aiDynFit: Fitting a Dynamic AIDS Model

In erer: Empirical Research in Economics with R

Fitting a Dynamic AIDS Model

Description

Estimate a dynamic AIDS model for a system.

Usage

aiDynFit(w, dum.dif = FALSE, AR1 = FALSE,
  rho.sel = c("all", "mean"), ...)

Arguments

w	a object of class aiStaFit.
dum.dif	a logical value (default of FALSE) of whether to take a difference on the dummy variables passed from w.
AR1	whether first-degree autocorrelation should be corrected.
rho.sel	if AR1 = TRUE, there are two ways of computing the autocorrelation coefficient.
...	additional arguments to be passed.

Details

This estimates a dynamic AIDS model. The residuals from the statis AIDS model are included. As it is programmed now, only one lag is allowed for the share variables on the right-hand side. Autocorrelation in the residuals can be corrected following the treatment in Berndt (1975).

Value

Return a list object of class "aiFit" and "aiDynFit" with the following components:

w	a object of class aiStaFit.
y	data for fitting the static AIDS model, passed down by w.
dum.dif	a logical value (default of FALSE) of whether to take a difference on the dummy variables passed from w.
daDyn	data for fitting the dynamic AIDS model.
share	names of shares by commodity, used as depedent variables.
price	names of prices by commodity, used as independent variables.
expen	names of expenditure variable.
shift	names of the shifters.
omit	names of the omitted share variable.
nOmit	position of the omitted share variable in the name of share variable.
hom	a logical value of homogeneity test.
sym	a logical value of symmetry test.
nShare	number of share variables.
nExoge	number of exogenous variables (lagged share, residual, expenditure, and shifters).
nParam	number of parameters in one equation.
nTotal	number of parameters in the whole system estimated.
formula	formula for estimating the system.
res.matrix	restriction matrix for hom or sym, or both.
res.rhs	right-hand values for tests of hom or sym, or both.
est	the dynamic AIDS model estimated.

Methods

One method is defined as follows:

print:

print the first several observations of the final data.

Author(s)

Changyou Sun ([e-mail])

References

Berndt, E.R., and N.E. Savin. 1975. Estimation and hypothesis testing in singular equation systems with autoregressive disturbances. Econometrica 43(5/6):937-957.

Wan, Y., C. Sun, and D.L. Grebner. 2010. Analysis of import demand for wooden beds in the United States. Journal of Agricultural and Applied Economics 42(4):643-658.

See Also

systemfitAR; aiStaFit; aiDiag; aiElas; summary.aiFit.

Examples

# --- Step 1: Read data
data(daExp, daBedRaw, daBed)

# --- Step 2: Hausman Test 
# 2.1 Getting started with a static AIDS model 
sh  <- c("sCN", "sVN", "sID", "sMY", "sCA", "sBR", "sIT", "sRW")
pr  <- c("lnpCN", "lnpVN", "lnpID", "lnpMY",  
         "lnpCA", "lnpBR", "lnpIT", "lnpRW")
du3 <- c("dum1","dum2","dum3")
rSta <- aiStaFit(y = daBed, share = sh, price = pr, shift = du3, 
  expen = "rte", omit = "sRW", hom = TRUE, sym = TRUE)
summary(rSta)

# The following steps should work. It can take a few minutes.

# 2.2 The final Hausman test and new data
(dg <- daExp[, "dg"])
rHau <- aiStaHau(x = rSta, instr = dg, choice = FALSE)
names(rHau); colnames(rHau$daHau); colnames(rHau$daFit); rHau
two.exp <- rHau$daFit[, c("rte", "rte.fit")] 
bsStat(two.exp, digits = 4)
plot(data.frame(two.exp)); abline(a = 0, b = 1)
daBedFit <- rHau$daFit

# --- Step 3: Static and dynamic AIDS models 
# 3.1 Diagnostics and coefficients
hSta  <- update(rSta, y = daBedFit, expen = "rte.fit")
hSta2 <- update(hSta, hom = FALSE, sym = FALSE) 
hSta3 <- update(hSta, hom = FALSE, sym = TRUE)
hSta4 <- update(hSta, hom = TRUE,  sym = FALSE)
lrtest(hSta2$est, hSta$est) 
lrtest(hSta2$est, hSta3$est)
lrtest(hSta2$est, hSta4$est)

hDyn  <- aiDynFit(hSta)
hDyn2 <- aiDynFit(hSta2); lrtest(hDyn2$est, hDyn$est)
hDyn3 <- aiDynFit(hSta3); lrtest(hDyn2$est, hDyn3$est)
hDyn4 <- aiDynFit(hSta4); lrtest(hDyn2$est, hDyn4$est)

(table.2 <- rbind(aiDiag(hSta), aiDiag(hDyn)))
(table.3 <- summary(hSta))
(table.4 <- summary(hDyn))
                          
# 3.2 Elasticity calculation
es <- aiElas(hSta); esm <- es$marsh
ed <- aiElas(hDyn); edm <- ed$marsh
esm2 <- data.frame(c(esm[1:2, 2], esm[3:4, 3], 
  esm[5:6, 4], esm[7:8, 5], esm[9:10, 6], esm[11:12, 7], 
  esm[13:14, 8], esm[15:16, 9]))
edm2 <- data.frame(c(edm[1:2, 2], edm[3:4, 3], 
  edm[5:6, 4], edm[7:8, 5], edm[9:10, 6], edm[11:12, 7], 
  edm[13:14, 8], edm[15:16, 9]))
eEM <- cbind(es$expen, esm2, ed$expen[2], edm2) 
colnames(eEM) <- c("Country", "LR.expen", "LR.Marsh", 
  "SR.expen", "SR.Marsh")
(table.5 <- eEM[-c(15:16),])
(table.6a <- es$hicks[-c(15:16), -9])
(table.6b <- ed$hicks[-c(15:16), -9])

erer documentation built on Sept. 26, 2024, 5:06 p.m.