I want to compute the following two regressions using R:

```
library("dynlm")
zooX = zoo(test[, -1])
lmx <- dynlm(d(Euribor3)~d(Ois3)+d(CDS)+d(Vstoxx)+d(log(omo))+d(L(Euribor3, 1)), data=zooX[1:16])
summary(lmx)
zooX = zoo(test[, -1])
lmx <- dynlm(d(Euribor3)~d(Ois3)+d(CDS)+d(Vstoxx)+d(log(omo))+d(L(Euribor3, 1)), data=zooX[17:31])
summary(lmx)
```

The only difference between those two models is the subset (the first[1:16] and the second [17:31]). Now these two models give me the following output:

```
Time series regression with "zoo" data:
Start = 3, End = 16
Call:
dynlm(formula = d(Euribor3) ~ d(Ois3) + d(CDS) + d(Vstoxx) +
d(log(omo)) + d(L(Euribor3, 1)), data = zooX[1:16])
```

and

```
Time series regression with "zoo" data:
Start = 19, End = 31
Call:
dynlm(formula = d(Euribor3) ~ d(Ois3) + d(CDS) + d(Vstoxx) +
d(log(omo)) + d(L(Euribor3, 1)), data = zooX[17:31])
```

As you can see from the output(coefficients, t value, etc. are not reported) the first model uses the observations [3:16] (since two are lost because of differencing the variables and taking the first lag of the dependent variable). The second model uses the observations [19:31] (again two observations are lost). Because of the transformation of the variables there arises a gap between the two subsets, i.e. the first model has the End=16 and the second model the Start=19, which means that in the regression the observations 17 and 18 are not "included". Now my question is what makes more sense? Either let the subsets in the model as they are or shift back the subset of the second model by two observations (i.e. to [15:31]), in order to close the gap? The output then would become:

```
Time series regression with "zoo" data:
Start = 17, End = 31
Call:
dynlm(formula = d(Euribor3) ~ d(Ois3) + d(CDS) + d(Vstoxx) +
d(log(omo)) + d(L(Euribor3, 1)), data = zooX[15:31])
```

As you can see the first model still has the End=16 and the second model now has the Start=17. Many Thanks!

Here is my data set:

```
Date Euribor3 Ois3 Vstoxx CDS omo
03.01.2005 2.154 2.089 14.47 17.938 344999
04.01.2005 2.151 2.084 14.51 17.886 344999
05.01.2005 2.151 2.087 14.42 17.95 333998
06.01.2005 2.15 2.085 13.8 17.95 333998
07.01.2005 2.146 2.086 13.57 17.913 333998
10.01.2005 2.146 2.087 12.92 17.958 333998
11.01.2005 2.146 2.089 13.68 17.962 333998
12.01.2005 2.145 2.085 14.05 17.886 339999
13.01.2005 2.144 2.084 13.64 17.568 339999
14.01.2005 2.144 2.085 13.57 17.471 339999
17.01.2005 2.143 2.085 13.2 17.365 339999
18.01.2005 2.144 2.085 13.17 17.214 347999
19.01.2005 2.143 2.086 13.63 17.143 354499
20.01.2005 2.144 2.087 14.17 17.125 354499
21.01.2005 2.143 2.087 13.96 17.193 354499
24.01.2005 2.143 2.086 14.11 17.283 354499
25.01.2005 2.144 2.086 13.63 17.083 354499
26.01.2005 2.143 2.086 13.32 17.348 347999
27.01.2005 2.144 2.085 12.46 17.295 352998
28.01.2005 2.144 2.084 12.81 17.219 352998
31.01.2005 2.142 2.084 12.72 17.143 352998
01.02.2005 2.142 2.083 12.36 17.125 352998
02.02.2005 2.141 2.083 12.25 17 357499
03.02.2005 2.144 2.088 12.38 16.808 357499
04.02.2005 2.142 2.084 11.6 16.817 357499
07.02.2005 2.142 2.084 11.99 16.798 359999
08.02.2005 2.141 2.083 11.92 16.804 355500
09.02.2005 2.142 2.08 12.19 16.589 355500
10.02.2005 2.14 2.08 12.04 16.5 355500
11.02.2005 2.14 2.078 11.99 16.429 355500
14.02.2005 2.139 2.078 12.52 16.042 355500
```

Answer:

If you do the subsetting yourself via `data = zooX[...,]`

, then `dynlm()`

doesn't see the full sample and hence has to lose two observations. If you supply the full `data = zooX`

and then set `end = 14`

and `start = 15`

respectively, then `dynlm()`

can first put together the full model frame with all lags/differences etc. and subsequently choose the desired subset. In the example below I do this but use the proper date index that is available in your data.

