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I am trying to use a dynamic linear regression using dynlm command in R programming since I need to analyze my panel data but I do not want to use panel regression.
However, my model specification do not contain any lagged variables at all. Can I still use the dynamic linear model (dynlm) in this case? The outputs it give are still quite good and helpful.
For instance, I get the following.
Call:
dynlm(formula = y ~ a + b + c + d*g + e*g +
f*g + h + i + j)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.12175142 1.87591046 1.131 0.258860
a 0.00019267 0.02859444 0.007 0.994628
b -0.01091167 0.02133546 -0.511 0.609392
c 0.17635258 0.05616125 3.140 0.001842 **
d -0.12717373 0.04706829 -2.702 0.007253 **
g -0.39693637 0.09144441 -4.341 1.894e-05 ***
e -0.15394576 0.05059879 -3.042 0.002536 **
f -0.22525696 0.07412517 -3.039 0.002565 **
h -0.10063528 0.01242704 -8.098 1.108e-14 ***
i 0.00098993 0.00240669 0.411 0.681102
j -0.11337655 0.30151860 -0.376 0.707146
d:g 0.06875835 0.09451161 0.728 0.467431
g:e 0.09787315 0.11409165 0.858 0.391603
g:f 0.19367624 0.14990202 1.292 0.197260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1648 on 331 degrees of freedom
Multiple R-squared: 0.5586 , Adjusted R-squared: 0.5452
F-statistic: 31.9 on 10 and 331 DF, p-value: < 1.401e-50
Durbin-Watson statistic
(original): 1.34863 , p-value: 1.861e-10
(transformed): 2.09349 , p-value: 8.068e-01>
The first part of the data set I use is the following:
Date ID a b c d e f g h i j y
01/01/2017 1 1 0 0 1 0 0 1 6.5 -0.287199892 6.26048245 0.380978369
01/01/2017 2 0 0 0 1 0 0 1 6.5 -0.287199892 6.26048245 0.380978369
01/01/2017 3 1 0 0 0 1 0 0 7.8 -0.287199892 6.26048245 0.524437496
01/03/2017 4 1 0 0 0 0 0 0 7.8 -0.260937218 6.258402008 0.63409868
01/04/2017 5 0 0 0 1 0 0 1 6.5 10.51545939 6.263858877 0.392317155
01/04/2017 6 0 0 0 1 0 0 1 6.5 10.51545939 6.263858877 0.392317155
01/04/2017 7 0 1 0 1 0 0 0 6.5 10.51545939 6.263858877 1.049993284
01/04/2017 8 0 0 0 0 1 0 0 7.3 10.51545939 6.263858877 0.461989851
01/05/2017 9 0 0 0 0 1 0 0 6.1 -16.12973095 6.280696169 0.69686996
01/05/2017 10 0 0 0 1 0 0 0 7.7 -16.12973095 6.280696169 0.639270495
01/05/2017 11 0 0 0 0 1 0 0 7.3 -16.12973095 6.280696169 0.369339223
01/06/2017 12 1 0 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.395179169
01/06/2017 13 0 1 0 1 0 0 0 6.3 -7.097505117 6.281526986 0.634524509
01/06/2017 14 0 1 0 1 0 0 0 7.8 -7.097505117 6.281526986 0.605731699
01/06/2017 15 0 0 0 0 0 0 0 3.2 -7.097505117 6.281526986 1.765103139
01/07/2017 16 0 1 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.323052418
01/07/2017 17 0 0 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.323052418
01/08/2017 18 0 0 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.357581409
01/09/2017 19 0 0 0 1 0 0 1 6.5 -0.376295821 6.278540118 0.375177221
01/09/2017 20 0 0 0 1 0 0 1 6.5 -0.376295821 6.278540118 0.375177221
01/10/2017 21 0 0 0 1 0 0 1 6.5 1.07381926 6.275634353 0.323677822
