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Poster Communications<br />
SP2<br />
Thursday, September 5th<br />
19:45<br />
Functional coefficient regression models for the<br />
problem of Okun’s Law<br />
Michel Helcias Montoril<br />
Sidney M. Caetano<br />
UFJF<br />
In 1962, Arthur Okun posited an empirical relationship between the change in the unemployment<br />
rate and growth rate. He explained the difference in the unemployment rate (Y ) by the growth<br />
rate of the real gross national product (X). Such a relationship became known as Okun’s law.<br />
Using quarter-to-quarter data from 1947 to 1960, Okun fitted a linear regression and concluded<br />
that Ŷ = 0.3 − 0.3X. Over time, Okun’s law has been studied under different models and different<br />
time samples. Among the approaches, it has been considered dynamic models assuming that the<br />
regression coefficient varies over time. In this work, instead of evaluating the regression coefficient<br />
as a function of time, we consider a wavelet-based functional regression model to study the Okun’s<br />
law, where (in our case) the regression coefficient changes according to lagged growth rates of the<br />
GNP. In this case, one can interpret that variations of the GNP in the past will be able to explain<br />
changes in Okun’s law in the present.<br />
Keywords: Functional coefficient regression models; Okun’s law; Wavelets<br />
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