PCR_FORE - Forecasting with PCR Model

Calculates the model's estimated values, standard errors, and related statistics.


PCR_FORE(X, Mask, Y, Intercept, Target, Return_type, Alpha)
is the independent variables data matrix, so each column represents one variable.
is the boolean array to choose the explanatory variables in the model. If missing, all variables in X are included.
is the response or the dependent variable data array (a one-dimensional array of cells (e.g., rows or columns)).
is the constant or the intercept value to fix (e.g., zero). If missing, an intercept will not be fixed and is usually computed.
is the value of the explanatory variables (a one-dimensional array of cells (e.g., rows or columns)).
is a switch to select the return output (1 = forecast (default), 2 = error, 3 = upper limit, 4 = lower limit).
Method Description
1 Mean value
2 Standard error
3 Upper limit
4 Lower limit
is the statistical significance of the test (i.e. alpha). If missing or omitted, an alpha value of 5% is assumed.


  1. The underlying model is described here.
  2. The sample data may include data points with missing values.
  3. Each column in the input matrix corresponds to a separate variable.
  4. Each row in the input matrix corresponds to an observation.
  5. Observations (i.e., rows) with missing values in X or Y are removed.
  6. The number of rows of the response variable (Y) must equal the number of rows of the explanatory variable (X).
  7. The PCR_FORE function is available starting with version 1.60 APACHE.

Files Examples

Related Links


  • Hamilton, J .D.; Time Series Analysis, Princeton University Press (1994), ISBN 0-691-04289-6
  • Kenney, J. F. and Keeping, E. S. (1962) "Linear Regression and Correlation." Ch. 15 in Mathematics of Statistics, Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand, pp. 252-285


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