谁能告诉我一些关于函数VAR()和函数之间的区别dynlm()？我想我也可以用 OLS 做我的 VAR lm()，但发现我不能包括滞后。这个问题通过使用来解决dynlm()。我尝试使用相同的数据VAR()并dynlm()更改了 VAR 函数的类型（“const”/“trend”/“none”/“both”），但在任何一种情况下，结果都不相同。有人知道区别吗？
Data Example (only 100 rows of my time series with over 1000 rows):
Price Open_Interest_All
1992-10-06 -0.781222672 1.63119379
1992-10-13 -0.226928010 4.05849973
1992-10-20 -0.314059754 0.74296670
1992-10-27 0.427535236 4.69469707
1992-11-03 -0.066975848 -8.97727509
1992-11-10 -1.105733849 13.60848986
1992-11-17 -0.033504156 -0.86606938
1992-11-24 0.132090521 3.09942608
1992-12-01 0.451677760 -6.96875467
1992-12-08 0.059010297 1.45547915
1992-12-15 0.018143072 2.47922886
1992-12-22 0.415489498 0.11104701
1992-12-29 0.281748150 -0.01741099
1993-01-05 1.264093684 0.75242942
1993-01-12 -1.765301997 -0.16864140
1993-01-19 0.352909608 1.23869888
1993-01-26 0.152561942 5.16469475
1993-02-02 -0.306023610 -1.88288581
1993-02-09 0.371154802 -0.22569400
1993-02-16 -0.096699431 0.72290467
1993-02-23 -0.216939288 -1.03640733
1993-03-02 -0.692525272 7.32922042
1993-03-09 0.148512407 -0.83248916
1993-03-16 0.053258877 -9.34412387
1993-03-23 0.282899028 -2.27865356
1993-03-30 -0.348375291 -0.68304626
1993-04-06 -0.701583175 5.13726283
1993-04-13 -0.643717452 7.49539668
1993-04-20 -2.315215806 6.78579388
1993-04-27 0.728123084 -7.89513692
1993-05-04 -0.401917826 1.93334817
1993-05-11 -0.820260275 -0.41889123
1993-05-18 -0.136744342 0.17780875
1993-05-25 0.851391229 5.73901054
1993-06-01 -1.030441814 2.02329734
1993-06-08 0.622336509 -0.47102737
1993-06-15 0.188830476 1.12995553
1993-06-22 0.425932387 3.25510238
1993-06-29 0.094860323 -1.15090784
1993-07-06 0.690949433 -3.84819577
1993-07-13 -0.555562379 -0.79792963
1993-07-20 -0.003165141 0.26002615
1993-07-27 0.303670755 -2.04747741
1993-08-03 0.173833160 0.14447390
1993-08-10 -0.361775029 4.78484100
1993-08-17 0.384427420 -3.66029272
1993-08-24 0.427606006 0.68034298
1993-08-31 -0.138889246 -7.61964095
1993-09-07 0.336480002 -2.54707323
1993-09-14 -1.121502023 5.46051062
1993-09-21 -1.763129531 13.11181789
1993-09-28 0.039080472 0.86031698
1993-10-05 -0.966905912 -4.20461016
1993-10-12 0.218149112 -2.12196119
1993-10-19 -0.817582360 -1.80785218
1993-10-26 -0.077143308 4.02653854
1993-11-02 0.079626855 0.64359810
1993-11-09 0.451802406 6.08728086
1993-11-16 -0.305816356 4.74542449
1993-11-23 0.217000320 4.48037134
1993-11-30 -0.188669087 -1.17994159
1993-12-07 0.747784520 5.22816662
1993-12-14 0.636011468 -2.22521990
1993-12-21 0.373247064 -0.52307968
1993-12-28 0.046941753 -0.95758768
1994-01-04 -0.810928287 -1.05574558
1994-01-11 0.490505014 -2.23947004
1994-01-18 0.699126287 1.47016108
1994-01-25 0.420702924 -5.33040334
1994-02-01 -0.034364270 1.03293264
1994-02-08 0.178692037 7.82838866
1994-02-15 -0.763248370 -4.77697912
1994-02-22 0.834492829 -4.39362728
1994-03-01 -0.319921226 -5.77740405
1994-03-08 0.428953703 6.08511360
1994-03-15 0.293429418 3.33746465
1994-03-22 0.212134960 8.28017661
1994-03-29 0.015434084 -3.84063215
1994-04-05 -0.819101724 -12.37463070
1994-04-12 -0.241797314 -6.35085378
1994-04-19 -0.110199011 -0.52875672
1994-04-26 0.292877781 8.25096506
1994-05-03 0.769091229 8.93992009
1994-05-10 1.335381796 -3.14410505
1994-05-17 1.218414811 -6.21162283
1994-05-24 0.915342249 -1.06778949
1994-05-31 -0.899495962 3.65817336
1994-06-07 -0.033983195 -4.82433827
1994-06-14 1.561915814 4.23026763
1994-06-21 -0.066335053 -1.33347133
1994-06-28 -0.385756193 -5.93684313
1994-07-05 0.991119380 -5.67886504
1994-07-12 -0.398792460 -8.50476149
1994-07-19 0.232917128 6.45658144
1994-07-26 0.382266943 -9.35866297
1994-08-02 -1.190583357 -0.50190110
1994-08-09 0.364675303 -5.42680501
1994-08-16 -0.070737617 1.57683454
1994-08-23 -0.167435051 -3.93086516
1994-08-30 0.928324476 5.51263721
VARexample <- VAR(example, p=5, type="const")
lmexample <- lm(example$Price[6:100]~example$Price[5:99]+example$Price[4:98]+example$Price[3:97]+example$Price[2:96]+example$Price[1:95]+example$Open_Interest_All[5:99]+example$Open_Interest_All[4:98]+example$Open_Interest_All[3:97] + example$Open_Interest_All[2:96] + example$Open_Interest_All[1:95])
dynlmexample <- dynlm(example$Price~L(example$Price,1:5) + L(example$Open_Interest_All,1:5))
VARcoeffsexample <- as.data.frame(coef(VARexample)$Price[c(11,1,3,5,7,9,2,4,6,8,10),1])
lmcoeffsexample <- as.data.frame(coef(lmexample))
dynlmcoeffsexample <- as.data.frame(coef(dynlmexample))
compare <- data.frame(VARcoeffsexample[,1], lmcoeffsexample[,1], dynlmcoeffsexample[,1])
rownames(compare) <- rownames(VARcoeffsexample)
Output:
> compare
VARcoeffsexample...1. lmcoeffsexample...1. dynlmcoeffsexample...1.
const 0.0162815782 0.0162815782 0.016408246
Price.l1 -0.0233716524 -0.0233716524 -0.062759716
Price.l2 0.1262282115 0.1262282115 0.050038501
Price.l3 0.1444267851 0.1444267851 0.103449318
Price.l4 -0.0806846153 -0.0806846153 0.005414185
Price.l5 -0.0197842734 -0.0197842734 -0.048802520
Open_Interest_All.l1 -0.0176241595 -0.0176241595 -0.022186654
Open_Interest_All.l2 0.0166671079 0.0166671079 -0.008759684
Open_Interest_All.l3 0.0205802095 0.0205802095 -0.007958906
Open_Interest_All.l4 -0.0384040280 -0.0384040280 -0.003796186
Open_Interest_All.l5 0.0006342804 0.0006342804 -0.029456337
所以 lm() 和 VAR() 是相同的系数，但 dynlm() 是不同的......