plm is a very versatile function which enable the estimation of a wide range of error component models. Those models can be written as follows :
\[ y_{nt}=\alpha + \beta^\top x_{nt} + \epsilon_{nt} = \alpha + \beta^\top x_{nt} + \eta_n + \mu_t + \nu_{nt} \]
where \(n\) and \(t\) are the individual and time indexes, \(y\) the response, \(x\) a vector of covariates, \(\alpha\) the overall intercept and \(\beta\) the vector of parameters of interest that we are willing to estimate. The error term \(\epsilon_{nt}\) is composed of three elements (in the two-way case):
plmThe first two arguments of plm are, like for most of the estimation functions of R a formula which describes the model to be estimated and a data.frame. subset, weights, and na.action are also available and have the same behavior as in the lm function. Three more main arguments can be set :
index helps plm to understand the structure of the data : if NULL, the first two columns of the data are assumed to contain the individual or the time index. Otherwise, supply the column names of the individual and time index as a character, e.g., use something like c("firm", "year") or just "firm" if there is no explicit time index.effect indicates the effects that should be taken into account ; this is one of "individual", "time", and "twoways".model indicates the model to be estimated : "pooling" is just the OLS estimation (equivalent to a call to lm), "between" performs the estimation on the individual or time means, "within" on the deviations from the individual or/and time mean, "fd" on the first differences and "random" perform a feasible generalized least squares estimation which takes into account the correlation induced by the presence of individual and/or time effects.The estimation of all but the last model is straightforward, as it requires only the estimation by OLS of obvious transformations of the data. The GLS model requires more explanation. In most of the cases, the estimation is obtained by quasi-differencing the data from the individual and/or the time means. The coefficients used to perform this quasi-difference depends on estimators of the variance of the components of the error, namely \(\sigma^2_\nu\), \(\sigma^2_\eta\) in case of individual effects and \(\sigma^2_\mu\) in case of time effects.
The most common technique used to estimate these variance is to use the following result :
\[ \frac{\mbox{E}(\epsilon^\top W \epsilon)}{N(T-1)} = \sigma_\nu^2 \]
and
\[ \frac{\mbox{E}(\epsilon^\top B \epsilon)}{N} = T \sigma_\eta^2 + \sigma_\nu^2 \]
where \(B\) and \(W\) are respectively the matrices that performs the individual (or time) means and the deviations from these means. Consistent estimators can be obtained by replacing the unknown errors by the residuals of a consistent preliminary estimation and by dropping the expecting value operator. Some degree of freedom correction can also be introduced. plm calls the general function ercomp to estimate the variances. Important arguments to ercomp are:
models indicates which models are estimated in order to calculate the two quadratic forms ; for example c("within", "Between"). Note that when only one model is provided in models, this means that the same residuals are used to compute the two quadratic forms.dfcor indicates what kind of degrees of freedom correction is used : if 0, the quadratic forms are divided by the number of observations, respectively \(N\times T\) and \(N\) ; if 1, the numerators of the previous expressions are used (\(N\times (T-1)\) and \(N\)) ; if 2, the number of estimated parameters in the preliminary estimate \(K\) is deducted. Finally, if 3, the unbiased version is computed, which is based on much more complex computations, which relies on the calculus of the trace of different cross-products which depends on the preliminary models used.method is an alternative to the models argument; it is one of :
"walhus" (equivalent to setting models = c("pooling")), Wallace and Hussain (1969),"swar" (equivalent to models = c("within", "Between")), Swamy and Arora (1972),"amemiya" (equivalent to models = c("within")), T. Amemiya (1971),"nerlove", which is a specific method which doesn’t fit to the quadratic form methodology described above (Nerlove (1971)) and uses an within model for the variance estimation as well,"ht" is an slightly modified version of "amemiya": when there are time-invariant covariates, the T. Amemiya (1971) estimator of the individual component of the variance under-estimates as the time-invariant covariates disappear in the within regression. In this case, Hausman and Taylor (1981) proposed to regress the estimation of the individual effects on the time-invariant covariates and use the residuals in order to estimate the components of the variance.Note that for plm, the arguments are random.models, random.dfcor, and random.method and correspond to arguments models, method, and random.dfcor of function ercomp with the same values as above, respectively.
