Monitor Exchange Rate Regressions

Description

Score-based monitoring of exchange rate regression models (Frankel-Wei models).

Usage

fxmonitor(formula, data, start, end = 3, alpha = 0.05, meat. = NULL)

## S3 method for class 'fxmonitor'
plot(x, which = NULL, aggregate = NULL,
  ylim = NULL, xlab = "Time", ylab = "Empirical fluctuation process",
  main = "Monitoring of FX model", ...)

Arguments

formula a “formula” describing the linear model to be fit (as in fxlm.
data a “zoo” time series (including history and monitoring time period).
start starting time (typically in “Date” format) of the monitoring period.
end end of the monitoring period (in scaled time, i.e., total length divided by length of history period).
alpha significance level of the monitoring procedure.
meat. optionally the meat of an alternative covariance matrix.
x an object of class “fxmonitor” as fitted by fxmonitor.
which name or number of parameter/process to plot.
aggregate logical. Should the multivariate monitoring process be aggregated (using the absolute maximum)? Default is to aggregate for multivariate series.
ylim, xlab, ylab, main, … graphical parameters.

Details

fxmonitor is a function for monitoring exchange rate regression models (also known as Frankel-Wei models). It fits the model on the history period (before start) and computes the predicted scores (or estimating functions) on the monitoring period. The scaled and decorrelated process can be employed for monitoring as described by Zeileis (2005) using a double-maximum-type procedure with linear boundary. The critical values are interpolated from Table III in Zeileis et al. (2005).

Because the model just has to be fitted once (and not updated with every incoming observation), the interface of fxmonitor is much simpler than that of mefp: The data should just include all available observations (including history and monitoring period). Instead of updating the model with each incoming observation, the whole procedure has to be repeated.

The plot method visualizes the monitoring process along with its boundaries. The print method reports the breakdate (time of the first boundary crossing, if any), which can also be queried by the breakpoints and breakdates methods.

Value

An object of class “fxmonitor” which is a list including components:

process the fitted empirical fluctuation process,
n the number of observations in the history period,
formula the formula used,
data the data used,
monitor start of the monitoring period,
end end of monitoring period,
alpha significance level of monitoring procedure,
critval the critical value (for a linear boundary).

References

Zeileis A., Leisch F., Kleiber C., Hornik K. (2005), Monitoring Structural Change in Dynamic Econometric Models, Journal of Applied Econometrics, 20, 99–121.

Zeileis A. (2005), A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals. Econometric Reviews, 24, 445–466.

Shah A., Zeileis A., Patnaik I. (2005), What is the New Chinese Currency Regime?, Report 23, Department of Statistics and Mathematics, Wirtschaftsuniversitaet Wien, Research Report Series, November 2005. http://epub.wu.ac.at.

Zeileis A., Shah A., Patnaik I. (2010), Testing, Monitoring, and Dating Structural Changes in Exchange Rate Regimes, Computational Statistics and Data Analysis, 54(6), 1696–1706. doi:10.1016/j.csda.2009.12.005.

See Also

fxlm, fxregimes

Examples

library("fxregime")

## load package and data
library("fxregime")
data("FXRatesCHF", package = "fxregime")

## compute returns for CNY (and explanatory currencies)
## for one year after abolishing fixed USD regime
cny <- fxreturns("CNY", frequency = "daily",
  start = as.Date("2005-07-25"), end = as.Date("2006-07-24"),
  other = c("USD", "JPY", "EUR", "GBP"))

## monitor CNY regression as in Shah et al. (2005)
mon <- fxmonitor(CNY ~ USD + JPY + EUR + GBP,
  data = cny, start = as.Date("2005-11-01"))
mon
Monitoring of FX model

Formula: CNY ~ USD + JPY + EUR + GBP 
History period: 2005-07-26 to 2005-10-31 
Break detected: 2006-03-27 
## visualization
plot(mon)

plot(mon, aggregate = FALSE)

plot(mon, which = "(Variance)")

## query breakpoint/date
breakpoints(mon)
[1] 167
[1] "2006-03-27"