NumXL is a suite of time series Excel add-ins. It transforms your Microsoft® Excel® application into a first-class time series and econometric tool, offering the kind of statistical accuracy provided by far more expensive statistical packages. NumXL integrates seamlessly with Excel, adding scores of econometric functions, a rich set of shortcuts, and an intuitive user interface to guide you through the entire process.
Whether you have a simple homework problem or a large-scale business project, NumXL simplifies your efforts. It helps you reach your goal in the quickest, most thorough way possible.
NumXL also keeps your data and results connected in Excel, allowing you to trace your calculations, add new data points, update an existing analysis, and share your results with ease.
NumXL allows you to hit the ground running, it does it not require any programming or scripting knowledge and is designed for ease of use: You will not have to move your data between any external programs. You can also perform any ad-hoc analysis, as all of the NumXL functions are accessible in your spreadsheet, and inside the VBA environment, should you choose to write a macro(s).
What can NumXL do for me?
Descriptive statistics is the discipline of quantitatively describing the main features of a collection of data; The main aim is to summarize a data set, rather than use the data to learn about the population that the data are thought to represent.
Some measures that are commonly used to describe a data set:
- Location / central tendency: mean, median, mode, etc.
- Variability or statistical dispersion
- The Shape of the distribution, either via indices such as skewness, kurtosis, quantile, or via tabular and/or graphical format, such as Histogram, EDF, and Kernel density estimate (KDE).
- Statistical dependence: serial/autocorrelation and cross-correlation.
Examples: mean, median, mode, etc.
|EWMA||Exponential-weighted moving volatility|
|MD||Mean absolute difference|
|MAD||Median absolute difference|
|RMD||Relative mean difference|
|LRVar||long-run variance (Bartlett kernel)|
The variable Distribution is depicted via a tabular or a graphical representation.
|EDF||Empirical Distribution Function|
|KDE||Kernel Density Function|
|NxHistogram||Frequency distribution (Histogram)|
- For one data set, serial or auto-correlation functions (e.g. ACF, PACF) are commonly used tools for not only checking randomness, but also in model identification stage for ARMA-type of models.
Function Description ACF Autocorrelation function PACF Partial autocorrelation function Hurst Hurst Exponent
- For two or more variables, cross-correlations are used to measure how those variables are related to one another.
Function Description XCF Cross-correlation function EWXCF Exponentially-weighted cross correlation