The finding of cointegration implies that the standard UVAR specification is incorrectly specified, thereby casting doubt on the earlier causality test findings.
t] should simply be converted to stationary series by first-differencing before entering the UVAR.
6) Throughout the empirical literature, most of the early work has applied the UVAR models using the relevant budgetary variables.
After controlling for these variables, we expect the coefficient of UVAR to be positive and that of CVAR to be negative, since as we discussed before, greater volatility would increase the demand for temporary workers, while greater correlation of output fluctuations among industries may shift down the supply curve of temporary workers and lower the use of temporary workers.
7) In column 1 of table 4, we consider the effects of both UVAR and CVAR on the shares of temporary employment across the states.
The positive coefficient for UVAR suggests that there may be greater demand for temporary labor in states with a mix of volatile industries.
However, the negative coefficient for CVAR indicates that, for a given level of UVAR, THS employment shares are lower if output fluctuations tend to coincide across industries.
In our sample, as shown in the regression in column 3 of table 4, on average, the positive effects of UVAR and the negative effects of CVAR seem to offset each other; the effects of overall volatility (VAR) on temporary service employment appear to be insignificant at the 10 percent level.
16) Note: DW denotes the cointegrating regression Durbin-Watson statistic; DF/ADF denotes the Dickey-Fuller statistic (zero lags) or the augmented Dickey-Fuller statistic (positive lags); RVAR/ ARVAR denotes the restricted VAR statistic (zero lags) or the augmented restricted VAR statistic (positive lags); UVAR
denotes the unrestricted VAR statistic (zero lags) or the augmented unrestricted VAR statistic (positive lags).