01/10/2017 22 1 0 0 0 0 0 0 6.3 1.07381926 6.275634353 0.529304377
01/11/2017 23 0 0 0 1 0 0 1 6.5 -15.99695552 6.292042205 0.272404556
01/11/2017 24 0 0 0 1 0 0 1 6.5 -15.99695552 6.292042205 0.272404556
01/11/2017 25 0 0 0 1 0 0 0 5.8 -15.99695552 6.292042205 0.485387413
01/11/2017 26 0 0 0 0 1 0 0 6.3 -15.99695552 6.292042205 0.651151817
01/12/2017 27 0 1 0 1 0 0 1 6.5 4.672168917 6.290699191 0.259498815
01/12/2017 28 0 1 0 1 0 0 0 7.3 4.672168917 6.290699191 0.396883681
01/13/2017 29 0 0 0 1 0 0 1 6.5 2.818656098 6.288309121 0.247276795
01/13/2017 30 0 0 0 1 0 0 0 6.1 2.818656098 6.288309121 0.72878018
01/13/2017 31 1 0 0 0 0 0 0 6.3 2.818656098 6.288309121 0.439525331
01/13/2017 32 1 0 0 0 0 0 0 6.3 2.818656098 6.288309121 0.439525331
01/13/2017 33 0 0 0 1 0 0 0 7.8 2.818656098 6.288309121 0.674418975
01/14/2017 34 0 0 0 1 0 0 1 6.5 2.818656098 6.288309121 0.228731465
01/14/2017 35 0 0 0 1 0 0 1 6.5 2.818656098 6.288309121 0.228731465
01/14/2017 36 1 0 0 0 0 0 0 3.2 2.818656098 6.288309121 1.614602435
01/15/2017 37 0 1 0 1 0 0 1 6.5 2.818656098 6.288309121 0.247426893
01/15/2017 38 0 0 1 1 0 0 0 7.3 2.818656098 6.288309121 0.557578826
01/16/2017 39 0 0 0 1 0 0 1 6.5 0.393759618 6.285334223 0.27193064
01/16/2017 40 0 0 0 0 1 0 0 7.3 0.393759618 6.285334223 0.85500972
01/16/2017 41 0 1 0 1 0 0 0 5.7 0.393759618 6.285334223 0.676041272
01/17/2017 42 0 1 0 1 0 0 1 6.5 7.88986693 6.287050627 0.271868581
01/17/2017 43 0 1 0 1 0 0 0 5.7 7.88986693 6.287050627 0.601684565
01/17/2017 44 0 0 0 1 0 0 0 7.3 7.88986693 6.287050627 0.561442547
01/17/2017 45 0 0 0 1 0 0 0 5.7 7.88986693 6.287050627 0.426279123
01/18/2017 46 0 0 0 1 0 0 1 6.5 -2.816167339 6.284668375 0.247257528
01/18/2017 47 0 1 0 1 0 0 1 6.5 -2.816167339 6.284668375 0.247257528
01/18/2017 48 0 1 0 1 0 0 1 6.5 -2.816167339 6.284668375 0.247257528
01/20/2017 49 0 0 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/20/2017 50 0 0 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/20/2017 51 0 1 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/20/2017 52 0 0 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/21/2017 53 0 1 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.255335931
01/21/2017 54 0 0 0 1 0 0 0 7.3 -0.673663504 6.279474132 0.511621616
01/21/2017 55 0 1 0 1 0 0 0 7.8 -0.673663504 6.279474132 0.658433144
01/22/2017 56 0 1 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.295583923
01/23/2017 57 0 0 0 1 0 0 0 6.5 2.407225453 6.276942114 1.536492494
01/23/2017 58 0 0 0 0 0 1 0 5.2 2.407225453 6.276942114 0.484052447
01/23/2017 59 0 0 1 1 0 0 0 7.8 2.407225453 6.276942114 0.781409215
01/24/2017 60 0 1 0 1 0 0 1 6.5 -1.218354632 6.274089502 0.321647225
01/24/2017 61 0 0 0 1 0 0 1 6.5 -1.218354632 6.274089502 0.321647225
01/25/2017 62 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 63 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 64 0 1 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 65 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 66 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 67 0 1 0 1 0 0 0 4.3 -1.851624795 6.271387207 0.613050528
