates (i.e. inflow/employment and outflow/unemployment). In the first section of his comments (Price, I985), Price rightly points out that such rates cannot be thought of as quarterly probabilities because of the length of the time interval involved. On the other hand, if it is thought to be helpful, the probability interpretation could be sustained to a high degree of approximation if one thought of the quarterly rates as the average daily probability times the number of days in a quarter. However, since this issue is of no significance vis-a-vis the original paper, I shall turn to the second section of Price's comment which deals with my results. His first point is concerned with the degree of serial correlation in the residuals. In my paper, I report Durbin-Watson and Box-Pierce tests, the former being a small sample test, the latter an asymptotic test. Neither test rejects the null in either equation. Price reports that large sample versions of the LM test reject the null in both equations with the outflow rate equation coming off worst. The large sample LM test for nth order autocorrelation is based on the R2 from a regression of the fitted residuals on n lags of itself plus all the regressors. In the outflow equation there are I5 regressors and 44 observations. In order to include n lags on the residuals, n observations must be dropped and the regression used to generate the LM test then includes I5 + n variables on the right and uses 44n observations. The number of degrees of freedom remaining is thus 292n. So even with the test for first order serial correlation we have only 27 degrees of freedom remaining and when we arrive at twelfth order serial correlation we are down to 5 degrees of freedom! This might explain why the first order LM test is somewhat at odds with the DurbinWatson test which is, after all, a perfectly reasonable small sample test of serial correlation in this particular equation. So I do not find this to be convincing evidence that the model originally reported is misspecified. However the fact that Price finds evidence of parameter instability across the middle of the sample is a more serious matter. This is always a potential problem with short samples and lots of regressors since the equations are more or less bound to be overfitted. I am guilty as charged on this count. The next section of Price's comment is concerned, for the most part, with the outflow rate equation. In essence he objects to my running a regression to explain the (log) outflow rate (log(Ot/Ut), 0 = outflow, U = unemployment) in spite of the fact that it is a natural consequence of the theory. To back up his [ I96 ]" @default.
- W2138413723 created "2016-06-24" @default.
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- W2138413723 date "1985-03-01" @default.
- W2138413723 modified "2023-09-30" @default.
- W2138413723 title "The Determinants of Equilibrium Unemployment in Britain: A Reply" @default.
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- W2138413723 doi "https://doi.org/10.2307/2233480" @default.
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