ForeignTrade: Foreign Trade of Developing Countries
In pder: Panel Data Econometrics with R
Description
Usage
Format
Source
References
Examples
Description
yearly observations of 31 countries from 1963 to 1986
number of observations : 744
number of time-series : 24
country : developing countries
package : panelivreg
JEL codes: O19, C51, F17
Chapter : 02, 06
Usage
1
Format
A dataframe containing:
- country
country name
- year
year
- exports
nominal exports deflated by the unit value of exports per capita
- imports
nominal imports deflated by the unit value of exports per capita
- resimp
official foreing reserves (in US dollars) divided by nominal imports (in US dollars)
- gnp
real GNP per capita
- pgnp
trend real GNP per capita calculated by fitting linear trend yit*=y0iexp(gi t), where y0i is the initial value of real gnp per capita for country i and gi is the ith country's average growth rate over 1964-1986
- gnpw
real genp for USA per capita
- pm
unit value of imports (in US dollars), 1980 = 100
- px
unit value of exports (in US dollars), 1980 = 100
- cpi
domestic CPI, 1980 = 100
- pw
US producer's price index, 1980 = 100
- exrate
exchange rate (price of US dollars in local currency), 1980 = 1
- consump
domestic consumption per capita,
- invest
domestic fixed gross investment per capita
- income
domestic disposable income per capita
- pop
population
- reserves
official foreing reserves (in US dollars)
- money
domestic money supply per capita
- trend
trend dummy, 1964 = 1
- pwcpi
log of us producer price index divided by domestic cpi
- importspmpx
log of nominal imports divided by export prices
- pmcpi
log of imports price divided by domestic cpi
- pxpw
log of exports price divided by domestic cpi
Source
Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/
References
Kinal, T. and K. Lahiri (1993) “On the Estimation of Simultaneous-equations Error-components Models with An Application to a Model of Developing Country Foreign Trade”, Journal of Applied Economics, 8, 81-92, doi: 10.1002/jae.3950080107
.
Examples
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#### Example 2-4
## ------------------------------------------------------------------------
library("plm")
data("ForeignTrade", package = "pder")
FT <- pdata.frame(ForeignTrade)
summary(FT$gnp)
ercomp(imports ~ gnp, FT)
models <- c("within", "random", "pooling", "between")
sapply(models, function(x) coef(plm(imports ~ gnp, FT, model = x))["gnp"])
#### Example 6-2
## ------------------------------------------------------------------------
data("ForeignTrade", package = "pder")
w1 <- plm(imports~pmcpi + gnp + lag(imports) + lag(resimp) |
lag(consump) + lag(cpi) + lag(income) + lag(gnp) + pm +
lag(invest) + lag(money) + gnpw + pw + lag(reserves) +
lag(exports) + trend + pgnp + lag(px),
ForeignTrade, model = "within")
r1 <- update(w1, model = "random", random.method = "nerlove",
random.dfcor = c(1, 1), inst.method = "baltagi")
## ------------------------------------------------------------------------
phtest(r1, w1)
## ------------------------------------------------------------------------
r1b <- plm(imports ~ pmcpi + gnp + lag(imports) + lag(resimp) |
lag(consump) + lag(cpi) + lag(income) + lag(px) +
lag(reserves) + lag(exports) | lag(gnp) + pm +
lag(invest) + lag(money) + gnpw + pw + trend + pgnp,
ForeignTrade, model = "random", inst.method = "baltagi",
random.method = "nerlove", random.dfcor = c(1, 1))
phtest(w1, r1b)
## ------------------------------------------------------------------------
rbind(within = coef(w1), ec2sls = coef(r1b)[-1])
## ------------------------------------------------------------------------
elast <- sapply(list(w1, r1, r1b),
function(x) c(coef(x)["pmcpi"],
coef(x)["pmcpi"] / (1 - coef(x)["lag(imports)"])))