First, we read the data into a `zoo`

series with proper `Date`

index:

```
library("dynlm")
zooX <- read.zoo(textConnection("Date Euribor3 Ois3 Vstoxx CDS omo
03.01.2005 2.154 2.089 14.47 17.938 344999
04.01.2005 2.151 2.084 14.51 17.886 344999
05.01.2005 2.151 2.087 14.42 17.95 333998
06.01.2005 2.15 2.085 13.8 17.95 333998
07.01.2005 2.146 2.086 13.57 17.913 333998
10.01.2005 2.146 2.087 12.92 17.958 333998
11.01.2005 2.146 2.089 13.68 17.962 333998
12.01.2005 2.145 2.085 14.05 17.886 339999
13.01.2005 2.144 2.084 13.64 17.568 339999
14.01.2005 2.144 2.085 13.57 17.471 339999
17.01.2005 2.143 2.085 13.2 17.365 339999
18.01.2005 2.144 2.085 13.17 17.214 347999
19.01.2005 2.143 2.086 13.63 17.143 354499
20.01.2005 2.144 2.087 14.17 17.125 354499
21.01.2005 2.143 2.087 13.96 17.193 354499
24.01.2005 2.143 2.086 14.11 17.283 354499
25.01.2005 2.144 2.086 13.63 17.083 354499
26.01.2005 2.143 2.086 13.32 17.348 347999
27.01.2005 2.144 2.085 12.46 17.295 352998
28.01.2005 2.144 2.084 12.81 17.219 352998
31.01.2005 2.142 2.084 12.72 17.143 352998
01.02.2005 2.142 2.083 12.36 17.125 352998
02.02.2005 2.141 2.083 12.25 17 357499
03.02.2005 2.144 2.088 12.38 16.808 357499
04.02.2005 2.142 2.084 11.6 16.817 357499
07.02.2005 2.142 2.084 11.99 16.798 359999
08.02.2005 2.141 2.083 11.92 16.804 355500
09.02.2005 2.142 2.08 12.19 16.589 355500
10.02.2005 2.14 2.08 12.04 16.5 355500
11.02.2005 2.14 2.078 11.99 16.429 355500
14.02.2005 2.139 2.078 12.52 16.042 355500
"), header = TRUE, format = "%d.%m.%Y")
```

Then, we select the time index of the observation in the middle. You can either do this manually or based on the `zooX`

series:

```
mid <- as.Date("2005-01-24")
mid <- time(zooX)[ceiling(nrow(zooX)/2)]
```

In either case `mid`

now represents the middle time index 2005-01-24. And then we can set up our models:

```
f <- d(Euribor3) ~ d(Ois3) + d(CDS) + d(Vstoxx) + d(log(omo)) + d(L(Euribor3))
m1 <- dynlm(f, data = zooX, end = mid)
m2 <- dynlm(f, data = zooX, start = mid + 1)
```

The first one now runs up to 2005-01-24:

```
Time series regression with "zoo" data:
Start = 2005-01-05, End = 2005-01-24
Call:
dynlm(formula = f, data = zooX, end = mid)
Coefficients:
(Intercept) d(Ois3) d(CDS) d(Vstoxx) d(log(omo))
-0.0008859 -0.0125676 0.0001073 0.0012116 0.0124502
d(L(Euribor3))
-0.4217354
```

And the second one starts on 2005-01-25:

```
Time series regression with "zoo" data:
Start = 2005-01-25, End = 2005-02-14
Call:
dynlm(formula = f, data = zooX, start = mid + 1)
Coefficients:
(Intercept) d(Ois3) d(CDS) d(Vstoxx) d(log(omo))
-0.0005556 0.2565964 -0.0027670 -0.0009140 -0.0152427
d(L(Euribor3))
-0.5143080
```

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