01/25/2017 68 0 1 0 1 0 0 0 7.3 -1.851624795 6.271387207 0.546333861
01/25/2017 69 0 0 0 0 1 0 0 7.2 -1.851624795 6.271387207 0.537850062
01/25/2017 70 0 1 0 0 1 0 0 7.8 -1.851624795 6.271387207 0.639599631
01/26/2017 71 0 0 0 1 0 0 1 6.5 2.534464661 6.26891399 0.300565872
01/26/2017 72 0 0 0 1 0 0 1 6.5 2.534464661 6.26891399 0.300565872
01/26/2017 73 0 1 0 1 0 0 0 8.3 2.534464661 6.26891399 0.529403228
01/27/2017 74 0 0 0 1 0 0 1 6.5 0.448048274 6.265976913 0.300919416
01/27/2017 75 0 1 0 1 0 0 1 6.5 0.448048274 6.265976913 0.300919416
01/27/2017 76 1 0 0 1 0 0 0 5.7 0.448048274 6.265976913 0.580776132
01/27/2017 77 0 0 0 0 1 0 0 6.1 0.448048274 6.265976913 0.686217699
02/01/2017 78 0 0 1 1 0 0 0 6.5 1.735858826 6.258495118 1.134076442
02/02/2017 79 1 0 0 1 0 0 1 6.5 2.678162264 6.256096701 0.246605523
02/02/2017 80 0 1 0 0 1 0 0 5.7 2.678162264 6.256096701 0.788278101
02/03/2017 81 0 0 0 1 0 0 1 6.5 1.153969121 6.253264176 0.240588472
02/03/2017 82 1 0 0 0 1 0 0 7.2 1.153969121 6.253264176 0.504511317
02/03/2017 83 1 0 0 1 0 0 0 7.8 1.153969121 6.253264176 0.367633571
02/04/2017 84 0 0 0 1 0 0 1 6.5 1.153969121 6.253264176 0.265418597
02/04/2017 85 0 1 0 1 0 0 0 6.1 1.153969121 6.253264176 0.657809188
02/04/2017 86 0 1 0 1 0 0 0 7.8 1.153969121 6.253264176 0.363212341
02/05/2017 87 0 0 0 1 0 0 0 7.3 1.153969121 6.253264176 0.508102373
02/06/2017 88 0 0 0 0 0 0 1 6.5 1.046748087 6.250418001 0.372827574
02/06/2017 89 0 0 0 1 0 0 1 6.5 1.046748087 6.250418001 0.372827574
02/06/2017 90 0 1 0 1 0 0 1 6.5 1.046748087 6.250418001 0.372827574
02/06/2017 91 0 0 0 1 0 0 0 6.1 1.046748087 6.250418001 0.755692532
02/07/2017 92 1 0 0 0 1 0 0 6.1 2.572012207 6.247989068 0.669478955
02/07/2017 93 1 0 0 0 0 0 0 6.3 2.572012207 6.247989068 0.533384869
02/07/2017 94 0 0 0 1 0 0 0 5.7 2.572012207 6.247989068 0.551575392
02/07/2017 95 0 1 0 1 0 0 0 7.7 2.572012207 6.247989068 0.544694026
02/07/2017 96 1 0 0 0 0 0 0 7.3 2.572012207 6.247989068 0.466169871
02/07/2017 97 0 0 0 0 0 0 0 7.8 2.572012207 6.247989068 0.352763774
02/08/2017 98 0 1 0 1 0 0 1 6.5 0.93372207 6.245134 0.317670958
02/08/2017 99 0 1 0 1 0 0 1 6.5 0.93372207 6.245134 0.317670958
02/08/2017 100 0 0 0 1 0 0 0 6.1 0.93372207 6.245134 0.590983019
02/08/2017 101 0 1 0 1 0 0 0 7.8 0.93372207 6.245134 0.288007254
02/08/2017 102 0 0 0 1 0 0 0 7.8 0.93372207 6.245134 0.288007254
02/09/2017 103 0 1 0 1 0 0 1 6.5 -7.364293184 6.246272494 0.322058201
02/09/2017 104 0 1 0 1 0 0 0 7.7 -7.364293184 6.246272494 0.45710099
02/09/2017 105 0 1 0 1 0 0 0 7.3 -7.364293184 6.246272494 0.618448667
02/09/2017 106 1 0 0 0 1 0 0 6.7 -7.364293184 6.246272494 0.75533239
02/10/2017 107 1 0 0 1 0 0 1 6.5 2.054261996 6.243673693 0.322091275
02/10/2017 108 0 1 0 0 1 0 0 7.3 2.054261996 6.243673693 0.619229315
02/10/2017 109 1 0 0 0 1 0 0 6.3 2.054261996 6.243673693 0.574019377
02/11/2017 110 0 0 0 0 1 0 0 5.8 2.054261996 6.243673693 0.576474297