To illustrate the use of plm, we use examples reproduced in B. H. Baltagi (2013), p. 21; B. H. Baltagi (2021), p. 31, table 2.1 presents EViews’ results of the estimation on the Grunfeld data set :
library("plm")
data("Grunfeld", package = "plm")
ols <- plm(inv ~ value + capital, Grunfeld, model = "pooling")
between <- update(ols, model = "between")
within <- update(ols, model = "within")
walhus <- update(ols, model = "random", random.method = "walhus", random.dfcor = 3)
amemiya <- update(walhus, random.method = "amemiya")
swar <- update(amemiya, random.method = "swar")Note that the random.dfcor argument is set to 3, which means that the unbiased version of the estimation of the error components is used. We use the texreg package to present the results :
library("texreg")
screenreg(list(ols = ols, between = between, within = within,
walhus = walhus, amemiya = amemiya, swar = swar),
digits = 5, omit.coef = "(Intercept)")##
## =================================================================================================
## ols between within walhus amemiya swar
## -------------------------------------------------------------------------------------------------
## value 0.11556 *** 0.13465 ** 0.11012 *** 0.10979 *** 0.10978 *** 0.10978 ***
## (0.00584) (0.02875) (0.01186) (0.01052) (0.01048) (0.01049)
## capital 0.23068 *** 0.03203 0.31007 *** 0.30818 *** 0.30808 *** 0.30811 ***
## (0.02548) (0.19094) (0.01735) (0.01717) (0.01718) (0.01718)
## -------------------------------------------------------------------------------------------------
## R^2 0.81241 0.85777 0.76676 0.76941 0.76954 0.76950
## Adj. R^2 0.81050 0.81713 0.75311 0.76707 0.76720 0.76716
## Num. obs. 200 10 200 200 200 200
## s_idios 53.74518 52.76797 52.76797
## s_id 87.35803 83.52354 84.20095
## =================================================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05The estimated variance can be extracted using the ercomp function. For example, for the amemiya model :
ercomp(amemiya)## var std.dev share
## idiosyncratic 2784.46 52.77 0.285
## individual 6976.18 83.52 0.715
## theta: 0.8601B. H. Baltagi (2013), p. 27; B. H. Baltagi (2021), p. 31 presents the Stata estimation of the Swamy-Arora estimator ; the Swamy-Arora estimator is the same if random.dfcor is set to 3 or 2 (the quadratic forms are divided by \(\sum_n T_n - K - N\) and by \(N - K - 1\)), so I don’t know what is the behaviour of Stata for the other estimators for which the unbiased estimators differs from the simple one.
data("Produc", package = "plm")
PrSwar <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, Produc,
model = "random", random.method = "swar", random.dfcor = 3)
summary(PrSwar)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
## data = Produc, model = "random", random.method = "swar",
## random.dfcor = 3)
##
## Balanced Panel: n = 48, T = 17, N = 816
##
## Effects:
## var std.dev share
## idiosyncratic 0.001454 0.038137 0.175
## individual 0.006838 0.082691 0.825
## theta: 0.8888
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.1067230 -0.0245520 -0.0023694 0.0217333 0.1996307
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 2.13541100 0.13346149 16.0002 < 0.00000000000000022 ***
## log(pcap) 0.00443859 0.02341732 0.1895 0.8497
## log(pc) 0.31054843 0.01980475 15.6805 < 0.00000000000000022 ***
## log(emp) 0.72967053 0.02492022 29.2803 < 0.00000000000000022 ***
## unemp -0.00617247 0.00090728 -6.8033 0.00000000001023 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 29.209
## Residual Sum of Squares: 1.1879
## R-Squared: 0.95933
## Adj. R-Squared: 0.95913
## Chisq: 19131.1 on 4 DF, p-value: < 0.000000000000000222The two-ways effect model is obtained by setting the effect argument to "twoways". B. H. Baltagi (2013) pp. 51-53; B. H. Baltagi (2021), pp. 61-62, tables 3.1-3.3, presents EViews’ output for the Grunfeld data set.
