[Limdep Nlogit List] RUM NLOGIT Land-Use Change Modeling
Mike Mann
mmann1123 at gmail.com
Thu Dec 16 03:03:05 EST 2010
I have few general questions about random utility models as they apply to
land use change in a NESTED LOGIT.
Here is a stylized version of the data. But general idea is predicting
current land-use based on rents, slope (and other choice invariant
properties of the parcels), and previous land use.
U(i) = rent(i,j) + G(i) - C(i), where i= parcel and j = LU choice,
G=choice invariant physical conditions (slope), C=conversion cost proxy
(previous land cover dummy variables).
Definitions- ID: Point representing a parcel LUse: Landuse options
Choice: LU Choosen Rent:$NPVpotential income Slope: slope of parcel
ID LUse Rent Slope PrevLUse Choice
1 Forest 20 2 Forest 0
1 Cerrado 0 2 Forest 0
1 Pasture 50 2 Forest 1
1 Agricult 120 2 Forest 0
1 Deforest 60 2 Forest 0
2 Forest 40 10 Agri 0
2 Cerrado 0 10 Agri 0
2 Pasture 80 10 Agri 0
2 Agricult 240 10 Agri 1
2 Deforest 10 10 Agri 0
.
Tree-> Human(pasture, agriculture, deforest), Natural(Forest, Cerrado)
*My general problem is that it is not clear to me which level of the nest
parameters should be estimated.* In particular I am confused by the
implications of the fact that ID identifies a particular parcel, and all
variables are particular to that parcel (whether choice invariant or
variant). Choices then reflect a decision made not for different plots,
with varying parcel charateristics, but for one plot with different land use
options (and therefore different rents).
Is it more appropriate to apply parcel-level choice variant and invariant
components at the leaf level?
U(past, agri, def, for, cer) = rent(i,j), slope(i), prevLU(i)
Or, (i,j) variables at leaf, and (i) variables at branch level?
U(past, agri, def, for, cer) = rent(ij)
U(Hum, Nat) = slope(i), prevLU(i)
Or all at branch level since choice reflects options for a particular plot
(i)?
U(Hum, Nat) = rent(ij), slope(i), prevLU(i)
Thank you for taking to time to look at this! Any suggestions or comments
would be helpful!
Sincerely,
Mike
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