02/12/2017 111 1 0 0 0 0 1 0 7.8 2.054261996 6.243673693 0.292138684
02/13/2017 112 0 0 1 1 0 0 0 6.1 -0.5847667 6.2407891 0.796668232
02/14/2017 113 1 0 0 1 0 0 1 6.5 1.418813574 6.238033264 0.347653035
02/14/2017 114 0 0 0 1 0 0 1 6.5 1.418813574 6.238033264 0.347653035
02/14/2017 115 0 0 0 1 0 0 1 6.5 1.418813574 6.238033264 0.347653035
02/14/2017 116 0 0 0 0 1 0 0 6.1 1.418813574 6.238033264 0.735497343
02/14/2017 117 0 1 0 1 0 0 0 4.3 1.418813574 6.238033264 0.51068871
02/14/2017 118 0 0 0 0 0 1 0 6.5 1.418813574 6.238033264 0.458087049
02/14/2017 119 0 0 0 0 1 0 0 4.5 1.418813574 6.238033264 0.402129607
02/15/2017 120 0 1 0 0 1 0 1 6.5 -0.041897715 6.235131309 0.308459288
.............
The data amount is too huge even for indentations so I stop here.
481
When the current value of your dependant variable depends on the past value(s), you add it as an explanatory variable, e.g. how much dividend was given in the previous year affects the dividend decision of the current year. That's why we add its lagged values in the model.
dynlm: Dynamic Linear Regression Dynamic linear models and time series regression. Version: 0.3-6. Depends: R (≥ 2.10.0), zoo.
A dependent variable that is lagged in time. For example, if Yt is the dependent variable, then Yt-1 will be a lagged dependent variable with a lag of one period. Lagged values are used in Dynamic Regression modeling.
With your data sample:
datastr <- "
Date ID a b c d e f g h i j y
01/01/2017 1 1 0 0 1 0 0 1 6.5 -0.287199892 6.26048245 0.380978369
01/01/2017 2 0 0 0 1 0 0 1 6.5 -0.287199892 6.26048245 0.380978369
01/01/2017 3 1 0 0 0 1 0 0 7.8 -0.287199892 6.26048245 0.524437496
01/03/2017 4 1 0 0 0 0 0 0 7.8 -0.260937218 6.258402008 0.63409868
01/04/2017 5 0 0 0 1 0 0 1 6.5 10.51545939 6.263858877 0.392317155
01/04/2017 6 0 0 0 1 0 0 1 6.5 10.51545939 6.263858877 0.392317155
01/04/2017 7 0 1 0 1 0 0 0 6.5 10.51545939 6.263858877 1.049993284
01/04/2017 8 0 0 0 0 1 0 0 7.3 10.51545939 6.263858877 0.461989851
01/05/2017 9 0 0 0 0 1 0 0 6.1 -16.12973095 6.280696169 0.69686996
01/05/2017 10 0 0 0 1 0 0 0 7.7 -16.12973095 6.280696169 0.639270495
01/05/2017 11 0 0 0 0 1 0 0 7.3 -16.12973095 6.280696169 0.369339223
01/06/2017 12 1 0 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.395179169
01/06/2017 13 0 1 0 1 0 0 0 6.3 -7.097505117 6.281526986 0.634524509
01/06/2017 14 0 1 0 1 0 0 0 7.8 -7.097505117 6.281526986 0.605731699
01/06/2017 15 0 0 0 0 0 0 0 3.2 -7.097505117 6.281526986 1.765103139
01/07/2017 16 0 1 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.323052418
01/07/2017 17 0 0 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.323052418
01/08/2017 18 0 0 0 1 0 0 1 6.5 -7.097505117 6.281526986 0.357581409
01/09/2017 19 0 0 0 1 0 0 1 6.5 -0.376295821 6.278540118 0.375177221
01/09/2017 20 0 0 0 1 0 0 1 6.5 -0.376295821 6.278540118 0.375177221
01/10/2017 21 0 0 0 1 0 0 1 6.5 1.07381926 6.275634353 0.323677822
01/10/2017 22 1 0 0 0 0 0 0 6.3 1.07381926 6.275634353 0.529304377
