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**N**umXL 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).

Summary Statistics

Tutorials

Screenshots

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.

Function | Description |
---|---|

EWMA | Exponential-weighted moving volatility |

GINI | Gini Coefficient |

Quantile | Sample Quantile |

IQR | Inter-quartile Range |

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.

Function | Description |
---|---|

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.
**Featured Functions**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.
**Featured Functions**Function Description XCF Cross-correlation function EWXCF Exponentially-weighted cross correlation

Statistical testing

Tutorials

Screenshots

Statistical/Hypothesis testing is a common method of drawing inferences about a population based on statistical evidence from a sample. For instance, statistical testing techniques are often applied for examining the significance (e.g. difference than zero) of a calculated parameter (e.g. mean, skew, kurtosis), or for verifying an assumption (e.g. Normality, white-noise, stationary, cointegration, multi-collinearity, etc.) using finite sample data.

In NumXL, the statistical testing functions can be broken into the following categories:

This category describes a suite of one-sample tests that compare the mean (or another location measure) of one sample to a known standard (known from theory or are calculated from the population).
#### Featured Functions:

Function | Description |
---|---|

TEST_MEAN | One-sample population mean Test |

These are also a one-sample test that compares the variance (or another statistical dispersion measure) of one sample to a known standard (known from theory or are calculated from the population).

Function | Description |
---|---|

TEST_STDEV | Standard Deviation Test |

TEST_SKEW | Skewness Test |

TEST_XKURT | Excess Kurtosis Test |

The statistical tests are one-sample tests that examine an assumption about the distribution of the random variable (e.g. Normality).

Function | Description |
---|---|

NormalityTest | Testing for Normal/Gaussian Distribution |

In this category, we group the functionality based on the number of variables they apply to:

- One variable – Serial (aka. Auto) correlation
**Featured Functions:**Function Description ACFTest Autocorrelation Test WNTest White-Noise Test ARCHTest Test of ARCH Effect ADFTest Augmented Dickey-Fuller Stationary Test - Two or more variables – cross-correlation
**Featured Functions**:Function Description XCFTest Cross-Correlation Test JohansenTest Johansen Cointegration Test Chow Test Regression Stability Test CollinearityTest Test for the Presence of Multi-colinearity

Data Fitting

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

The construction of the curve may involve interpolation, where an exact fit to the data is required, or smoothing, in which the function is constructed that approximately fits the data.

Discover a function that is an exact fit to the data points (assumes no measurement error). NumXL supports One and two-dimensional interpolation.

Function | Description |
---|---|

NxINTRPL | Linear, polynomial, cubic spline Interpolation and Extrapolation |

NxINTRPL2D | 2-Dimension-interpolation-extrapolation |

Find a curve-function that is an approximate fit to the data points: we permit the actual data points to be near, but not necessarily on the curve; while minimizing the overall error.

Function | Description |
---|---|

NxRegress | Simple Linear Regression Function |

NxKNN | K Nearest neighbors (K-NN ) Regression |

NxKREG | Kernel Regression |

NxLOCREG | Local or moving polynomial regression |

Transform

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

Variable transformation is a common preliminary step in real-world analysis to get a more representative variable for the analysis.

In some cases, you’d use a transform (e.g., Box-Cox) function, so the distribution of the values of your variable is closer to a normal distribution. In other cases, you’d use another transform (e.g., log) function to distribute the values of your variable better, hence avoiding any biases in your model.

Function | Description |
---|---|

DIFF | Time Series Difference Operator |

LAG | Time Series Lag or Backshift Operator |

INTG | Time Series Integration Operator |

Function | Description |
---|---|

BoxCox | Box-Cox Transform |

CLOGLOG | Complementary Log-Log Transform |

LOGIT | Logit Transform |

PROBIT | Probit Transform |

DETREND | Remove Deterministic Trend From Time Series |

Function | Description |
---|---|

SUBNA | Missing values interpolation |

Function | Description |
---|---|

RESAMPLE | Time series resampling |

Statistical Sampling

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

Function | Description |
---|---|

NxSort | Sort input array |

NxReverse | Reverse Chronicle Order of Time Series |

NxShuffle | Shuffle the order of elements in data set |

Function | Description |
---|---|

NxSubset | Time Series Subset |

NxChoose | Random sampling |

Smoothing

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

one-side, centered, single, double or weighted-moving average
#### Featured Functions:

Function | Description |
---|---|

NxEMA | exponentially-weighted moving (rolling/running) average |

NxMA | Moving (rolling) average using prior data points |

WMA | Weighted-Moving Average |

NxCMA | Centered Moving-average Filter |

NxSMA | Seasonal Moving-average Filter |

Built-in optimizer for smoothing parameter, and initial value.
#### Featured Functions:

Function | Description |
---|---|

SESMTH | (Brown’s) Simple Exponential Smoothing |

LESMTH | (Brown’s) Linear Exponential Smoothing |

DESMTH | (Holt’s) Double Exponential Smoothing |

TESMTH | (Holt-Winters’s) Triple Exponential Smoothing |

GESMTH | Generalized Exponential Smoothing Function |

Trend analysis is very often used (or abused) in the industry to make a quick (and dirty) forecast. Executives might use the trending tool as a sanity check when he/she examines results from more advanced models. NumXL supports several forms of trend: linear, polynomial, power, exponential and logarithmic.
#### Featured Functions:

Function | Description |
---|---|

NxTrend | Deterministic trend in a time series |

ARMA/ARIMA

Tutorials

Screenshots

NumXL offers a wide selection of ARMA/ARIMA models such as ARMA (Autoregressive Moving Average), ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal Autoregressive Integrated Moving Average), ARMAX (Autoregressive Moving Average with Exogenous variables), SARIMAX, and US-Census X12-ARIMA.