Grw <- plm(inv ~ value + capital, Grunfeld, model = "random", effect = "twoways",
random.method = "walhus", random.dfcor = 3)
Grs <- update(Grw, random.method = "swar")
Gra <- update(Grw, random.method = "amemiya")
screenreg(list("Wallace-Hussain" = Grw, "Swamy-Arora" = Grs, "Amemiya" = Gra), digits = 5)##
## ==========================================================
## Wallace-Hussain Swamy-Arora Amemiya
## ----------------------------------------------------------
## (Intercept) -57.81705 * -57.86538 * -63.89217 *
## (28.63258) (29.39336) (30.53284)
## value 0.10978 *** 0.10979 *** 0.11145 ***
## (0.01047) (0.01053) (0.01096)
## capital 0.30807 *** 0.30819 *** 0.32353 ***
## (0.01719) (0.01717) (0.01877)
## ----------------------------------------------------------
## s_idios 55.33298 51.72452 51.72452
## s_id 87.31428 84.23332 89.26257
## s_time 0.00000 0.00000 15.77783
## R^2 0.76956 0.76940 0.74898
## Adj. R^2 0.76722 0.76706 0.74643
## Num. obs. 200 200 200
## ==========================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05The estimated variance of the time component is negative for the Wallace-Hussain as well as the Swamy-Arora models and plm sets it to 0.
B. H. Baltagi (2009) pp. 60-62, presents EViews’ output for the Produc data.
data("Produc", package = "plm")
Prw <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, Produc,
model = "random", random.method = "walhus",
effect = "twoways", random.dfcor = 3)
Prs <- update(Prw, random.method = "swar")
Pra <- update(Prw, random.method = "amemiya")
screenreg(list("Wallace-Hussain" = Prw, "Swamy-Arora" = Prs, "Amemiya" = Pra), digits = 5)##
## ==========================================================
## Wallace-Hussain Swamy-Arora Amemiya
## ----------------------------------------------------------
## (Intercept) 2.39200 *** 2.36350 *** 2.85210 ***
## (0.13833) (0.13891) (0.18502)
## log(pcap) 0.02562 0.01785 0.00221
## (0.02336) (0.02332) (0.02469)
## log(pc) 0.25781 *** 0.26559 *** 0.21666 ***
## (0.02128) (0.02098) (0.02438)
## log(emp) 0.74180 *** 0.74490 *** 0.77005 ***
## (0.02371) (0.02411) (0.02584)
## unemp -0.00455 *** -0.00458 *** -0.00398 ***
## (0.00106) (0.00102) (0.00108)
## ----------------------------------------------------------
## s_idios 0.03571 0.03429 0.03429
## s_id 0.08244 0.08279 0.15390
## s_time 0.01595 0.00984 0.02608
## R^2 0.92915 0.93212 0.85826
## Adj. R^2 0.92880 0.93178 0.85756
## Num. obs. 816 816 816
## ==========================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05Two difficulties arise with unbalanced panels :
B. H. Baltagi (2021), B. H. Baltagi (2013), and B. H. Baltagi (2009) present results of the estimation of the Swamy and Arora (1972) model with the Hedonic data set. B. H. Baltagi (2013), p. 195; B. H. Baltagi (2021), p. 237, table 9.1, presents the Stata output and B. H. Baltagi (2009), p. 211 presents EViews’ output. EViews’ Wallace-Hussain estimator is reported in B. H. Baltagi (2009), p. 210.