dimnames(elast) <- list(c("ST", "LT"), c("w1", "r1", "r1b"))
elast
## ------------------------------------------------------------------------
rbind(within = coef(summary(w1))[, 2],
ec2sls = coef(summary(r1b))[-1, 2])
#### Example 6-4
## ------------------------------------------------------------------------
eqimp <- imports ~ pmcpi + gnp + lag(imports) +
lag(resimp) | lag(consump) + lag(cpi) + lag(income) +
lag(px) + lag(reserves) + lag(exports) | lag(gnp) + pm +
lag(invest) + lag(money) + gnpw + pw + trend + pgnp
eqexp <- exports ~ pxpw + gnpw + lag(exports) |
lag(gnp) + pw + lag(consump) + pm + lag(px) + lag(cpi) |
lag(money) + gnpw + pgnp + pop + lag(invest) +
lag(income) + lag(reserves) + exrate
r12 <- plm(list(import.demand = eqimp,
export.demand = eqexp),
data = ForeignTrade, index = 31, model = "random",
inst.method = "baltagi", random.method = "nerlove",
random.dfcor = c(1, 1))
summary(r12)
## ------------------------------------------------------------------------
rbind(ec2sls = coef(summary(r1b))[-1, 2],
ec3sls = coef(summary(r12), "import.demand")[-1, 2])
Example output
Loading required package: Formula
total sum of squares: 4110.659
id time
0.98248044 0.00763845
var std.dev share
idiosyncratic 0.08634 0.29383 0.074
individual 1.07785 1.03820 0.926
theta: 0.9423
within.gnp random.gnp pooling.gnp between.gnp
0.90236420 0.76815599 0.06366400 0.04870833
Hausman Test
data: imports ~ pmcpi + gnp + lag(imports) + lag(resimp) | lag(consump) + ...
chisq = 10.629, df = 4, p-value = 0.03106
alternative hypothesis: one model is inconsistent
Hausman Test
data: imports ~ pmcpi + gnp + lag(imports) + lag(resimp) | lag(consump) + ...
chisq = 7.1486, df = 4, p-value = 0.1282
alternative hypothesis: one model is inconsistent
pmcpi gnp lag(imports) lag(resimp)
within -0.05873374 0.02890065 0.9512149 0.05215182
ec2sls -0.05419773 0.01361175 0.9482115 0.04195281
w1 r1 r1b
ST -0.05873374 -0.05519734 -0.05419773
LT -1.20392829 -1.19529901 -1.04651970
pmcpi gnp lag(imports) lag(resimp)
within 0.02915262 0.041235082 0.03066695 0.008257449
ec2sls 0.02180217 0.006998615 0.01288882 0.006708722
Oneway (individual) effect Random Effect Model
(Nerlove's transformation)
Call:
plm.list(formula = list(import.demand = eqimp, export.demand = eqexp),
data = ForeignTrade, model = "random", random.method = "nerlove",
inst.method = "baltagi", index = 31, ... = pairlist(random.dfcor = c(1,
1)))
Balanced Panel: n = 31, T = 24, N = 744
Effects:
Estimated standard deviations of the error
import.demand export.demand
id 0.061871 0.078159
idios 0.143934 0.120007
Estimated correlation matrix of the individual effects
import.demand export.demand
import.demand 1.000 .
export.demand 0.138 1
Estimated correlation matrix of the idiosyncratic effects
import.demand export.demand
import.demand 1.000000 .
export.demand 0.097493 1
- import.demand
Estimate Std. Error t-value Pr(>|t|)
(Intercept) 0.3987408 0.1189935 3.3509 0.0008266 ***
pmcpi -0.0540741 0.0217007 -2.4918 0.0128231 *
gnp 0.0110320 0.0053078 2.0784 0.0378497 *
lag(imports) 0.9504604 0.0118726 80.0549 < 2.2e-16 ***
lag(resimp) 0.0394789 0.0063424 6.2246 6.347e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- export.demand
Estimate Std. Error t-value Pr(>|t|)
(Intercept) 0.143674 0.139492 1.0300 0.303195
pxpw -0.061469 0.019467 -3.1576 0.001624 **
gnpw 0.114402 0.053359 2.1440 0.032201 *
lag(exports) 0.946533 0.013310 71.1143 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pmcpi gnp lag(imports) lag(resimp) (Intercept) pxpw
ec2sls 0.02180217 0.006998615 0.01288882 0.006708722 0.02180217 0.006998615
ec3sls 0.02170074 0.005307839 0.01187260 0.006342416 0.13949165 0.019466793
gnpw lag(exports)
ec2sls 0.01288882 0.006708722
ec3sls 0.05335866 0.013310021
pder documentation built on Jan. 27, 2022, 1:12 a.m.