By definition, auto-regressive moving average (ARMA) is a stationary stochastic process made up of sums of auto-regressive and moving average components:

$x_t -\phi_o – \phi_1 x_{t-1}-\phi_2 x_{t-2}-\cdots -\phi_p x_{t-p}= \\ a_t + \theta_1 a_{t-1} + \theta_2 a_{t-2} + \cdots + \theta_q a_{t-q}$

Where

- $x_t$ is the observed output at time t.
- $a_t$ is the innovation, shock or error term at time t.
- $p$ is the order of the last lagged variables.
- $q$ is the order of the last lagged innovation or shock.
- $a_t$ time series observations are independent and identically distributed (i.e. i.i.d) and follow a Gaussian distribution (i.e. $\Phi(0,\sigma^2)$)

Function | Description |
---|---|

ARMA | Defining an ARMA model |

ARMA_CHECK | Check parameters’ values for model stability |

ARMA_PARAM | Values of the Model’s Parameters |

ARMA_GOF | Goodness-of-fit of an ARMA Model |

ARMA_FIT | ARMA Model Fitted Values |

ARMA_FORE | Forecasting for ARMA Model |

ARMA_SIM | Simulated values of an ARMA Model |

By definition, auto-regressive moving average (ARMA) is a stationary stochastic process made up of sums of auto-regressive and moving average components:

$(1-\phi_1 L – \phi_2 L^2 -\cdots – \phi_p L^p)(1-L)^d x_t – \phi_o= \\(1+\theta_1 L+\theta_2 L^2 + \cdots + \theta_q L^q)a_t$

$y_t = (1-L)^d x_t$

$y_t = (1-L)^d x_t$

Where

- $x_t$ is the original non-stationary output at time t.
- $y_t$ is the observed differenced (stationary) output at time t.
- $d$ is the integration order of the time series.
- $a_t$ is the innovation, shock, or error term at time t.
- $p$ is the order of the last lagged variables.
- $q$ is the order of the last lagged innovation or shock.
- $a_t$ time-series observations are independent and identically distributed

(i.e. i.i.d) and follow a Gaussian distribution (i.e. $\Phi(0,\sigma^2)$)

Function | Description |
---|---|

ARIMA | Defining an ARIMA model |

ARIMA_CHECK | Check parameters’ values for model stability |

ARIMA_PARAM | Values of the Model’s Parameters |

ARIMA_GOF | Goodness-of-fit of an ARIMA Model |

ARIMA_FIT | ARIMA Model Fitted Values |

ARIMA_FORE | Forecasting for ARIMA Model |

ARIMA_SIM | Simulated values of an ARIMA Model |

The SARIMA model is an extension of the ARIMA model, often used when we suspect a model may have a seasonal effect.

By definition, the seasonal auto-regressive integrated moving average process ( SARIMA(p,d,q)(P, D, Q)s ) is a multiplicative of two ARMA processes of the differenced time series.

$(1-\sum_{i=1}^p {\phi_i L^i})(1-\sum_{j=1}^P {\Phi_j L^{j \times s}})(1-L)^d

(1-L^s)^D x_t = \\ (1+\sum_{i=1}^q {\theta_i L^i})(1+\sum_{j=1}^Q {\Theta_j L^{j

\times s}}) a_t$

$y_t = (1-L)^d (1-L^s)^D $

(1-L^s)^D x_t = \\ (1+\sum_{i=1}^q {\theta_i L^i})(1+\sum_{j=1}^Q {\Theta_j L^{j

\times s}}) a_t$

$y_t = (1-L)^d (1-L^s)^D $

Where:

- $x_t$ is the original non-stationary output at time t.
- $y_y$ is the differenced (stationary) output at time t.
- $d$ is the non-seasonal integration order of the time series.
- $p$ is the order of the non-seasonal AR component.
- $P$ is the order of the seasonal AR component.
- $q$ is the order of the non-seasonal MA component.
- $Q$ is the order of the seasonal MA component.
- $s$ is the seasonal length.
- $D$ is the seasonal integration order of the time series.
- $a_t$ is the innovation, shock, or the error term at time t.
- $\{a_t\}$ time-series observations are independent and identically distributed

(i.e. i.i.d) and follow a Gaussian distribution (i.e. $\Phi(0,\sigma^2)$)

Function | Description |
---|---|

SARIMA | Defining a SARIMA model |

SARIMA_CHECK | Check parameters’ values for model stability |

SARIMA_PARAM | Values of the Model’s Parameters |

SARIMA_GOF | Goodness-of-fit of an ARIMA Model |

SARIMA_FIT | ARIMA Model Fitted Values |

SARIMA_FORE | Forecasting for SARIMA Model |

SARIMA_SIM | Simulated values of a SARIMA Model |

The airline model is a special, but often used, case of multiplicative ARIMA model. For a given seasonality length (s), the airline model is defined by four(4) parameters: $\mu$, $\sigma$, $\theta$ and $\Theta$).

$ (1-L^s)(1-L)Y_t = \mu + (1-\theta L)(1-\Theta L^s)a_t$ OR

$ Z_t = (1-L^s)(1-L)Y_t = \mu + (1-\theta L)(1-\Theta L^s)a_t $ OR

$Z_t = \mu -\theta \times a_{t-1}-\Theta \times a_{t-s} +\theta\times\Theta \times a_{t-s-1}+ a_t $

$ Z_t = (1-L^s)(1-L)Y_t = \mu + (1-\theta L)(1-\Theta L^s)a_t $ OR

$Z_t = \mu -\theta \times a_{t-1}-\Theta \times a_{t-s} +\theta\times\Theta \times a_{t-s-1}+ a_t $

Where:

- $s$ is the length of seasonality.
- $\mu$ is the model mean
- $\theta$ is the coefficient of first lagged innovation
- $\Theta$ is the coefficient of s-lagged innovation.
- $\left [a_t\right ] $ is the innovations time series.