data("Hedonic", package = "plm")
form <- mv ~ crim + zn + indus + chas + nox + rm +
age + dis + rad + tax + ptratio + blacks + lstat
HedStata <- plm(form, Hedonic, model = "random", index = "townid",
random.models = c("within", "between"))
HedEviews <- plm(form, Hedonic, model = "random", index = "townid",
random.models = c("within", "Between"))
HedEviewsWH <- update(HedEviews, random.models = "pooling")
screenreg(list(EViews = HedEviews, Stata = HedStata, "Wallace-Hussain" = HedEviewsWH),
digits = 5, single.row = TRUE)##
## ======================================================================================
## EViews Stata Wallace-Hussain
## --------------------------------------------------------------------------------------
## (Intercept) 9.68587 (0.19751) *** 9.67780 (0.20714) *** 9.68443 (0.19922) ***
## crim -0.00741 (0.00105) *** -0.00723 (0.00103) *** -0.00738 (0.00105) ***
## zn 0.00008 (0.00065) 0.00004 (0.00069) 0.00007 (0.00066)
## indus 0.00156 (0.00403) 0.00208 (0.00434) 0.00165 (0.00409)
## chasyes -0.00442 (0.02921) -0.01059 (0.02896) -0.00565 (0.02916)
## nox -0.00584 (0.00125) *** -0.00586 (0.00125) *** -0.00585 (0.00125) ***
## rm 0.00906 (0.00119) *** 0.00918 (0.00118) *** 0.00908 (0.00119) ***
## age -0.00086 (0.00047) -0.00093 (0.00046) * -0.00087 (0.00047)
## dis -0.14442 (0.04409) ** -0.13288 (0.04568) ** -0.14236 (0.04439) **
## rad 0.09598 (0.02661) *** 0.09686 (0.02835) *** 0.09614 (0.02692) ***
## tax -0.00038 (0.00018) * -0.00037 (0.00019) * -0.00038 (0.00018) *
## ptratio -0.02948 (0.00907) ** -0.02972 (0.00975) ** -0.02951 (0.00919) **
## blacks 0.56278 (0.10197) *** 0.57506 (0.10103) *** 0.56520 (0.10179) ***
## lstat -0.29107 (0.02393) *** -0.28514 (0.02385) *** -0.28991 (0.02391) ***
## --------------------------------------------------------------------------------------
## s_idios 0.13025 0.13025 0.14050
## s_id 0.11505 0.12974 0.12698
## R^2 0.99091 0.99029 0.99081
## Adj. R^2 0.99067 0.99004 0.99057
## Num. obs. 506 506 506
## ======================================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05The difference is due to the fact that Stata uses a between regression on \(N\) observations while EViews uses a between regression on \(\sum_n T_n\) observations, which are not the same on unbalanced panels. Note the use of between with or without the B capitalized ("Between" and "between") in the random.models argument. plm’s default is to use the between regression with \(\sum_n T_n\) observations when setting model = "random", random.method = "swar". The default employed is what the original paper for the unbalanced one-way Swamy-Arora estimator defined (in B. H. Baltagi and Chang (1994), p. 73). A more detailed analysis of Stata’s Swamy-Arora estimation procedure is given by Cottrell (2017).
All of the models presented above may be estimated using instrumental variables (IV). The instruments are specified using two- or three-part formulas, each part being separated by a | sign :
The instrumental variables estimator used is indicated with the inst.method argument:
"bvk", from Balestra and Varadharajan–Krishnakumar (1987), the default value : in this case, all the instruments are introduced in quasi-differences, using the same transformation as for the response and the covariates,"baltagi", from B. H. Baltagi (1981), the instruments of the second part are introduced twice by using the between and the within transformation and instruments of the third part are introduced with only the within transformation,"am", from Takeshi Amemiya and MaCurdy (1986), in addition to the instrument set of "baltagi", the within transformation of the variables of the second part for each period are also included as instruments,"bms", from Breusch, Mizon, and Schmidt (1989), in addition to the instrument set of "baltagi", the within transformation of the variables of the second and the third part for each period are included as instruments.The various possible values of the inst.method argument are not relevant for fixed effect IV models as there is only one method for this type of IV models but many for random effect IV models.
The instrumental variable estimators are illustrated in the following example from B. H. Baltagi (2005), pp. 117/120; B. H. Baltagi (2013), pp. 133/137; B. H. Baltagi (2021), pp. 162/165, tables 7.1, 7.3.
