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DO_spatial_cross_sectionsV3.txt
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DO_spatial_cross_sectionsV3.txt
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---------------------------------------------------------------------------------------------------------------------------
name: <unnamed>
log: /Users/carlos/Github/QuaRCS-lab/tutorial-spatial-cross-section-columbus-crime/DO_spatial_cross_sectionsV3.txt
log type: text
opened on: 29 May 2022, 11:59:17
.
. ** 3. Install modules
. *ssc install estout, replace all
. *ssc install outreg2, replace all
. *ssc install reghdfe, replace all * INFO: http://scorreia.com/software/reghdfe/
. *net install gr0009_1, from (http://www.stata-journal.com/software/sj10-1) replace all
. *net install tsg_schemes, from("https://raw.githubusercontent.com/asjadnaqvi/Stata-schemes/main/schemes/") replace all
. *set scheme white_tableau, permanently
. *set scheme gg_tableau, permanently
.
. *ssc install spmap
. *ssc install shp2dta
. *net install sg162, from(http://www.stata.com/stb/stb60)
. *net install st0292, from(http://www.stata-journal.com/software/sj13-2)
. *net install spwmatrix, from(http://fmwww.bc.edu/RePEc/bocode/s)
. *net install splagvar, from(http://fmwww.bc.edu/RePEc/bocode/s)
. *ssc install xsmle.pkg
. *ssc install xtcsd
. *ssc install geo2xy
. *ssc install palettes
. *ssc install colrspace
.
. ** Import W matrix from .csv file (See create_W_using_PySAL.ipynb AND https://github.com/quarcs-lab/tutorial-spatial-cro
> ss-section-columbus-crime/blob/main/df_ids_AND_WqueenMatrix.csv )
. import delimited "https://github.com/quarcs-lab/tutorial-spatial-cross-section-columbus-crime/raw/main/df_ids_AND_WqueenM
> atrix.csv", case(preserve) clear
(encoding automatically selected: ISO-8859-2)
(51 vars, 49 obs)
. drop v1
.
. ** Create weights matrix from tabular data file
. spset polyID
Sp dataset: <current>
Linked shapefile: <none>
Data: Cross sectional
Spatial-unit ID: _ID (equal to polyID)
Coordinates: <none>
. spmatrix fromdata WqueenSt = v*, normalize(row) replace
. spmatrix summarize WqueenSt
Weighting matrix WqueenSt
---------------------------------------
Type | contiguity
Normalization | row
Dimension | 49 x 49
Elements |
minimum | 0
minimum > 0 | .1
mean | .0204082
max | .5
Neighbors |
minimum | 2
mean | 4.816327
maximum | 10
---------------------------------------
.
. ** Import dataset with variables (Source: https://geodacenter.github.io/data-and-lab/columbus/)
. use "https://github.com/quarcs-lab/data-open/raw/master/Columbus/columbus/columbusDbase.dta", clear
. spset id
Sp dataset: columbusDbase.dta
Linked shapefile: <none>
Data: Cross sectional
Spatial-unit ID: _ID (equal to id)
Coordinates: <none>
.
. * Add labels
. label var CRIME "Crime"
. label var INC "Income"
. label var HOVAL "House value"
.
. ** OLS model
. reg CRIME INC HOVAL
Source | SS df MS Number of obs = 49
-------------+---------------------------------- F(2, 46) = 28.39
Model | 7423.32674 2 3711.66337 Prob > F = 0.0000
Residual | 6014.89274 46 130.758538 R-squared = 0.5524
-------------+---------------------------------- Adj R-squared = 0.5329
Total | 13438.2195 48 279.962906 Root MSE = 11.435
------------------------------------------------------------------------------
CRIME | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
INC | -1.597311 .3341308 -4.78 0.000 -2.269881 -.9247405
HOVAL | -.2739315 .1031987 -2.65 0.011 -.4816597 -.0662033
_cons | 68.61896 4.735486 14.49 0.000 59.08692 78.151
------------------------------------------------------------------------------
. eststo OLS
.
.
. ** Test for spatial dependence in the residuals
. qui reg CRIME INC HOVAL
. estat moran, errorlag(WqueenSt)
Moran test for spatial dependence
H0: Error terms are i.i.d.
Errorlags: WqueenSt
chi2(1) = 5.21
Prob > chi2 = 0.0225
.
. ** Taxanomy of spatial models
. collect clear
.