Function | Description |
---|---|

AIRLINE_CHECK | Check parameters’ values for model stability |

AIRLINE_PARAM | Values of the Model’s Parameters |

AIRLINE_GOF | Goodness-of-fit of an AIRLINE Model |

AIRLINE_FIT | ARIMA Model Fitted Values |

AIRLINE_FORE | Forecasting for AIRLINE Model |

AIRLINE_SIM | Simulated values of an AIRLINE Model |

In principle, an ARMAX model is a linear regression model that uses an ARMA-type process (i.e. $w_t$) to model residuals:

$y_t = \alpha_o + \beta_1 x_{1,t} + \beta_2 x_{2,t} + \cdots + \beta_b x_{b,t} + w_t$

$(1-\phi_1 L – \phi_2L^2-\cdots-\phi_pL^p)(y_t-\alpha_o -\beta_1 x_{1,t} – \beta_2 x_{2,t} – \cdots – \beta_b x_{b,t})= (1+ \theta_1 L + \theta_2 L^2 + \cdots + \theta_q L^q)a_t$

$(1-\phi_1 L – \phi_2 L^2 – \cdots – \phi_p L^p)w_t= (1+\theta_1 L+ \theta_2 L^2 + \cdots + \theta_q L^q ) a_t$

$a_t \sim \textrm{i.i.d} \sim \Phi (0,\sigma^2)$

$(1-\phi_1 L – \phi_2L^2-\cdots-\phi_pL^p)(y_t-\alpha_o -\beta_1 x_{1,t} – \beta_2 x_{2,t} – \cdots – \beta_b x_{b,t})= (1+ \theta_1 L + \theta_2 L^2 + \cdots + \theta_q L^q)a_t$

$(1-\phi_1 L – \phi_2 L^2 – \cdots – \phi_p L^p)w_t= (1+\theta_1 L+ \theta_2 L^2 + \cdots + \theta_q L^q ) a_t$

$a_t \sim \textrm{i.i.d} \sim \Phi (0,\sigma^2)$

- $L$ is the lag (aka back-shift) operator.
- $y_t$ is the observed output at time t.
- $x_{k,t}$ is the k-th exogenous input variable at time t.
- $\beta_k$ is the coefficient value for the k-th exogenous (explanatory)

input variable. - $b$ is the number of exogenous input variables.
- $w_t$ is the auto-correlated regression residuals.
- $p$ is the order of the last lagged variables.
- $q$ is the order of the last lagged innovation or shock.
- $a_t$ is the innovation, shock or error term at time t.
- $a_t$ time series observations are independent and identically distributed

(i.e. i.i.d) and follow a Gaussian distribution (i.e. $\Phi(0,\sigma^2)$)

Function | Description |
---|---|

ARMAX | Defining an ARMAX model |

ARMAX_CHECK | Check parameters’ values for model stability |

ARMAX_PARAM | Values of the Model’s Parameters |

ARMAX_GOF | Goodness of fit of an ARMAX Model |

ARMAX_FIT | ARMAX Model Fitted Values |

ARMAX_FORE | Forecasting for ARMAX Model |

ARMAX_SIM | Simulated values of an ARMAX Model |

In principle, an SARIMAX model is a linear regression model that uses a SARIMA-type process (i.e. ) This model is useful in cases we suspect that residuals may exhibit a seasonal trend or pattern.

$w_t = y_t – \beta_1 x_{1,t}-\beta_2 x_{2,t} – \cdots – \beta_b x_{b,t}$

$(1-\sum_{i=1}^p {\phi_i L^i})(1-\sum_{j=1}^P {\Phi_j L^{j \times s}})(1-L)^d (1-L^s)^D w_t -\eta = \\ (1+\sum_{i=1}^q {\theta_i L^i})(1+\sum_{j=1}^Q {\Theta_j L^{j \times s}}) a_t$ $a_t \sim \textrm{i.i.d} \sim \Phi(0,\sigma^2)$

$(1-\sum_{i=1}^p {\phi_i L^i})(1-\sum_{j=1}^P {\Phi_j L^{j \times s}})(1-L)^d (1-L^s)^D w_t -\eta = \\ (1+\sum_{i=1}^q {\theta_i L^i})(1+\sum_{j=1}^Q {\Theta_j L^{j \times s}}) a_t$ $a_t \sim \textrm{i.i.d} \sim \Phi(0,\sigma^2)$

Where:

- $L$ is the lag (aka back-shift) operator.
- $y_t$ is the observed output at time t.
- $x_{k,t}$ is the k-th exogenous input variable at time t.
- $\beta_k$ is the coefficient value for the k-th exogenous (explanatory)

input variable. - $b$ is the number of exogenous input variables.
- $w_t$ is the auto-correlated regression residuals.
- $p$ is the order of the non-seasonal AR component.
- $P$ is the order of the seasonal AR component.
- $q$ is the order of the non-seasonal MA component.
- $Q$ is the order of the seasonal MA component.
- $s$ is the seasonal length.
- $D$ is the seasonal integration order of the time series.
- $\eta$ is a constant in the SARIMA model
- $a_t$ is the innovation, shock, or error term at time t.
- $\{a_t\}$ time-series observations are independent and identically distributed

(i.e. i.i.d) and follow a Gaussian distribution (i.e. $\Phi(0,\sigma^2)$)

Function | Description |
---|---|

SARIMAX | Defining a SARIMAX model |

SARIMAX_PARAM | Values of the Model’s Parameters |

SARIMAX_CHECK | Check parameters’ values for model stability |

SARIMAX_GOF | Goodness-of-fit of a SARIMAX Model |

SARIMAX_FIT | SARIMAX In-Sample Fitted Values |

SARIMAX_FORE | Forecasting for SARIMAX Model |

SARIMAX_SIM | Simulated values of a SARIMAX Model |

The X-12-ARIMA software comes with extensive time series modeling and model selection capabilities for linear regression models with ARIMA errors (regARIMA models).