01/11/2017 23 0 0 0 1 0 0 1 6.5 -15.99695552 6.292042205 0.272404556
01/11/2017 24 0 0 0 1 0 0 1 6.5 -15.99695552 6.292042205 0.272404556
01/11/2017 25 0 0 0 1 0 0 0 5.8 -15.99695552 6.292042205 0.485387413
01/11/2017 26 0 0 0 0 1 0 0 6.3 -15.99695552 6.292042205 0.651151817
01/12/2017 27 0 1 0 1 0 0 1 6.5 4.672168917 6.290699191 0.259498815
01/12/2017 28 0 1 0 1 0 0 0 7.3 4.672168917 6.290699191 0.396883681
01/13/2017 29 0 0 0 1 0 0 1 6.5 2.818656098 6.288309121 0.247276795
01/13/2017 30 0 0 0 1 0 0 0 6.1 2.818656098 6.288309121 0.72878018
01/13/2017 31 1 0 0 0 0 0 0 6.3 2.818656098 6.288309121 0.439525331
01/13/2017 32 1 0 0 0 0 0 0 6.3 2.818656098 6.288309121 0.439525331
01/13/2017 33 0 0 0 1 0 0 0 7.8 2.818656098 6.288309121 0.674418975
01/14/2017 34 0 0 0 1 0 0 1 6.5 2.818656098 6.288309121 0.228731465
01/14/2017 35 0 0 0 1 0 0 1 6.5 2.818656098 6.288309121 0.228731465
01/14/2017 36 1 0 0 0 0 0 0 3.2 2.818656098 6.288309121 1.614602435
01/15/2017 37 0 1 0 1 0 0 1 6.5 2.818656098 6.288309121 0.247426893
01/15/2017 38 0 0 1 1 0 0 0 7.3 2.818656098 6.288309121 0.557578826
01/16/2017 39 0 0 0 1 0 0 1 6.5 0.393759618 6.285334223 0.27193064
01/16/2017 40 0 0 0 0 1 0 0 7.3 0.393759618 6.285334223 0.85500972
01/16/2017 41 0 1 0 1 0 0 0 5.7 0.393759618 6.285334223 0.676041272
01/17/2017 42 0 1 0 1 0 0 1 6.5 7.88986693 6.287050627 0.271868581
01/17/2017 43 0 1 0 1 0 0 0 5.7 7.88986693 6.287050627 0.601684565
01/17/2017 44 0 0 0 1 0 0 0 7.3 7.88986693 6.287050627 0.561442547
01/17/2017 45 0 0 0 1 0 0 0 5.7 7.88986693 6.287050627 0.426279123
01/18/2017 46 0 0 0 1 0 0 1 6.5 -2.816167339 6.284668375 0.247257528
01/18/2017 47 0 1 0 1 0 0 1 6.5 -2.816167339 6.284668375 0.247257528
01/18/2017 48 0 1 0 1 0 0 1 6.5 -2.816167339 6.284668375 0.247257528
01/20/2017 49 0 0 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/20/2017 50 0 0 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/20/2017 51 0 1 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/20/2017 52 0 0 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.240110499
01/21/2017 53 0 1 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.255335931
01/21/2017 54 0 0 0 1 0 0 0 7.3 -0.673663504 6.279474132 0.511621616
01/21/2017 55 0 1 0 1 0 0 0 7.8 -0.673663504 6.279474132 0.658433144
01/22/2017 56 0 1 0 1 0 0 1 6.5 -0.673663504 6.279474132 0.295583923
01/23/2017 57 0 0 0 1 0 0 0 6.5 2.407225453 6.276942114 1.536492494
01/23/2017 58 0 0 0 0 0 1 0 5.2 2.407225453 6.276942114 0.484052447
01/23/2017 59 0 0 1 1 0 0 0 7.8 2.407225453 6.276942114 0.781409215
01/24/2017 60 0 1 0 1 0 0 1 6.5 -1.218354632 6.274089502 0.321647225
01/24/2017 61 0 0 0 1 0 0 1 6.5 -1.218354632 6.274089502 0.321647225
01/25/2017 62 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 63 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 64 0 1 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 65 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 66 0 0 0 1 0 0 1 6.5 -1.851624795 6.271387207 0.307303763
