The term "lag value" is correct and usable in written English.
It is often used in mathematical or statistical contexts when referring to a predetermined unit of time. For example, "We used a lag value of six months to compare the two sets of data."
The suitable lag value for each model is selected on the basis of Akaike information criterion.
Such a reference point can be the zero lag value which we calculate exactly.
Furthermore, to estimate the cointegration equation, a suitable lag value is required to obtain robust results and this is mostly obtained from vector autoregressive (VAR) statistics.
The correlation coefficient between GEC27R and EUV27R changes when different lag values are used; it peaks when the lag value of 2 days is used.
The time lag corresponds to the lag value of GEC to EUV for which the correlation coefficient between GEC and EUV reaches a maximum.
This can be achieved by using a multitap PLP model [47] which features multiple nonzero filter coefficients centered around the pitch lag value.
Values of variables occurring prior to the current observation are named lag values.
Results showed that U-values were higher when reinforcement was contingent on increased variability, indicating the effectiveness of higher lag values on response variability.
Connectivity combinations with statistically significant (p < 0.01) lag values were assessed using a two sample t-test of the difference between averaged HV and MO patient lags.
Therefore, we obtained two lag values for the two sub-periods from the VAR statistics based on the minimum value of the Schwarz criterion.
In this study, correlation between the error term and regressors associated with the introduction of lag values of the dependent variable affects its current value.