The ARIMA models, as discussed by Box and Jenkins (1976), are frequently used for seasonal time series. A general multiplicative seasonal ARIMA model for a time series z_t can be written:

$\phi(L)\Phi(L^s)(1-L)^d (1-L^s)^D\times z_t = \theta(L)\Theta(L^s)a_t$

Where:

- $L$ is the Lag or the Backshift operator
- $s$ is the seasonal period
- $(\phi(L)=\phi_o+\phi_1 L+\phi_2 L^2 +\cdots +\phi_p L^p)$ is the nonseasonal

auto-regressive (AR) model component - $(\Phi(L)=\Phi_o+\Phi_1 L+\Phi_2 L^2 +\cdots +\Phi_P L^P)$ is the seasonal

auto-regressive (AR) model component - $(\theta(L)=\theta_o+\theta_1 L+\theta_2 L^2 +\cdots +\theta_q L^q)$

is the nonseasonal moving average (MA) model component - $(\Theta(L)=\Theta_o+\Theta_1 L+\Theta_2 L^2 +\cdots +\Theta_Q L^Q)$

is the seasonal moving average (MA) model component - $(1-L)^d$ is the non-seasonal differencing operator of order d
- $(1-L^s)^D$ is the seasonal differencing operator of order D and seasonal

period (s) - $\{a_t\}\sim \textrm{i.i.d}\sim N(0,\sigma^2)$

Function | Description |
---|---|

X12ARIMA | Defining an X12-ARIMA Model |

X12APROP | X11 Seasonal Adjustment and X12-ARIMA Model properties |

X12ACOMP | X12-ARIMA Output Time Series |

X12AFORE | Forecasting for X12-ARIMA Model |

ARCH/GARCH

Tutorials

Screenshots

ARCH models are commonly employed in modeling financial time series that exhibit time-varying volatility and volatility clustering, i.e. periods of swings interspersed with periods of relative calm. ARCH-type models are sometimes considered to be in the family of stochastic volatility models, although this is strictly incorrect since at time t the volatility is completely pre-determined (deterministic) given previous values.

Similar to ARMA/ARIMA, modeling a GARCH-type model is a breeze. Using the GARCH Wizard, you can generate a model output table with all coefficient values and related calculations (e.g. LLF and residual diagnosis). This table can be used to calibrate the model and predict out-of-sample values.

If an autoregressive moving average model (ARMA model) is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH, Bollerslev(1986)) model.

$x_t = \mu + a_t$

$\sigma_t^2 = \alpha_o + \sum_{i=1}^p {\alpha_i a_{t-i}^2}+\sum_{j=1}^q{\beta_j \sigma_{t-j}^2}$

$a_t = \sigma_t \times \epsilon_t$

$\epsilon_t \sim P_{\nu}(0,1)$

$\sigma_t^2 = \alpha_o + \sum_{i=1}^p {\alpha_i a_{t-i}^2}+\sum_{j=1}^q{\beta_j \sigma_{t-j}^2}$

$a_t = \sigma_t \times \epsilon_t$

$\epsilon_t \sim P_{\nu}(0,1)$

Where

- $x_t$ is the time series value at time t.
- $\mu$ is the mean of GARCH model.
- $a_t$ is the model’s residual at time t.
- $\sigma_t$ is the conditional standard deviation (i.e. volatility)

at time t. - $p$ is the order of the ARCH component model.
- $\alpha_o,\alpha_1,\alpha_2,…,\alpha_p$ are the parameters of the

the ARCH component model. - $q$ is the order of the GARCH component model.
- $\beta_1,\beta_2,…,\beta_q$ are the parameters of the the GARCH

component model. - $\left[\epsilon_t\right]$ are the standardized residuals:

$\left[\epsilon_t\right] \sim i.i.d$

$E\left[\epsilon_t\right]=0$

$\mathit{VAR}\left[\epsilon_t\right]=1$ - $P_{\nu}$ is the probability distribution function for $\epsilon_t$.

Currently, the following distributions are supported:- Normal distribution

$P_{\nu} = N(0,1) $. - Student’s t-distribution

$P_{\nu} = t_{\nu}(0,1) $

$\nu \succ 4 $ - Generalized error distribution (GED)

$P_{\nu} = \mathit{GED}_{\nu}(0,1) $

$\nu \succ 1 $

- Normal distribution

Function | Description |
---|---|

GARCH | Defining a GARCH model |

GARCH_GUESS | Initial values for Model’s Parameters |

GARCH_CHECK | Check parameters’ values for model stability |

GARCH_LLF | Goodness-of-fit of a SARIMAX Model |

GARCH_RESID | GARCH In-Sample (standardized) residuals |

GARCH_FORE | Forecasting for GARCH Model |

GARCH_SIM | Simulated values of a GARCH Model |

GARCH_VL | Long-run Volatility of the GARCH Model |

The exponential general autoregressive conditional heteroskedastic (EGARCH) is another form of the GARCH model. E-GARCH model was proposed by Nelson (1991) to overcome the weakness in GARCH handling of financial time series. In particular, to allow for asymmetric effects between positive and negative asset returns:

$x_t = \mu + a_t$

$\ln\sigma_t^2 = \alpha_o + \sum_{i=1}^p {\alpha_i \left(\left|\epsilon_{t-i}\right|+\gamma_i\epsilon_{t-i}\right )}+\sum_{j=1}^q{\beta_j \ln\sigma_{t-j}^2}$

$ a_t = \sigma_t \times \epsilon_t$

$ \epsilon_t \sim P_{\nu}(0,1)$

$\ln\sigma_t^2 = \alpha_o + \sum_{i=1}^p {\alpha_i \left(\left|\epsilon_{t-i}\right|+\gamma_i\epsilon_{t-i}\right )}+\sum_{j=1}^q{\beta_j \ln\sigma_{t-j}^2}$

$ a_t = \sigma_t \times \epsilon_t$

$ \epsilon_t \sim P_{\nu}(0,1)$

Where

- $x_t$ is the time series value at time t.
- $\mu$ is the mean of the GARCH model.
- $a_t$ is the model’s residual at time t.
- $\sigma_t$ is the conditional standard deviation (i.e. volatility) at time t.
- $p$ is the order of the ARCH component model.
- $\alpha_o,\alpha_1,\alpha_2,…,\alpha_p$ are the parameters of the ARCH component model.
- $q$ is the order of the GARCH component model.
- $\beta_1,\beta_2,…,\beta_q$ are the parameters of the GARCH component model.
- $\left[\epsilon_t\right]$ are the standardized residuals:

$ \left[\epsilon_t \right] \sim i.i.d$

$ E\left[\epsilon_t\right]=0$

$ \mathit{VAR}\left[\epsilon_t\right]=1$ - $P_{\nu}$ is the probability distribution function for $\epsilon_t$.

