Calculates the mean directional accuracy function for the forecast and the eventual outcomes.
Syntax
MDA(X, F)
X is the eventual outcome time series sample data (a onedimensional array of cells e.g. row or column).
F is the forecast time series data (a onedimensional array of cells e.g. row or column).
Remarks
 The time series is homogeneous or equally spaced.
 The time series X and F must be of identical size
 The time series X or F may include observations with missing values (e.g. #N/A or blank).
 Observations with missing values in Y or F are excluded from the MDA calculation.
 The MDA compares the forecast direction (upward or downward) to the actual realized direction
 The mean directional accuracy is given by:
$$ \mathrm{MDA} = \frac{1}{N}\sum_t \mathbf{1}_{sign(X_t  X_{t1}) == sign(F_t  X_{t1})} $$
Where:
 $\{X_i\}$ is the actual observations time series.
 $\{F_i\}$ is the estimated or forecast time series.
 $N$ is the number of nonmissing data points.
 $sign(\cdot)$ is sign function
 $\mathbf{1}$ is the indicator function
 In short, MDA provides the probability that the under study forecasting method can detect the correct direction of the time series.
 MDA is a popular metric for forecasting performance in economics and finance.
 The MDA function is available starting with version 1.66 PARSON.
Examples
Example 1:


Formula  Description (Result)  

=MDA($B$3:$B$21,$C$3:$C$21)  MDA (94.44%)  
Files Examples
References
 R.J. Hyndman, A.B. Koehler, "Another look at measures of forecast accuracy", International Journal of Forecasting, 22 (2006), pp. 679688
 James Douglas Hamilton; Time Series Analysis; Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896
 Tsay, Ruey S.; Analysis of Financial Time Series; John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0471690740
 D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906