data("Crime", package = "plm")
crbalt <- plm(lcrmrte ~ lprbarr + lpolpc + lprbconv + lprbpris + lavgsen +
ldensity + lwcon + lwtuc + lwtrd + lwfir + lwser + lwmfg + lwfed +
lwsta + lwloc + lpctymle + lpctmin + region + smsa + factor(year)
| . - lprbarr - lpolpc + ltaxpc + lmix,
data = Crime, model = "random", inst.method = "baltagi")
crbvk <- update(crbalt, inst.method = "bvk")
crwth <- update(crbalt, model = "within")
crbe <- update(crbalt, model = "between")
screenreg(list(FE2SLS = crwth, BE2SLS = crbe, EC2SLS = crbalt, G2SLS = crbvk),
single.row = FALSE, digits = 5, omit.coef = "(region)|(year)",
reorder.coef = c(1:16, 19, 18, 17))##
## ===================================================================
## FE2SLS BE2SLS EC2SLS G2SLS
## -------------------------------------------------------------------
## lprbarr -0.57551 -0.50294 * -0.41293 *** -0.41414
## (0.80218) (0.24062) (0.09740) (0.22105)
## lpolpc 0.65753 0.40844 * 0.43475 *** 0.50495 *
## (0.84687) (0.19300) (0.08970) (0.22778)
## lprbconv -0.42314 -0.52477 *** -0.32289 *** -0.34325 **
## (0.50194) (0.09995) (0.05355) (0.13246)
## lprbpris -0.25026 0.18718 -0.18632 *** -0.19005 **
## (0.27946) (0.31829) (0.04194) (0.07334)
## lavgsen 0.00910 -0.22723 -0.01018 -0.00644
## (0.04899) (0.17851) (0.02702) (0.02894)
## ldensity 0.13941 0.22562 * 0.42903 *** 0.43434 ***
## (1.02124) (0.10247) (0.05485) (0.07115)
## lwcon -0.02873 0.31400 -0.00748 -0.00430
## (0.05351) (0.25910) (0.03958) (0.04142)
## lwtuc 0.03913 -0.19894 0.04545 * 0.04446 *
## (0.03086) (0.19712) (0.01979) (0.02154)
## lwtrd -0.01775 0.05356 -0.00814 -0.00856
## (0.04531) (0.29600) (0.04138) (0.04198)
## lwfir -0.00934 0.04170 -0.00364 -0.00403
## (0.03655) (0.30562) (0.02892) (0.02946)
## lwser 0.01859 -0.13543 0.00561 0.01056
## (0.03882) (0.17365) (0.02013) (0.02158)
## lwmfg -0.24317 -0.04200 -0.20414 * -0.20180 *
## (0.41955) (0.15627) (0.08044) (0.08394)
## lwfed -0.45134 0.14803 -0.16351 -0.21346
## (0.52712) (0.32565) (0.15945) (0.21510)
## lwsta -0.01875 -0.20309 -0.05405 -0.06012
## (0.28082) (0.29815) (0.10568) (0.12031)
## lwloc 0.26326 0.04444 0.16305 0.18354
## (0.31239) (0.49436) (0.11964) (0.13968)
## lpctymle 0.35112 -0.09472 -0.10811 -0.14587
## (1.01103) (0.19180) (0.13969) (0.22681)
## smsayes -0.08050 -0.22515 -0.25955
## (0.14423) (0.11563) (0.14997)
## lpctmin 0.16890 ** 0.18904 *** 0.19488 ***
## (0.05270) (0.04150) (0.04594)
## (Intercept) -1.97714 -0.95380 -0.45385
## (4.00081) (1.28397) (1.70298)
## -------------------------------------------------------------------
## R^2 0.44364 0.87385 0.59847 0.59230
## Adj. R^2 0.32442 0.83729 0.58115 0.57472
## Num. obs. 630 90 630 630
## s_idios 0.14924 0.14924
## s_id 0.21456 0.21456
## ===================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05The Hausman-Taylor model (Hausman and Taylor (1981)) may be estimated with the plm function by setting argument random.method = "ht" and inst.method = "baltagi". The following example is from B. H. Baltagi (2005), p. 130; B. H. Baltagi (2013), pp. 145-7, tables 7.4-7.6; B. H. Baltagi (2021), pp. 174-6 , tables 7.5-7.7.
