micEcon (version 0.1-3)

aidsBestA0: Find 'best' Value for Coefficient alpha 0

Description

Search for the intercept of the translog price index ($\alpha_0$) that gives the best fit to the model, i.e. the value that leads to the smallest determinant of the residual covariance matrix (see Michalek and Keyzer, 1992).

Usage

aidsBestA0( pNames, wNames, xtName,
      data = NULL, ivNames = NULL, method = "MK:L",
      a0min = -50, a0max = 50, stoprange = 3, stopiter = 10,
      verbose = FALSE, ... )

Arguments

pNames

a vector of strings containing the names of prices.

wNames

a vector of strings containing the names of expenditure shares.

xtName

a name of the total expenditure variable.

data

a data frame containing the data.

ivNames

a vector of strings containing the names of instrumental variables.

method

the method to estimate the aids (see aidsEst).

a0min

lower limit of the range for searching for $\alpha_0$.

a0max

upper limit of the range for searching for $\alpha_0$.

stoprange

stop searching when the search interval is smaller than or equal to stoprange.

stopiter

maximal number of iterations.

verbose

print each determinant of the residual covariance matrix immediately after its calculation.

...

argunents passed to aidsEst.

Value

• a list containing following objects:
• alpha0
  $\alpha_0$ that gives the best fit.
• allValues
  all $\alpha_0$ tested and the corresponding determinants of the residual covariance matrix.
• iter
  number of iterations.

Details

Since the call of aidsEst takes some time, the search algorithm is constructed to minimize the calls of the function aidsEst.

References

Deaton, A.S. and J. Muellbauer (1980) An Almost Ideal Demand System. American Economic Review, 70, p. 312-326.

Michalek, J. and M. A. Keyzer (1992) Estimation of a two-stage LES-AIDS consumer demand system for eight EC countries. European Review of Agricultural Economics, 19 (2), p. 137-163.

See Also

aidsEst

Examples

data( Blanciforti86 )
   bestA0 <- aidsBestA0( c( "pFood1", "pFood2", "pFood3", "pFood4" ),
      c( "wFood1", "wFood2", "wFood3", "wFood4" ), "xFood",
      data = Blanciforti86, method = "MK:L" ) # may take some time
   bestA0$alpha0
   plot( bestA0$allValues ) # this should be convex