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Description Usage Format Source References Examples
Description
yearly observations of 31 countries from 1963 to 1986
number of observations : 744
number of time-series : 24
country : developing countries
package : panelivreg
JEL codes: O19, C51, F17
Chapter : 02, 06
Usage
1 |
Format
A dataframe containing:
- country
country name
- year
year
- exports
nominal exports deflated by the unit value of exports per capita
- imports
nominal imports deflated by the unit value of exports per capita
- resimp
official foreing reserves (in US dollars) divided by nominal imports (in US dollars)
- gnp
real GNP per capita
- pgnp
trend real GNP per capita calculated by fitting linear trend yit*=y0iexp(gi t), where y0i is the initial value of real gnp per capita for country i and gi is the ith country's average growth rate over 1964-1986
- gnpw
real genp for USA per capita
- pm
unit value of imports (in US dollars), 1980 = 100
- px
unit value of exports (in US dollars), 1980 = 100
- cpi
domestic CPI, 1980 = 100
- pw
US producer's price index, 1980 = 100
- exrate
exchange rate (price of US dollars in local currency), 1980 = 1
- consump
domestic consumption per capita,
- invest
domestic fixed gross investment per capita
- income
domestic disposable income per capita
- pop
population
- reserves
official foreing reserves (in US dollars)
- money
domestic money supply per capita
- trend
trend dummy, 1964 = 1
- pwcpi
log of us producer price index divided by domestic cpi
- importspmpx
log of nominal imports divided by export prices
- pmcpi
log of imports price divided by domestic cpi
- pxpw
log of exports price divided by domestic cpi
Source
Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/
References
Kinal, T. and K. Lahiri (1993) “On the Estimation of Simultaneous-equations Error-components Models with An Application to a Model of Developing Country Foreign Trade”, Journal of Applied Economics, 8, 81-92, doi: 10.1002/jae.3950080107 .
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | #### Example 2-4
## ------------------------------------------------------------------------
library("plm")
data("ForeignTrade", package = "pder")
FT <- pdata.frame(ForeignTrade)
summary(FT$gnp)
ercomp(imports ~ gnp, FT)
models <- c("within", "random", "pooling", "between")
sapply(models, function(x) coef(plm(imports ~ gnp, FT, model = x))["gnp"])
#### Example 6-2
## ------------------------------------------------------------------------
data("ForeignTrade", package = "pder")
w1 <- plm(imports~pmcpi + gnp + lag(imports) + lag(resimp) |
lag(consump) + lag(cpi) + lag(income) + lag(gnp) + pm +
lag(invest) + lag(money) + gnpw + pw + lag(reserves) +
lag(exports) + trend + pgnp + lag(px),
ForeignTrade, model = "within")
r1 <- update(w1, model = "random", random.method = "nerlove",
random.dfcor = c(1, 1), inst.method = "baltagi")
## ------------------------------------------------------------------------
phtest(r1, w1)
## ------------------------------------------------------------------------
r1b <- plm(imports ~ pmcpi + gnp + lag(imports) + lag(resimp) |
lag(consump) + lag(cpi) + lag(income) + lag(px) +
lag(reserves) + lag(exports) | lag(gnp) + pm +
lag(invest) + lag(money) + gnpw + pw + trend + pgnp,
ForeignTrade, model = "random", inst.method = "baltagi",
random.method = "nerlove", random.dfcor = c(1, 1))
phtest(w1, r1b)
## ------------------------------------------------------------------------
rbind(within = coef(w1), ec2sls = coef(r1b)[-1])
## ------------------------------------------------------------------------
elast <- sapply(list(w1, r1, r1b),
function(x) c(coef(x)["pmcpi"],
coef(x)["pmcpi"] / (1 - coef(x)["lag(imports)"])))
dimnames(elast) <- list(c("ST", "LT"), c("w1", "r1", "r1b"))