. * SAR/SLM model
. qui spregress CRIME INC HOVAL, ml dvarlag(WqueenSt)
. eststo SAR
. collect: qui estat impact
.
. * SEM model
. qui spregress CRIME INC HOVAL, ml errorlag(WqueenSt)
. eststo SEM
. collect: qui estat impact
.
. * SLX model
. qui spregress CRIME INC HOVAL, ml ivarlag(WqueenSt: INC HOVAL)
. eststo SLX
. collect: qui estat impact
.
. * SDM model
. qui spregress CRIME INC HOVAL, ml dvarlag(WqueenSt) ivarlag(WqueenSt: INC HOVAL)
. eststo SDM
. collect: qui estat impact
.
. * SDEM model
. qui spregress CRIME INC HOVAL, ml ivarlag(WqueenSt: INC HOVAL) errorlag(WqueenSt)
. eststo SDEM
. collect: qui estat impact
.
. * SARAR/SAC model
. qui spregress CRIME INC HOVAL, ml dvarlag(WqueenSt) errorlag(WqueenSt)
. eststo SARAR
. collect: qui estat impact
.
. * GNS model
. qui spregress CRIME INC HOVAL, ml dvarlag(WqueenSt) ivarlag(WqueenSt: INC HOVAL) errorlag(WqueenSt)
. eststo GNS
. collect: qui estat impact
.
. * Comparative results
. esttab OLS SAR SEM SLX SDM SDEM SARAR GNS, label b(%7.2f) star(* 0.10 ** 0.05 *** 0.01) stats(ll aic) mtitle("OLS" "SAR
> " "SEM" "SLX" "SDM" "SDEM" "SARAR" "GNS")
---------------------------------------------------------------------------------------------------------------------------
> -------------------------
(1) (2) (3) (4) (5) (6)
> (7) (8)
OLS SAR SEM SLX SDM SDEM
> SARAR GNS
---------------------------------------------------------------------------------------------------------------------------
> -------------------------
main
>
Income -1.60*** -1.05*** -0.96** -1.10*** -0.92*** -1.05***
> -1.04*** -0.96***
(-4.78) (-3.17) (-2.54) (-3.10) (-2.71) (-3.25) (
> -3.11) (-2.69)
House value -0.27** -0.27*** -0.30*** -0.29*** -0.30*** -0.28***
> -0.28*** -0.29***
(-2.65) (-3.00) (-3.30) (-3.06) (-3.30) (-3.05) (
> -2.98) (-3.13)
Constant 68.62*** 45.60*** 60.28*** 74.55*** 44.32*** 73.65***
> 47.92*** 53.05*
(14.49) (5.73) (10.21) (11.72) (3.12) (8.41)
> (5.15) (1.69)
---------------------------------------------------------------------------------------------------------------------------
> -------------------------
WqueenSt
>
Crime 0.42*** 0.40**
> 0.37** 0.28
(3.37) (2.35)
> (2.06) (0.68)
e.Crime 0.55*** 0.40**
> 0.15 0.16
(3.47) (2.28)
> (0.49) (0.35)
Income -1.40*** -0.58 -1.20**
> -0.77
(-2.64) (-0.96) (-2.06)
> (-0.90)
House value 0.21 0.26 0.13
> 0.22
(1.09) (1.39) (0.62)
> (0.95)
---------------------------------------------------------------------------------------------------------------------------
> -------------------------
/
>
var(e.CRIME) 96.86*** 97.67*** 106.82*** 93.27*** 93.80***
> 97.04*** 94.49***
(4.90) (4.78) (4.95) (4.87) (4.86)
> (4.91) (4.89)
---------------------------------------------------------------------------------------------------------------------------
> -------------------------
ll -187.38 -182.67 -183.75 -183.97 -181.64 -181.78 -
> 182.56 -181.58
aic 380.75 375.35 377.50 379.94 377.28 377.56
> 377.11 379.16
---------------------------------------------------------------------------------------------------------------------------
> -------------------------
t statistics in parentheses
* p<0.10, ** p<0.05, *** p<0.01
. *eststo clear
.
. ** Direct and indirect effects
.