data("Wages", package = "plm")
ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp^2) +
bluecol + ind + union + sex + black + ed |
bluecol + south + smsa + ind + sex + black |
wks + married + exp + I(exp^2) + union,
data = Wages, index = 595,
inst.method = "baltagi", model = "random",
random.method = "ht")
am <- update(ht, inst.method = "am")
bms <- update(ht, inst.method = "bms")
screenreg(list("Hausman-Taylor" = ht, "Amemiya-MaCurdy" = am,
"Breusch-Mizon-Schmidt" = bms),
digits = 5, single.row = FALSE)##
## ===================================================================
## Hausman-Taylor Amemiya-MaCurdy Breusch-Mizon-Schmidt
## -------------------------------------------------------------------
## (Intercept) 2.91273 *** 2.92734 *** 1.97944 ***
## (0.28365) (0.27513) (0.26724)
## wks 0.00084 0.00084 0.00080
## (0.00060) (0.00060) (0.00060)
## southyes 0.00744 0.00728 0.01467
## (0.03196) (0.03194) (0.03188)
## smsayes -0.04183 * -0.04195 * -0.05204 **
## (0.01896) (0.01895) (0.01891)
## marriedyes -0.02985 -0.03009 -0.03926 *
## (0.01898) (0.01897) (0.01892)
## exp 0.11313 *** 0.11297 *** 0.10867 ***
## (0.00247) (0.00247) (0.00246)
## exp^2 -0.00042 *** -0.00042 *** -0.00049 ***
## (0.00005) (0.00005) (0.00005)
## bluecolyes -0.02070 -0.02085 -0.01539
## (0.01378) (0.01377) (0.01374)
## ind 0.01360 0.01363 0.01902
## (0.01524) (0.01523) (0.01520)
## unionyes 0.03277 * 0.03248 * 0.03786 *
## (0.01491) (0.01489) (0.01486)
## sexfemale -0.13092 -0.13201 -0.18027
## (0.12666) (0.12660) (0.12639)
## blackyes -0.28575 -0.28590 -0.15636
## (0.15570) (0.15549) (0.15506)
## ed 0.13794 *** 0.13720 *** 0.22066 ***
## (0.02125) (0.02057) (0.01985)
## -------------------------------------------------------------------
## s_idios 0.15180 0.15180 0.15180
## s_id 0.94180 0.94180 0.94180
## R^2 0.60945 0.60948 0.60686
## Adj. R^2 0.60833 0.60835 0.60572
## Num. obs. 4165 4165 4165
## ===================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05This section shows how the nested error component model as per B. H. Baltagi, Song, and Jung (2001) can be estimated. The model is given by :
\[ y_{nt}=\alpha + \beta^\top x_{jnt} + u_{jnt} = \alpha + \beta^\top x_{jnt} + \mu_{j} + \nu_{jn} + \epsilon_{jnt} \] where \(n\) and \(t\) are the individual and time indexes and \(j\) is the group index in which the individuals are nested. The error \(u_{jnt}\) consists of three components :
In the estimated examples below (replication of B. H. Baltagi, Song, and Jung (2001), p. 378, table 6; B. H. Baltagi (2021), p. 248, table 9.1), states are nested within regions. The group index is given in the 3rd position of the index argument to pdata.frame or to plm directly and plm’s argument effect is set to "nested":
data("Produc", package = "plm")
swar <- plm(form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp,
Produc, index = c("state", "year", "region"), model = "random", effect = "nested", random.method = "swar")
walhus <- update(swar, random.method = "walhus")
amem <- update(swar, random.method = "amemiya")
screenreg(list("Swamy-Arora" = swar, "Wallace-Hussain" = walhus, "Amemiya" = amem), digits = 5)##
## ==========================================================
## Swamy-Arora Wallace-Hussain Amemiya
## ----------------------------------------------------------
## (Intercept) 2.08921 *** 2.08165 *** 2.13133 ***
## (0.14570) (0.15035) (0.16014)
## log(pc) 0.27412 *** 0.27256 *** 0.26448 ***
## (0.02054) (0.02093) (0.02176)
## log(emp) 0.73984 *** 0.74164 *** 0.75811 ***
## (0.02575) (0.02607) (0.02661)
## log(hwy) 0.07274 *** 0.07493 *** 0.07211 **
## (0.02203) (0.02235) (0.02363)
## log(water) 0.07645 *** 0.07639 *** 0.07616 ***
## (0.01386) (0.01387) (0.01402)
## log(util) -0.09437 *** -0.09523 *** -0.10151 ***
## (0.01677) (0.01677) (0.01705)
## unemp -0.00616 *** -0.00615 *** -0.00584 ***
## (0.00090) (0.00091) (0.00091)
## ----------------------------------------------------------
## s_idios 0.03676 0.03762 0.03676
## s_id 0.06541 0.06713 0.08306
## s_gp 0.03815 0.05239 0.04659
## R^2 0.97387 0.97231 0.96799
## Adj. R^2 0.97368 0.97210 0.96776
## Num. obs. 816 816 816
## ==========================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05