TempDisaggDGP function

High and Low-Frequency Data Generating Processes

This function generates the high-frequency $n \times 1$ response vector $y$, according to $y=X\beta+\epsilon$, where $X$ is an $n\times p$ matrix of indicator series, and the $p\times 1$ coefficient vector may be sparse. The low-frequency $n_l\times 1$ vector $Y$

can be generated by pre-multiplying an aggregation matrix $n_l\times n$ matrix, such that the sum, the average, the last or the first value of $y$ equates the corresponding $Y$ observation. The parameter aggRatio is the specified aggregation ratio between the low and high frequency series, e.g. aggRatio = 4 for annual-to-quarterly and aggRatio = 3 for quarterly-to-monthly. If $n > aggRatio \times n_l$, then the last $n - aggRatio \times n_l$ columns of the aggregation matrix are 0 such that $Y$ is only observed up to $n_l$. For a comprehensive review, see if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_citeOnly(keys="dagum2006benchmarking;textual",package="TSdisaggregation",cached_env=.Rdpack.currefs) .

TempDisaggDGP(
  n_l,
  n,
  aggRatio = 4,
  p = 1,
  beta = 1,
  sparsity = 1,
  method = "Chow-Lin",
  aggMat = "sum",
  rho = 0,
  mean_X = 0,
  sd_X = 1,
  sd_e = 1,
  simul = FALSE,
  setSeed = 42
)

Arguments

• n_l: Size of the low frequency series.
• n: Size of the high frequency series.
• aggRatio: aggregation ratio (default is 4)
• p: The number of high-frequency indicator series to include.
• beta: The positive and negative beta elements for the coefficient vector.
• sparsity: Sparsity percentage of the coefficient vector.
• method: DGP of residuals, either 'Denton', 'Denton-Cholette', 'Chow-Lin', 'Fernandez', 'Litterman'.
• aggMat: Aggregation matrix according to 'first', 'sum', 'average', 'last'.
• rho: The residual autocorrelation coefficient. Default is 0.
• mean_X: Mean of the design matrix. Default is 0.
• sd_X: Standard deviation of the design matrix. Default is 1.
• sd_e: Standard deviation of the errors. Default is 1.
• simul: When 'TRUE' the design matrix and the coefficient vector are fixed.
• setSeed: The seed used when 'simul' is set to 'TRUE'.

Returns

y_Gen Generated high-frequency response series.

Y_Gen Generated low-frequency response series.

X_Gen Generated high-frequency indicator series.

Beta_Gen Generated coefficient vector.

e_Gen Generated high-frequency residual series.

Examples

data = TempDisaggDGP(n_l=25, n=100, aggRatio=4,p=10, rho=0.5)
X = data$X_Gen
Y = data$Y_Gen

References

if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_all_ref(.Rdpack.currefs)