01/25/2017 67 0 1 0 1 0 0 0 4.3 -1.851624795 6.271387207 0.613050528
01/25/2017 68 0 1 0 1 0 0 0 7.3 -1.851624795 6.271387207 0.546333861
01/25/2017 69 0 0 0 0 1 0 0 7.2 -1.851624795 6.271387207 0.537850062
01/25/2017 70 0 1 0 0 1 0 0 7.8 -1.851624795 6.271387207 0.639599631
01/26/2017 71 0 0 0 1 0 0 1 6.5 2.534464661 6.26891399 0.300565872
01/26/2017 72 0 0 0 1 0 0 1 6.5 2.534464661 6.26891399 0.300565872
01/26/2017 73 0 1 0 1 0 0 0 8.3 2.534464661 6.26891399 0.529403228
01/27/2017 74 0 0 0 1 0 0 1 6.5 0.448048274 6.265976913 0.300919416
01/27/2017 75 0 1 0 1 0 0 1 6.5 0.448048274 6.265976913 0.300919416
01/27/2017 76 1 0 0 1 0 0 0 5.7 0.448048274 6.265976913 0.580776132
01/27/2017 77 0 0 0 0 1 0 0 6.1 0.448048274 6.265976913 0.686217699
02/01/2017 78 0 0 1 1 0 0 0 6.5 1.735858826 6.258495118 1.134076442
02/02/2017 79 1 0 0 1 0 0 1 6.5 2.678162264 6.256096701 0.246605523
02/02/2017 80 0 1 0 0 1 0 0 5.7 2.678162264 6.256096701 0.788278101
02/03/2017 81 0 0 0 1 0 0 1 6.5 1.153969121 6.253264176 0.240588472
02/03/2017 82 1 0 0 0 1 0 0 7.2 1.153969121 6.253264176 0.504511317
02/03/2017 83 1 0 0 1 0 0 0 7.8 1.153969121 6.253264176 0.367633571
02/04/2017 84 0 0 0 1 0 0 1 6.5 1.153969121 6.253264176 0.265418597
02/04/2017 85 0 1 0 1 0 0 0 6.1 1.153969121 6.253264176 0.657809188
02/04/2017 86 0 1 0 1 0 0 0 7.8 1.153969121 6.253264176 0.363212341
02/05/2017 87 0 0 0 1 0 0 0 7.3 1.153969121 6.253264176 0.508102373
02/06/2017 88 0 0 0 0 0 0 1 6.5 1.046748087 6.250418001 0.372827574
02/06/2017 89 0 0 0 1 0 0 1 6.5 1.046748087 6.250418001 0.372827574
02/06/2017 90 0 1 0 1 0 0 1 6.5 1.046748087 6.250418001 0.372827574
02/06/2017 91 0 0 0 1 0 0 0 6.1 1.046748087 6.250418001 0.755692532
02/07/2017 92 1 0 0 0 1 0 0 6.1 2.572012207 6.247989068 0.669478955
02/07/2017 93 1 0 0 0 0 0 0 6.3 2.572012207 6.247989068 0.533384869
02/07/2017 94 0 0 0 1 0 0 0 5.7 2.572012207 6.247989068 0.551575392
02/07/2017 95 0 1 0 1 0 0 0 7.7 2.572012207 6.247989068 0.544694026
02/07/2017 96 1 0 0 0 0 0 0 7.3 2.572012207 6.247989068 0.466169871
02/07/2017 97 0 0 0 0 0 0 0 7.8 2.572012207 6.247989068 0.352763774
02/08/2017 98 0 1 0 1 0 0 1 6.5 0.93372207 6.245134 0.317670958
02/08/2017 99 0 1 0 1 0 0 1 6.5 0.93372207 6.245134 0.317670958
02/08/2017 100 0 0 0 1 0 0 0 6.1 0.93372207 6.245134 0.590983019
02/08/2017 101 0 1 0 1 0 0 0 7.8 0.93372207 6.245134 0.288007254
02/08/2017 102 0 0 0 1 0 0 0 7.8 0.93372207 6.245134 0.288007254
02/09/2017 103 0 1 0 1 0 0 1 6.5 -7.364293184 6.246272494 0.322058201
02/09/2017 104 0 1 0 1 0 0 0 7.7 -7.364293184 6.246272494 0.45710099
02/09/2017 105 0 1 0 1 0 0 0 7.3 -7.364293184 6.246272494 0.618448667
02/09/2017 106 1 0 0 0 1 0 0 6.7 -7.364293184 6.246272494 0.75533239
02/10/2017 107 1 0 0 1 0 0 1 6.5 2.054261996 6.243673693 0.322091275
02/10/2017 108 0 1 0 0 1 0 0 7.3 2.054261996 6.243673693 0.619229315
02/10/2017 109 1 0 0 0 1 0 0 6.3 2.054261996 6.243673693 0.574019377
02/11/2017 110 0 0 0 0 1 0 0 5.8 2.054261996 6.243673693 0.576474297