Currently, the following distributions are supported:- Normal Distribution

$P_{\nu} = N(0,1) $ - Student’s t-Distribution

$P_{\nu} = t_{\nu}(0,1) $

$\nu \gt 4 $ - Generalized Error Distribution (GED)

$P_{\nu} = \mathit{GED}_{\nu}(0,1) $

$\nu \gt 1 $

- Normal Distribution

Function | Description |
---|---|

EGARCH | Defining an EGARCH model |

EGARCH_GUESS | Initial values for Model’s Parameters |

EGARCH_CHECK | Check parameters’ values for model stability |

EGARCH_LLF | Goodness of fit of a EGARCH Model |

EGARCH_RESID | EGARCH In-Sample (standardized) residuals |

EGARCH_FORE | Forecasting for EGARCH Model |

EGARCH_SIM | Simulated values of a EGARCH Model |

EGARCH_VL | Long-run Volatility of the EGARCH Model |

In finance, the return of a security may depend on its volatility (risk). To model such phenomena, the GARCH-in-mean (GARCH-M) model adds a heteroskedasticity term into the mean equation. It has the specification:

The GARCH-M(p,q) model is written as:

$x_t = \mu + \lambda \sigma_t + a_t$

$\sigma_t^2 = \alpha_o + \sum_{i=1}^p {\alpha_i a_{t- i}^2}+\sum_{j=1}^q{\beta_j \sigma_{t-j}^2}$

$ a_t = \sigma_t \times \epsilon_t$

$ \epsilon_t \sim P_{\nu}(0,1)$

$x_t = \mu + \lambda \sigma_t + a_t$

$\sigma_t^2 = \alpha_o + \sum_{i=1}^p {\alpha_i a_{t- i}^2}+\sum_{j=1}^q{\beta_j \sigma_{t-j}^2}$

$ a_t = \sigma_t \times \epsilon_t$

$ \epsilon_t \sim P_{\nu}(0,1)$

Where:

- $x_t$ is the time series value at time t.
- $\mu$ is the mean of the GARCH model.
- $\lambda$ is the volatility coefficient for the mean.
- $a_t$ is the model’s residual at time t.
- $\sigma_t$ is the conditional standard deviation (i.e. volatility) at time t.
- $p$ is the order of the ARCH component model.
- $\alpha_o,\alpha_1,\alpha_2,…,\alpha_p$ are the parameters of the ARCH component model.
- $q$ is the order of the GARCH component model.
- $\beta_1,\beta_2,…,\beta_q$ are the parameters of the GARCH component model.
- $\left[\epsilon_t\right]$ are the standardized residuals:

$ \left[\epsilon_t \right]\sim i.i.d$

$ E\left[\epsilon_t\right]=0$

$ \mathit{VAR}\left[\epsilon_t\right]=1$ - $P_{\nu}$ is the probability distribution function for $\epsilon_t$.

Currently, the following distributions are supported:- Normal Distribution

$P_{\nu} = N(0,1) $ - Student’s t-Distribution

$P_{\nu} = t_{\nu}(0,1) $

$\nu \gt 4 $ - Generalized Error Distribution (GED)

$P_{\nu} = \mathit{GED}_{\nu}(0,1) $

$\nu \gt 1 $

- Normal Distribution

Function | Description |
---|---|

GARCHM | Defining a GARCH-M model |

GARCHM_GUESS | Initial values for Model’s Parameters |

GARCHM_CHECK | Check parameters’ values for model stability |

GARCHM_LLF | Goodness-of-fit of a GARCH-M Model |

GARCHM_RESID | GARCH-M In-Sample (standardized) residuals |

GARCHM_FORE | Forecasting for GARCH-M Model |

GARCHM_SIM | Simulated values of a GARCH-M Model |

GARCHM_VL | Long-run Volatility of the GARCH-M Model |

Date & Calendar

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

A core assumption in econometric methods is that time-series observations are equally spaced and present. This arises either because observations are made deliberately at even intervals (continuous process) or because the process only generates outputs at such an interval in time (discrete process).

For example, a daily financial time series of IBM stock closing prices are based on the NYSE holidays calendar, so each observation is taken on an NYSE trading day (open/close). For weekly or monthly time series, the number of trading days varies from one observation to another and we may have to adjust for their effect.

Calendar events influence the values of the time series sample, and a prior adjustment for those events will help us to better understand the process, modeling, and forecast.

NumXL comes with numerous functions to support calendar adjustment, date rolling and adjustment, U.S. and non-U.S based holidays support, non-western weekends, and public and bank holiday calendars.

This date Calculation functions allow you to perform calculations with dates and periods like:

- Adding and subtracting periods in days, months and years
- Move a given date to the nearest workday using any of the industry business day counting conventions.
- Calculate the date of given n-th weekday occurrence (e.g. 3rd Friday) in any given month.
- more.

Function | Description |
---|---|

NxAdjust | Move to the nearest workday |

NxEDATE | Advance a date by a given period |

NxNetWorkdays | Number of working days in a given date range |

NxNWKDY | Date of the N-th Weekday in a Month |

NxWKDYOrder | Order of the weekday for a given date |

NxWorkday | Advance a given date N-Working Days |

A holiday is a day designated as having special significance for individuals, governments, or religious groups. Typically, a holiday does not necessarily exclude doing normal work but for our purposes, NumXL assumes all supported holidays (e.g. National Holidays) exclude normal work.

Observed holidays and actual dates for particular holidays (e.g. Easter) may vary between countries, so in NumXL a holiday code is prefixed by the country ISO code (e.g. USA, CAN, GBR, etc.).