. collect label list cmdset, all
Collection: default
Dimension: cmdset
Label: Command results index
Level labels:
1
2
3
4
5
6
7
. collect style autolevels result b_direct b_indirect
. collect label levels cmdset 1 "SAR" 2 "SEM" 3 "SLX" 4 "SDM" 5 "SDEM" 6 "SARAR" 7 "GNS"
. collect style cell, nformat(%7.2f)
. collect layout (colname#result) (cmdset)
Collection: default
Rows: colname#result
Columns: cmdset
Table 1: 6 x 7
--------------------------------------------------------
| SAR SEM SLX SDM SDEM SARAR GNS
-------------+------------------------------------------
Income |
b_direct | -1.10 -0.96 -1.10 -1.02 -1.05 -1.08 -1.03
b_indirect | -0.72 0.00 -1.40 -1.50 -1.20 -0.57 -1.39
House value |
b_direct | -0.28 -0.30 -0.29 -0.28 -0.28 -0.29 -0.28
b_indirect | -0.18 0.00 0.21 0.22 0.13 -0.15 0.18
--------------------------------------------------------
.
.
. ** Model selection
.
. * LR test: SDM becomes SAR? (If p < 10%, No)
. lrtest SDM SAR
Likelihood-ratio test
Assumption: SAR nested within SDM
LR chi2(2) = 2.07
Prob > chi2 = 0.3553
.
. * LR test: SDM becomes SEM? (If p < 10%, No)
. lrtest SDM SEM
Likelihood-ratio test
Assumption: SEM nested within SDM
LR chi2(2) = 4.22
Prob > chi2 = 0.1212
.
. * LR test: SDM becomes SLX? (If p < 10%, No)
. lrtest SDM SLX
Likelihood-ratio test
Assumption: SLX nested within SDM
LR chi2(1) = 4.66
Prob > chi2 = 0.0308
.
. * Wald test: SDM becomes SAR? (If p < 10%, No)
. test ([WqueenSt]INC = 0) ([WqueenSt]HOVAL = 0)
( 1) [WqueenSt]INC = 0
( 2) [WqueenSt]HOVAL = 0
chi2( 2) = 1.93
Prob > chi2 = 0.3809
.
. * Wald test: SDM becomes SEM? (If p < 10%, No)
. testnl ([WqueenSt]INC = -[WqueenSt]CRIME*[CRIME]INC) ([WqueenSt]HOVAL = -[WqueenSt]CRIME*[CRIME]HOVAL)
(1) [WqueenSt]INC = -[WqueenSt]CRIME*[CRIME]INC
(2) [WqueenSt]HOVAL = -[WqueenSt]CRIME*[CRIME]HOVAL
chi2(2) = 3.21
Prob > chi2 = 0.2014
.
. * Wald test: SDM becomes SLX? (If p < 10%, No)
. test ([WqueenSt]CRIME = 0)
( 1) [WqueenSt]CRIME = 0
chi2( 1) = 0.46
Prob > chi2 = 0.4967
.
. * LM tests (W created using spatwmat)
. spatwmat using "WqueenBin.dta", name(WqueenSt_from_spatwmat) eigenval(eWqueenSt_from_spatwmat) standardize
The following matrices have been created:
1. Imported binary weights matrix WqueenSt_from_spatwmat (row-standardized)
Dimension: 49x49
2. Eigenvalues matrix eWqueenSt_from_spatwmat
Dimension: 49x1
. qui reg CRIME INC HOVAL
. spatdiag, weights(WqueenSt_from_spatwmat)
Diagnostic tests for spatial dependence in OLS regression
Fitted model
------------------------------------------------------------
CRIME = INC + HOVAL
------------------------------------------------------------
Weights matrix
------------------------------------------------------------
Name: WqueenSt_from_spatwmat
Type: Imported (binary)
Row-standardized: Yes
------------------------------------------------------------
Diagnostics
------------------------------------------------------------
Test | Statistic df p-value
-------------------------------+----------------------------
Spatial error: |
Moran's I | 2.840 1 0.005
Lagrange multiplier | 5.206 1 0.023
Robust Lagrange multiplier | 0.044 1 0.834
|
Spatial lag: |
Lagrange multiplier | 8.898 1 0.003
Robust Lagrange multiplier | 3.736 1 0.053
------------------------------------------------------------
.
. ** 99. Close log file
. log close
name: <unnamed>
log: /Users/carlos/Github/QuaRCS-lab/tutorial-spatial-cross-section-columbus-crime/DO_spatial_cross_sectionsV3.txt
log type: text
closed on: 29 May 2022, 11:59:20
---------------------------------------------------------------------------------------------------------------------------