02/12/2017 111 1 0 0 0 0 1 0 7.8 2.054261996 6.243673693 0.292138684
02/13/2017 112 0 0 1 1 0 0 0 6.1 -0.5847667 6.2407891 0.796668232
02/14/2017 113 1 0 0 1 0 0 1 6.5 1.418813574 6.238033264 0.347653035
02/14/2017 114 0 0 0 1 0 0 1 6.5 1.418813574 6.238033264 0.347653035
02/14/2017 115 0 0 0 1 0 0 1 6.5 1.418813574 6.238033264 0.347653035
02/14/2017 116 0 0 0 0 1 0 0 6.1 1.418813574 6.238033264 0.735497343
02/14/2017 117 0 1 0 1 0 0 0 4.3 1.418813574 6.238033264 0.51068871
02/14/2017 118 0 0 0 0 0 1 0 6.5 1.418813574 6.238033264 0.458087049
02/14/2017 119 0 0 0 0 1 0 0 4.5 1.418813574 6.238033264 0.402129607
02/15/2017 120 0 1 0 0 1 0 1 6.5 -0.041897715 6.235131309 0.308459288
"
I managed to run the dynlm model:
> data <- read.table(text=datastr,header=TRUE)
>
> library('dynlm')
> dynlm_model <-dynlm(formula = y ~ a + b + c + d*g + e*g + f*g + h + i + j, data)
> dynlm_model
Time series regression with "numeric" data:
Start = 1, End = 120
Call:
dynlm(formula = y ~ a + b + c + d * g + e * g + f * g + h + i +
j, data = data)
Coefficients:
(Intercept) a b c d g e f h i
-3.980619 -0.027872 -0.009254 0.238524 -0.091746 -0.333547 -0.080245 -0.287590 -0.115933 -0.000234
j d:g g:e g:f
0.870471 0.009906 0.038182 NA
Seems like you have data in Date order, and most probably also in time order, because ID is increasing.
However, your time step is varying, because the number of observations per date is not constant. The lag in the data sample varies approximately between a couple of days and several hours.
If you use the model, you will get indicative results for some kind of average time step, because the method does not know that your time step is varying. If you try to predict next points in the time series, then then the prediction the most accurate when you are closest to the average time step (e.g. 6h vs. 6h) and the least accurate when you are furthest from the average time step (e.g. 6 days vs. 6h).
Bearing this in mind, you may try to interpret the results of a fitted dynamic linear model. Even with a moderately varying time step, dlm models can be used in smoothing the data so that trends will be revealed within a scattered data.
Edit:
With ordinary lm function I get exactly the same results:
> lm_model <-lm(formula = y ~ a + b + c + d*g + e*g + f*g + h + i + j, data)
> lm_model
Call:
lm(formula = y ~ a + b + c + d * g + e * g + f * g + h + i +
j, data = data)
Coefficients:
(Intercept) a b c d g e f
-3.980619 -0.027872 -0.009254 0.238524 -0.091746 -0.333547 -0.080245 -0.287590
h i j d:g g:e g:f
-0.115933 -0.000234 0.870471 0.009906 0.038182 NA
So results from fitting formula = y ~ a + b + c + d * g + e * g + f * g + h + i + j with dynlm are exactly same as fitting the same formula with the regression fitting function lm.
According to dynlm package manual, you need to specify dynamics (via d() and L()) or linear/cyclical patterns (via trend(), season(), and harmon()) in the formula in order to get full advantage of dynlm.
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