Function | Description |
---|---|

NxIsHoliday | Examine a given date if it falls on any holiday. |

NxFindHLDY | Lookup the holiday name that falls on a given date |

NxHLDYDate | Calculate the occurrence date of a given holiday in a given year. |

NxHLDYDates | Lookup and return all holiday dates in a given date range. |

As of Excel 2007, Microsoft supports different weekend occurrences in the international version of the date functions (e.g. WORKDAY.INTL). The weekend conventions are defined by either a number or a 7-characters long string (code).
#### Featured Functions:

Function | Description |
---|---|

NxWKNDStr | Weekend Code |

NxWKNDUR | Weekend Duration |

NxWKNDate | Next/Last Weekend date |

NxWKNDNo | Weekend Convention Number |

NxIsWeekend | Examine a given date whether it falls on a weekend. |

For financial time analysis, a calendar is a definition of a list of observed holidays and a weekend days convention.

Calendars are used extensively in date calculation functions. NumXL comes in with many predefined calendars (e.g. US Federal Government Holidays, US Bank holidays, EU Bank calendar, etc, ), but the user can also define a custom calendar by specifying supported holidays and weekend convention.

Function | Description |
---|---|

NxCalendars | Returns a list of supported calendars with a given prefix (optional) |

NxCALHolidays | Returns a list of supported holidays in a given calendar |

NxCALWKND | Return the weekend convention for a given calendar. |

Forecasting Performance

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

Forecasting is an ongoing process undertaken by several stakeholders to improve accuracy and reliability over time. To this end, the process’s output (i.e. forecast accuracy) should be quantified and monitored over time.

Furthermore, by quantifying forecast accuracy for competing models over time, you may use those metrics to compare (and rank) several forecasting methods or establish a baseline.

Function | Description |
---|---|

SSE | Sum of squared errors |

MSE | Mean of squared errors |

GMSE | Geometric mean of squared errors |

SAE | Sum of absolute errors |

MAE | Mean of absolute erros |

RMSE | Root mean squared errors |

GRMSE | Geometric root mean squared errors |

Function | Description |
---|---|

MAPE | Mean absolute percentage error |

MdAPE | Median absolute percentage error |

MAAPE | Mean arctangent absolute percentage error |

In this category, we use relative forecast error to some benchmark forecast such as the latest available value (Naïve 1 forecast), or the latest available value after seasonality has to be taken into account (Naïve 2 forecast).
#### Featured Functions:

Function | Description |
---|---|

MRAE | Mean relative absolute error |

MdRAE | Median relative absolute error |

GMRAE | Geometric mean relative absolute error |

MASE | Mean absolute scaled error |

PB | Percentage better |

MDA | Mean directional accuracy |

Factors Analysis

Tutorials

Screenshots

Factor analysis is a useful tool to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved, uncorrelated variables called factors.

In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible.

For the SLR, the objective is to find a straight line which provides the best fit for the data points $\left(x_i,y_i\right)$

$y = \alpha + \beta \times x$

$y = \alpha + \beta \times x$

Where

- $\alpha$ is the constant (aka intercept) of the regression.
- $\beta$ is the coefficient (aka slope) of the explanatory variable.

Function | Description |
---|---|

SLR_PARAM | Coefficients’ values for the SLR model |

SLR_GOF | Goodness-of-fit (R^2, LLF, AIC) for SLR model |

SLR_FITTED | Fitted values (mean and residuals) of the SLR model |

SLR_FORE | Forecast (mean and error) of the SLR model |

SLR_SNOVA | Analysis of Variance of the SLR model |

Given a data set $\{y_i,\, x_{i1}, \ldots, x_{ip}\}_{i=1}^n$ of n statistical units, a linear regression model assumes that the relationship between the dependent variable $y_i$ and the p-vector of regressors $x_i$ is linear. This relationship is modeled through a disturbance term or error variable $\epsilon_i$ — an unobserved random variable that adds noise to the linear relationship between the dependent variable and regressors.

The MLR is described as follow:

$ y_i = \beta_1 x_{i1} + \cdots + \beta_p x_{ip} + \varepsilon_i = \mathbf{x}^{\rm

T}_i\boldsymbol\beta + \varepsilon_i, \qquad i = 1, \ldots, n, $

$ y_i = \beta_1 x_{i1} + \cdots + \beta_p x_{ip} + \varepsilon_i = \mathbf{x}^{\rm

T}_i\boldsymbol\beta + \varepsilon_i, \qquad i = 1, \ldots, n, $

Where

- $\mathbf{x}^{\rm T}_i$ is the transpose matrix

Function | Description |
---|---|

MLR_PARAM | Coefficients’ values of the MLR model |

MLR_GOF | Goodness of fit of MLR model |

MLR_FITTED | Fitted (mean and residuals) values of MLR model |

MLR_FORE | Forecasting (mean, error, C.I.) for MLR model |

MLR_ANOVA | Analysis of Variance for MLR model |

MLR_PRTest | Partial F-Test for regression variables |

MLR_STEPWISE | Regression variables selection method (stepwise) |

Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal linear transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Let’s define a matrix $\mathbf{X}$, where each column corresponds to one

variable, and each row corresponds to a different repetition (or measurement) of the experiment:

$\mathbf{X} = \begin{pmatrix} \mathbf{x}^{\rm T}_1 \\ \mathbf{x}^{\rm T}_2

\\ \vdots \\ \mathbf{x}^{\rm T}_n \end{pmatrix} = \begin{pmatrix} x_{11}

& \cdots & x_{1p} \\ x_{21} & \cdots & x_{2p} \\ \vdots &

\ddots & \vdots \\ x_{n1} & \cdots & x_{np} \end{pmatrix} $

variable, and each row corresponds to a different repetition (or measurement) of the experiment:

$\mathbf{X} = \begin{pmatrix} \mathbf{x}^{\rm T}_1 \\ \mathbf{x}^{\rm T}_2

\\ \vdots \\ \mathbf{x}^{\rm T}_n \end{pmatrix} = \begin{pmatrix} x_{11}

& \cdots & x_{1p} \\ x_{21} & \cdots & x_{2p} \\ \vdots &

\ddots & \vdots \\ x_{n1} & \cdots & x_{np} \end{pmatrix} $

Furthermore, each column (variable) has a zero empirical mean (the empirical (sample) mean of the distribution has been subtracted from the data set).

