LAG1

AcronymDefinition
LAG1Longevity Assurance Gene 1
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Short-term [PM.sub.2.5] concentration NE EPI lag0 -0.2 (-3.3, 3) 1.1 (-3.7,6) lag1 -2.1 (-5.4, 1.1) -2.2 (-7.2, 2.5) lag1-2 -1.5 (-4.8, 1.8) -2.5 (-7.5, 2.3) lag1-3 -1.9 (-5.2, 1.4) -2.9 (-7.9, 2) lag1-4 -2.5 (-5.9, 0.7) -3.8 (-8.9, 1.1) lag1-5 -2.1 (-5.4, 1.2) -3.0 (-8.1, 1.9) Short-term [PM.sub.2.5] concentration DA lag0 1.1 (-1.8, 4.1) lag1 -2.2 (-5.2, 0.8) lag1-2 -2.5 (-5.6, 0.4) lag1-3 -2.2 (-5.2, 0.8) lag1-4 -2.7 (-5.8, 0.3) lag1-5 -2.1 (-5.1, 0.9) Note: Standard deviation (SD) increases are as follows: day of urine collection 8.3, day prior 7.9, 2-day average 7.1, 3-day average 6.5, 4-day average 6.0, and 5-day average 5.6 [micro]g/[m.sup.3].
In patients with ACS, a significant association between inpatient hospitalization and the following variables, namely, PM10 lag1 and PM10lag3 (p = 0.001), and the rise of temperature and dust storm (p= 0.001) was demonstrated.
Most important variables in the model were the regressive EET in 1, 2, 3, 4, 7, 16, 24, 40 of the previous year that is 2920, 2921, and so on, explaining a strong contagion between data, namely a great correlation between the data Lag1 and so on, with the data in the previous year.
In case of stock price dependent, stock price lag1, lag6, lag7, and buyback lag5 are affecting the present stock prices.
The negative value of GARCH (-1) shows that volatility in lag1 would affect volatility for current lag in such a way that if volatility is less in lag1 it gets increased for current lag.
Yr/Yr %Chg 0.002 0.327 Germany: Real retail sales Yr/Yr 0.001 0.163 %Chg Germany: Current account: Trade 0.020 1.909 balance Germany: Real GDP Qtr/Qtr %Chg 0.042 3.493 * Cos_q1 -0.011 -22.020 ([dagger]) Cos_q2 0.004 8.137 * Cos_q3 -0.009 -20.889 ([dagger]) Cos_q4 -0.007 -15.360 ([dagger]) Sin_q1 0.016 35.591 * Sin_q2 -0.001 -3.291 ([dagger]) Sin_q3 0.003 6.110 * Sin_q4 -0.007 -14.930 ([dagger]) Absolute return lag1 0.095 21.428 * Absolute return lag2 0.043 9.632 * Absolute return lag3 0.024 5.418 * Absolute return lag4 0.031 6.923 * Absolute return lag5 0.022 5.019 * Constant -0.009 -8.002 ([dagger]) GARCH daily volatility 3.010 47.135 * Friday_7 p.m.
TABLE 1 Hotel Rooms Regression Results--92 Indiana Counties, 1994-2004 Rooms Casino open -148.0 * (3.76) LAG1 -110.3 * (2.80) LAG2 49.0 (1.24) LAG3 179.6 * (4.56) LAG4 241.4 * (6.14) LAG5 187.0 * (4.43) Population -37.3 * (7.23) Pop squared 0.0612 * (22.01) Density 9.78 * (5.00) Real income 42.1 * (5.42) Male -71.4 * (3.40) White 51.0 * (2.01) Black 189.1 * (7.52) Pop 10-19 -53.9 * (2.97) Pop 20-29 -45.0 * (2.88) Pop 30-39 -92.3 * (3.85) Pop 40-49 -28.0 (1.60) Pop 50-64 -89.5 * (5.19) Pop over 64 -58.4 * (3.12) Constant 3176.3 (0.95) [R.sup.2] .7356 Num obs 1012 Note: t-values are given in parentheses.
Results are displayed in Table 6 (where Lag1 and Lag2 refer to the first two lags of the dependent variable) and all coefficients are in line with the previous estimates.
There is no evidence of serial correlation in the residuals (SW lag1 autocorrelation = 0.06, P = 0.64; NE lag 1 autocorrelation = 0.07, P = 0.59).
Associations with ambient [PM.sub.2.5] were less consistent; the effect estimates for lag0 and lag1 were negative, while the effect estimates for lag2, lag3, and ambient averages for the full sampling period were positive.
The changes in exposure to air pollutants were estimated for the same day (lag0) as well as on the lags of days 1-6 (lag1, lag2, ...