aiDynFit: Fitting a Dynamic AIDS Model

Description

Estimate a dynamic AIDS model for a system.

Usage

aiDynFit(w, dum.dif = FALSE, ...)

Arguments

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.

...

additional arguments to be passed.

Value

Return a list object of class "aiFit" and "aiDynFit" with the following components:
wa object of class aiStaFit.
ydata for fitting the static AIDS model, passed down by w.
dum.difa logical value (default of FALSE) of whether to take a difference on the dummy variables passed from w.
daDyndata for fitting the dynamic AIDS model.
sharenames of shares by commodity, used as depedent variables.
pricenames of prices by commodity, used as independent variables.
expennames of expenditure variable.
shiftnames of the shifters.
omitnames of the omitted share variable.
nOmitposition of the omitted share variable in the name of share variable.
homa logical value of homogeneity test.
syma logical value of symmetry test.
nSharenumber of share variables.
nExogenumber of exogenous variables (lagged share, residual, expenditure, and shifters).
nParamnumber of parameters in one equation.
nTotalnumber of parameters in the whole system estimated.
formulaformula for estimating the system.
res.matrixrestriction matrix for hom or sym, or both.
res.rhsright-hand values for tests of hom or sym, or both.
estthe dynamic AIDS model estimated.

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.

References

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.

Examples

Run this code

# --- 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)

# 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])

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