The PCA transformation that preserves dimensionality (that is, gives the same number of principal components as original variables) Y is then given by:

$ \mathbf{Y}^{\rm T} = \mathbf{X}^{\rm T}\mathbf{W} $

$ \mathbf{Y}^{\rm T} = \mathbf{X}^{\rm T}\mathbf{W} $

Using signular value decomposition (SVD) for the $ \mathbf{X}^{\rm T}$, we

can express the PCA transform as

$ \mathbf{Y}^{\rm T} = (\mathbf{W}\mathbf{\Sigma}\mathbf{V}^{\rm T})^{\rm

T}\mathbf{W}$

can express the PCA transform as

$ \mathbf{Y}^{\rm T} = (\mathbf{W}\mathbf{\Sigma}\mathbf{V}^{\rm T})^{\rm

T}\mathbf{W}$

Where:

- $\mathbf{W}$ is the matrix of eigenvectors of the covariance matrix $\mathbf{X}

\mathbf{X}^{\rm T}$ - $\mathbf{V}$ is the matrix of eigenvectors of the matrix $\mathbf{X}^{\rm

T} \mathbf{X}$ - $\mathbf{\Sigma}$ is a rectangle matrix with nonnegative real numbers

on the diagonal

The PCA transformation $\mathbf{Y}$ is given by:

$ \mathbf{Y}^{\rm T} = \mathbf{V}\mathbf{\Sigma}^{\rm T} $

$ \mathbf{Y}^{\rm T} = \mathbf{V}\mathbf{\Sigma}^{\rm T} $

Function | Description |
---|---|

PCA_COMP | Principal component (PC) values |

PCA_VAR | Calculate the variable values using a subset of the principal components. |

Principal component regression (PCR) is a two-stage procedure; first reduces the predictor variables using principal component analysis then uses the reduced variables in an OLS regression fit.

PCR is often used when the number of predictor variables is large, or when strong correlations exist among the predictor variables.

Function | Description |
---|---|

PCR_PARAM | Calculate the coefficients’ values (end std. errors) for the PCR model |

PCR_GOF | Calculate the goodness-of-fit (e.g. R^2, LLF, AIC) for the PCR model. |

PCR_ANOVA | Conduct the Analysis of Variance (ANOVA) for the PCR model. |

PCR_FITTED | Calculate the in-sample fitted values (mean, residuals) for the PCR model. |

PCR_FORE | Forecasting (mean, error, and C.I.) for the PCR model. |

PCR_PRTest | Partial F-Test for PCR. |

PCR_STEPWISE | Perform the stepwise variables selection method. |

The generalized linear model (GLM) is a flexible generalization of ordinary least squares regression. The GLM generalizes linear regression by allowing

the linear model to be related to the response variable (i.e. $Y$) via a

link function (i.e. $g(.)$)and by allowing the magnitude of the variance

of each measurement to be a function of its predicted value.

The GLM is described as follow:

$Y = \mu + \epsilon $

And

$ E\left[Y\right]=\mu=g^{-1}(X\beta) = g^{-1}(\eta)$

$Y = \mu + \epsilon $

And

$ E\left[Y\right]=\mu=g^{-1}(X\beta) = g^{-1}(\eta)$

Where

- $\epsilon$ is the residuals or deviation from the mean
- $g(.)$ is the link function
- $g^{-1}(.)$ is the inverse-link function
- $X$ is the independent variables or the exogenous factors
- $\beta$ is a parameter vector
- $\eta$ is the
__linear predictor__: the quantity which incorporates

the information about the independent variables into the model.

$\eta=X\beta$

Function | Description |
---|---|

GLM | GLM model definition |

GLM_CHECK | Verify the parameters’ values of the GLM model |

GLM_GUESS | Calculate a rough, but a valid set of coefficients’ values for the given GLM model. |

GLM_CALIBRATE | Calculate the GLM optimized model coefficients values (and errors). |

GLM_LLF | Calculate the log-likelihood function (LLF) for the GLM Model. |

GLM_MEAN | Calculate the in-sample fitted value of the GLM Model |

GLM_RESID | Calculate the in-sample error terms/residuals for the GLM model |

GLM_FORE | Calculate the mean-forecast of the GLM Model |

GLM_FORECI | GLM Forecasting Confidence Interval |

Spectral Analysis

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

In statistics, spectral analysis is a procedure that decomposes a time series into a spectrum of cycles of different lengths. Spectral analysis is also known as frequency domain analysis.

In principle, the DFT converts a discrete set of observations into a series of continuous trigonometric (i.e. sine and cosine) functions. So the original signal can be represented as:

$ X_k = \sum_{j=0}^{N-1} x_j e^{-\frac{2\pi i}{N} j k} $

Where

- $k$ is the frequency component
- $x_0,…,x_{N-1}$ are the values of the input time series
- $N$ is the number of non-missing values in the input time series

The Cooley-Tukey radix-2 decimation-in-time fast Fourier transform (FFT) algorithm divides a DFT of size N into two overlapping DFTs of size $\frac{N}{2}$ at each of its stages using the following formula:

$ X_{k} = \begin{cases} E_k + \alpha \cdot O_k & \text{ if }

k \lt \frac{N}{2} \\ E_{\left (k-\frac{N}{2} \right )} – \ \alpha

\cdot O_{\left (k-\frac{N}{2} \right )} & \text{ if } k \geq

\frac{N}{2} \end{cases} $

k \lt \frac{N}{2} \\ E_{\left (k-\frac{N}{2} \right )} – \ \alpha

\cdot O_{\left (k-\frac{N}{2} \right )} & \text{ if } k \geq

\frac{N}{2} \end{cases} $

Where

- $E_k$ is the DFT of the even-indicied values of the input time series, $x_{2m} \left(x_0, x_2, \ldots, x_{N-2}\right)$
- $O_k$ is the DFT of the odd-indicied values of the input time series, $x_{2m+1} \left(x_1, x_3, \ldots, x_{N-2}\right)$
- $\alpha = e^{ \left (-2 \pi i k /N \right )}$,
- $N$ is the number of non-missing values in the time series data.

