[Gretl-users] Deterministic trend in VAR

One should not even think of estimating an 11 variable VAR(4) with 100 observations. Standard errors will be so large that any null will be acceptable an tests will have no power. If he draws impulse response functions confidence intervals will be so large that he will be unable to draw any conclusions. Is there no way that the number of variables can be reduced to say 3-5 On Tuesday, 13 December 2011, Summers, Peter <psummers(a)highpoint.edu&gt; wrote: then based on the unit root tests you showed earlier, it is not "stable (and stationary) without a trend." On the contrary, it has a unit root -- and is therefore non-stationary -- whether or not a deterministic trend is included in the dgp. renders it non-stationary. lags has 44 parameters per equation, not counting a constant (or trend?!). There are also 55 parameters in the covariance matrix. With 100 observations per series, you're asking quite a lot of your data set. Even if you knew the covariance matrix for sure, you'd have just over 2 obs/parameter for estimating the dynamics. I don't think that's asymptotic yet, but I could be wrong ;-) on behalf of Dr RJF Hudson [rjfhud(a)powerup.com.au] are confident that you haven't what's the reason to introduce trend information and then trust inferences from the results ? in the model. Well, the VAR is a 11 variable VAR (4). The 11 variables are GDP and macroeconomic variables. if the reduced form is stable (and stationary) WITHOUT a trend, should one include a trend when the univariate tests suggest that SOME of the series may have trend in their unit roots. <mailto:psummers@highpoint.edu>> wrote: unit root in at least one series. the list is too). If this is one of the series in your VAR, then it's not stable/stationary, by definition. That is, the lag operator polynomial will have at least one root on the unit circle. My earlier answer assumed that your unit root & cointegration tests ruled out both, but now it seems that's not the case. that 100 obs per series isn't really a lot, especially if you're trying to sort out issues related to deterministic vs stochastic trends, cointegration vs none, etc. favorite reference on VARs & VECMs, and/or b) providing some more detail about what you're trying to do. gretl-users-bounces(a)lists.wfu.edu&gt; [gretl-users-bounces(a)lists.wfu.edu <mailto:gretl-users-bounces@lists.wfu.edu>] on behalf of Muheed Jamaldeen [ mj.myworld@gmail.com<mailto:mj.myworld@gmail.com>] an issue. I am wondering if the deterministic trend is an issue at all because the VAR is stable implying stationarity of the described process in each equation WITHOUT the trend (i.e. the polynomial defined by the determinant of the autoregressive operator has no roots in and on the complex unit circle without the time trend term). you an example. Following is the ADF tests for logged US GDP. root tests according to the formula: Int {12(T /100)1/ 4} so the lag order is 12 with 100 observations. Including I(1) variables in an unrestricted VAR is fine as Lutekepohl and Toda and Yammoto have demonstrated. It's a question of whether a trend term is to be included. I am inclined to think not because the VAR is stable WITHOUT a trend. <mailto:psummers@highpoint.edu><mailto:psummers@highpoint.edu<mailto: psummers(a)highpoint.edu&gt;&gt;&gt; wrote: that up (though there may be a problem in small samples). If you include a trend but the dgp is stationary, then a t-test should conclude that the trend coefficient is zero. Presumably your unit root tests reject the null, right? gretl-users-bounces@lists.wfu.edu><mailto:gretl-users-bounces@lists.wfu.edu <mailto:gretl-users-bounces@lists.wfu.edu>> [mailto: gretl-users-bounces@lists.wfu.edu<mailto:gretl-users-bounces@lists.wfu.edu gretl-users-bounces(a)lists.wfu.edu&gt;&gt;] On Behalf Of Muheed Jamaldeen deterministic time trend in the reduced form VAR? Visually some of the data series (not all) look like they have trending properties. In any case, does the inclusion of the time trend matter if the process is stable and therefore stationary (i.e. the polynomial defined by the determinant of the autoregressive operator has no roots in and on the complex unit circle) without the time trend term. Other than unit root tests, is there a better way to test whether the underlying data generating process has a stochastic or deterministic process? Gretl-users@lists.wfu.edu<mailto:Gretl-users@lists.wfu.edu>>