elast
## ------------------------------------------------------------------------
rbind(within = coef(summary(w1))[, 2],
ec2sls = coef(summary(r1b))[-1, 2])
#### Example 6-4
## ------------------------------------------------------------------------
eqimp <- imports ~ pmcpi + gnp + lag(imports) +
lag(resimp) | lag(consump) + lag(cpi) + lag(income) +
lag(px) + lag(reserves) + lag(exports) | lag(gnp) + pm +
lag(invest) + lag(money) + gnpw + pw + trend + pgnp
eqexp <- exports ~ pxpw + gnpw + lag(exports) |
lag(gnp) + pw + lag(consump) + pm + lag(px) + lag(cpi) |
lag(money) + gnpw + pgnp + pop + lag(invest) +
lag(income) + lag(reserves) + exrate
r12 <- plm(list(import.demand = eqimp,
export.demand = eqexp),
data = ForeignTrade, index = 31, model = "random",
inst.method = "baltagi", random.method = "nerlove",
random.dfcor = c(1, 1))
summary(r12)
## ------------------------------------------------------------------------
rbind(ec2sls = coef(summary(r1b))[-1, 2],
ec3sls = coef(summary(r12), "import.demand")[-1, 2])
|
Example output
Loading required package: Formula
total sum of squares: 4110.659
id time
0.98248044 0.00763845
var std.dev share
idiosyncratic 0.08634 0.29383 0.074
individual 1.07785 1.03820 0.926
theta: 0.9423
within.gnp random.gnp pooling.gnp between.gnp
0.90236420 0.76815599 0.06366400 0.04870833
Hausman Test
data: imports ~ pmcpi + gnp + lag(imports) + lag(resimp) | lag(consump) + ...
chisq = 10.629, df = 4, p-value = 0.03106
alternative hypothesis: one model is inconsistent
Hausman Test
data: imports ~ pmcpi + gnp + lag(imports) + lag(resimp) | lag(consump) + ...
chisq = 7.1486, df = 4, p-value = 0.1282
alternative hypothesis: one model is inconsistent
pmcpi gnp lag(imports) lag(resimp)
within -0.05873374 0.02890065 0.9512149 0.05215182
ec2sls -0.05419773 0.01361175 0.9482115 0.04195281
w1 r1 r1b
ST -0.05873374 -0.05519734 -0.05419773
LT -1.20392829 -1.19529901 -1.04651970
pmcpi gnp lag(imports) lag(resimp)
within 0.02915262 0.041235082 0.03066695 0.008257449
ec2sls 0.02180217 0.006998615 0.01288882 0.006708722
Oneway (individual) effect Random Effect Model
(Nerlove's transformation)
Call:
plm.list(formula = list(import.demand = eqimp, export.demand = eqexp),
data = ForeignTrade, model = "random", random.method = "nerlove",
inst.method = "baltagi", index = 31, ... = pairlist(random.dfcor = c(1,
1)))
Balanced Panel: n = 31, T = 24, N = 744
Effects:
Estimated standard deviations of the error
import.demand export.demand
id 0.061871 0.078159
idios 0.143934 0.120007
Estimated correlation matrix of the individual effects
import.demand export.demand
import.demand 1.000 .
export.demand 0.138 1
Estimated correlation matrix of the idiosyncratic effects
import.demand export.demand
import.demand 1.000000 .
export.demand 0.097493 1
- import.demand
Estimate Std. Error t-value Pr(>|t|)
(Intercept) 0.3987408 0.1189935 3.3509 0.0008266 ***
pmcpi -0.0540741 0.0217007 -2.4918 0.0128231 *
gnp 0.0110320 0.0053078 2.0784 0.0378497 *
lag(imports) 0.9504604 0.0118726 80.0549 < 2.2e-16 ***
lag(resimp) 0.0394789 0.0063424 6.2246 6.347e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- export.demand
Estimate Std. Error t-value Pr(>|t|)
(Intercept) 0.143674 0.139492 1.0300 0.303195
pxpw -0.061469 0.019467 -3.1576 0.001624 **
gnpw 0.114402 0.053359 2.1440 0.032201 *
lag(exports) 0.946533 0.013310 71.1143 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pmcpi gnp lag(imports) lag(resimp) (Intercept) pxpw
ec2sls 0.02180217 0.006998615 0.01288882 0.006708722 0.02180217 0.006998615
ec3sls 0.02170074 0.005307839 0.01187260 0.006342416 0.13949165 0.019466793
gnpw lag(exports)
ec2sls 0.01288882 0.006708722
ec3sls 0.05335866 0.013310021
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