Function | Description |
---|---|

DFT | Calculate the Discrete (Fast) Fourier Transform |

IDFT | Calculate the Inverse Discrete Fourier Transform |

The filters functions decompose the time series into a trend and cyclical components.

Function | Description |
---|---|

NxBK | Apply the Baxter-King filter |

NxHP | Apply Hodrick-Prescott filter |

NxConv | Compute the convolution operator between the two time-series. |

A periodogram is an estimate of the power spectral density of a time series. This method is useful to identify dominant seasonality in the underlying data set.

Function | Description |
---|---|

Periodogram | Calculates the Power Spectral Density (PSD) estimate. |

Power Tools

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

In this section, we present a set of tools available on the NumXL toolbar as shortcuts for:

- calibrating the values of your underlying model (e.g. ARMA, GARCH, GLM, etc,) using MS Solver.
- Creating an out-of-sample model-based forecast,
- Running model-based simulation
- Running MC simulation

Using the currently active cell, the calibration tool detects the underlying model, Invoke and Setup MS Solver’s fields (e.g. utility function, parameters to optimize for, constraints, etc.) with calculations in the selected model table for the optimization problem.

Using the currently active cell, the forecasting wizard detects the underlying model, and displays proper UI to collected required inputs (e.g. most-recent realized observation, forecast horizon, significance level, etc.) from the end-user, and, finally, generate an output forecast table with proper C.I.

Using the currently active cell, the Simulation wizard detects the designated model and pops proper UI to collect required inputs (e.g. recently realized observations, horizon, number of simulation paths, etc.) from the end-user, and generate the simulations paths.

This functionality is similar to Excel’s data-table concept, except there is no set of predefined cells’ values to calculate for. Instead, assuming you have one or more cells with volatile (i.e. random) values (and your output calculation(s) affected by those cells), the MC simulation functionality simply forces Excel to re-calculate designated workbook/worksheet/cells range, and capture the outputs in each run into a designated cells range.

Utilities

Tutorials

Screenshots

- Tutorial Videos
- Reference Manual
- User’s Guide
- Technical Notes
- Tips & Tricks
- Case Studies

Need more tools to prepare your data set? Run regular expressions for match and/or replace, split/tokenize string, examine the presence of missing values and transform 2-D data sets.

A regular expression (shortened as regex)is a sequence of characters that define a search pattern. The pattern can be applied:

- (Match) Matching against a given text/string for a possible match, and, if desired,
- (Extract) Identify and extract substring.
- (Replace) Identify and substitute one (or all) occurrence of a substring with another value
- (Tokenize): Split the text into a series of substrings, using a given separator pattern.

Function | Description |
---|---|

NxMatch | Regular Expression (regex) Match Function |

NxReplace | Regular Expression (regex) Replace Function |

NxTokenize | Separate a string by a delimiter into an array of sub-strings |

Detect any missing value in a data set, and, possibly remove them.
#### Featured Functions:

Function | Description |
---|---|

HASNA | Check input for observations with missing Value |

RMNA | Remove Missing Values |

MV_OBS | Number of observations with non-missing values |

MV_VARS | Effective Number of Variables |

The functions in the category operate on one(1) or two(2) dimensional data cells. They are very useful, especially when we handle missing values, interpolation, and/or selecting a subset of input variables.

Function | Description |
---|---|

NxArray | Create an array using constants and/or cells references |

NxFold | Convert a 3-column dataset into a 2-D table or matrix |

NxFlatten | collapse a data table into a flat 3-column dataset form |

NxTranspose | Convert row to column (and vice versa) |

NumXL installation and user’s license information.
#### Featured Function:

Function | Description |
---|---|

NUMXL_INFO | Retrieves NumXL’s installation information. |

Read More

Superb! Not only is the software leading edge, but our interactions with NumXL staff have been excellent. We have been lucky enough to work with the owner, Mohamad, who not only has advised us about how to use their product most efficiently but has also helped us integrate NumXL into our VBA scripts! I'd rather deal with people I can speak to and build a strong working relationship than being a number to a large multi-national.

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Its a great software, and I can build my own models easily. Its focus on times series analysis which is what investors in the financial markets need. 2. Its ease of use. 3. Has facilities and features that incorporate the latest advances in techniques for time series analysis.

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Excellent Time Series Forecasting Tool! NumXL is easy to use and is well supported! There are plenty of tutorials and examples to assist you!

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I purchased NumXL to adjust variables seasonally. It is a lot more friendly to use than the obsolete Arima X-12 user interface. I was able to do just what I wanted with this software. Moreover, it allows the creation of ARMA / GARCH models following easy steps. I highly recommend NumXL to prospective buyers who want a reliable and useful Excel add-on.

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Love it! Been a long time user and expect to be a user forever. The founder, Mohamad, has built an excellent product, and his support team is very responsive and knowledgeable. We use the tool every day, and I love it.

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I highly recommend NumXL to anyone looking to perform statistical analysis within Excel. The tutorials, examples, and customer support are a real bonus, and the Spider Financial Corp. team is responsive and knowledgeable. The array of functions included with NumXL is quite diverse, and the software has performed flawlessly. NumXL is excellent software.

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Easy interface, excellent support, and lots of good white papers with easy to understand examples. Suitable software for introductory, medium econometric needs at the business level. No programming necessary.

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The NumXL is not the only product I use. However, it is fully functional and sufficient for many of my statistical operations. I use this product frequently when preparing the support material for my classes. I also like to present my students various software products in order to show them the broad spectrum of possibilities in selecting out of these products those, which are the most convenient for their special needs.

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Powerful, economical, friendly and resourceful support

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It is a tool with a very intuitive and easy to handle interface, with many functionalities, and also very reliable to create any graphic you want. In addition to its excellent functions and good system support, when presenting problems with the tool, it has a super responsive technical support that is responsible for giving immediate answers to the needs submitted by users.

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Great software, excellent tutorials, and superb support

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I needed the package for a particular purpose: doing Fourier analysis. Worked very well, easy to use, and well supported by videos on YouTube.

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I love the compatibility of this software, as well as the ease of use.

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