estTSF: Estimate Time Series Factor Model
In Xtratsfa: Time Series Factor Analysis Extra Functions

Description Usage Arguments Details Value Author(s) References See Also Examples

Estimate a TSFmodel .

    estTSF.R2M(y, p, diff.=TRUE, 
                rotation=if(p==1) "none" else "quartimin", 
		rotationArgs=NULL, 
		normalize=TRUE, eps=1e-5, maxit=1000, Tmat=diag(p),
		BpermuteTarget=NULL,
                factorNames=paste("factor", seq(p)))
    estTSF.MCV(y, p, diff.=TRUE,
                rotation=if(p==1) "none" else "oblimin", 
		rotationArgs=NULL,  
		normalize=TRUE, eps=1e-5, maxit=1000, Tmat=diag(p),
		BpermuteTarget=NULL,
                factorNames=paste("factor", seq(p)))

`y`	a time series matrix.
`p`	integer indication number of factors to estimate.
`diff.`	logical indicating if model should be estimated with differenced data.
`rotation`	character vector indicating the factor rotation method (see GPArotation for options).
`rotationArgs`	list passed to GPFoblq, and then to the rotation method, specifying arguments for the rotation criteria. See GPFoblq.
`normalize`	Passed to GPFoblq. TRUE means do Kaiser normalization before rotation and then undo it after completing rotatation. FALSE means do no normalization. See GPFoblq for other possibilities.
`eps`	passed to GPFoblq
`maxit`	passed to GPFoblq
`Tmat`	passed to GPFoblq
`BpermuteTarget`	matrix of loadings. If supplied, this is used to permute the order of estimated factors and change signs in order to compare properly.
`factorNames`	vector of strings indicating names to be given to factor series.

The function estTSF.R2M estimates parameters using raw second moments. THIS ALL NEEDS TO BE CHECKED. The function factanal with no rotation is used to find the initial (orthogonal) solution. Rotation, if specified, is then done with GPFoblq. factanal always uses the correlation matrix, so standardizing does not affect the solution.

If diff. is TRUE (the default) the indicator data is differenced before it is passed to factanal. This is necessary if the data is not stationary. The resulting Bartlett predictor (rotated) is applied to the undifferenced data. See Gilbert and Meijer (2005) for a discussion of this approach.

If rotation is "none" the result of the factanal estimation is not rotated. In this case, to avoid confusion with a rotated solution, the factor covariance matrix Phi is returned as NULL. Another possibility for its value would be the identity matrix, but this is not calculated so NULL avoids confusion.

The arguments rotation, methodArgs, normalize, eps, maxit, and Tmat are passed to GPFoblq.

The estimated loadings, Bartlett predictor and predicted factor scores are put in a TSFmodel which is part of the returned object. The Bartlett predictor can be calculated as

(B' Omega exp(-1) B) exp(-1) B' Omega exp(-1) x

,

or equivalently as

(B' Sigma exp(-1) B) exp(-1) B' Sigma exp(-1) x

,

The first is simpler because Omega is diagonal, but breaks down with a Heywood case, because Omega is then singular (one or more of its diagonal elements are zero). The second only requires nonsingularity of Sigma. Typically, Sigma is not singular even if Omega is singular. Sigma is calculated from B Phi B' + Omega, where B, Phi, and Omega are the estimated values returned from factanal and rotated. The data covariance could also be used for Sigma. (It returns the same result with this estimation method.)

The returned TSFestModel object is a list containing

model: the estimated TSFmodel.
data: the indicator data used in the estimation.
estimates: a list of
estimation: a character string indicating the name of the estimation function.
diff.: the setting of the argument diff..
rotation: the setting of the argument rotation.
uniquenesses: the estimated uniquenesses.
BpermuteTarget: the setting of the argument BpermuteTarget.

A TSFestModel object which is a list containing TSFmodel, the data, and some information about the estimation.

Paul Gilbert and Erik Meijer

Gilbert, Paul D. and Meijer, Erik (2005) Time Series Factor Analaysis with an Application to Measuring Money. Research Report 05F10, University of Groningen, SOM Research School. Available from http://som.eldoc.ub.rug.nl/reports/themeF/2005/05F10/.

Gilbert, Paul D. and Meijer, Erik (2006) Money and Credit Factors. Bank of Canada Working Paper 2006-3, available at http://www.bankofcanada.ca/2006/03/publications/research/working-paper-2006-3/.

Tom Wansbeek and Erik Meijer (2000) Measurement Error and Latent Variables in Econometrics, Amsterdam: North-Holland.

TSFmodel, estTSF.ML, GPFoblq, rotations, factanal

data("CanadianMoneyData.asof.28Jan2005", package="CDNmoney")
data("CanadianCreditData.asof.28Jan2005", package="CDNmoney")

cpi <- 100 * M1total / M1real
seriesNames(cpi) <- "CPI"
popm <- M1total / M1PerCapita
seriesNames(popm) <- "Population of Canada"

z <- tframed(tbind(
    MB2001,
    MB486 + MB452 + MB453 ,
    NonbankCheq,
    MB472 + MB473 + MB487p,
    MB475,
    NonbankNonCheq + MB454 + NonbankTerm + MB2046 + MB2047 + MB2048 +
    MB2057 + MB2058 + MB482),
    names=c("currency", "personal cheq.", "NonbankCheq",
    "N-P demand & notice", "N-P term", "Investment" )
  )


TotalMoney <- tframed(rowSums(z), tframe(z))

z <- tbind (z, ConsumerCredit, ResidentialMortgage,
    ShortTermBusinessCredit, OtherBusinessCredit)

z <-tfwindow(z, start=c(1981,11), end=c(2004,11))
scale <- tfwindow(1e8 /(popm * cpi), tf=tframe(z))

MBandCredit <- sweep(z, 1, scale, "*")
c4withR2M  <- estTSF.R2M(MBandCredit, 4)
tfplot(ytoypc(factors(c4withR2M)),
       Title="Factors from 4 factor model (year-to-year growth rate)")
tfplot(c4withR2M, graphs.per.page=3)
summary(c4withR2M)