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Software Free Download | Soft32.com Home Windows Mac Mobile Blog You can skip this in seconds Click here to continue Home Windows Business Office Add-ins NumXL Free Download Secure NumXL Download Options There are no available download mirrors for this version. You can download the latest version of NumXL here: Download Latest! Popular Downloads Kundli 4.5 Understand your life events through astrology. Macromedia Flash 8 8.0 Macromedia Flash 8 Professional. Grand Auto Adventure 1.0 3D racing sandbox game. Grand Theft Auto: Vice City 1.0 Welcome to Vice City. Welcome to the 1980s.... Cool Edit Pro 2.1.3097.0 Advanced multi track sound editing program. Hill Climb Racing 1.0 The Ultimate Hill Climb Racing adventure Cheat Engine 6.8.1 Cheating for experts! Tom VPN 2.2.8 TomVPN-No Sign up Free used Zuma Deluxe 1.0 Survive the ancient temples of Zuma. Minecraft 1.10.2 Build anything you can imagine. Netcut 2.1.4 Netcut Horizon 2.9.0.0 Innovative Xbox 360 modding tool. Ulead Video Studio Plus 11 Video editing software for Windows. Grand Theft Auto IV Patch... 1.0.1.0 Grand Theft Auto IV Patch 1.0.1.0. C-Free 5.0 An excellent IDE for C/C++ language. CDMA2000 1X Wireless Network... 1.0.0.26 Age of Empires 1.0 Civilization-building strategy game. Vector on PC 1.0 Vector, a side scrolling parkour inspired Driver Booster 9.0.1.104 Update outdated drivers safely and rapidly. Operation Ubersoldat 1.0 WW2 first person shooter. All popular downloads Home Submit Software Contact Us Advertise with Us All Software Sitemap Jobs Blog Facebook Twitter RSS Soft32© 2003 - 2025 ITNT. All rights reserved. Soft32 is also available in Deutsch, Español, Français, Português, Italiano. Legal Information Privacy Policy Terms of Service Copyright EULA DMCA Uninstall

Download NumXL 1.68.7.1

This blog stemmed from a support case involving an enterprise customer who had difficulties running NumXL add-ins after the MIS/IT department implemented new security policies that restricted application add-ins and macros in Microsoft Excel via the Microsoft Trust Center:Under the “Add-ins” section, Check or select the” Require Application Add-in to be signed by Trusted Publisher.” Option. Under the “Macro Settings” section, Select or check the “Disable VBA macros except digitally signed macros” option. Although Spider Financial adheres to the best security practices and digitally codesigned the NumXL application and its associated macros, the end-user “Trusted Publishers” list did not include the public certificate of our codesigning key. Consequently, Microsoft Excel deactivated the NumXL add-ins and displayed a security warning message regarding the disabled active content. Resolution I: The Warning message above includes a button to enable the add-in only for the current session but to make it permanent, you’d need to click on the link in the warning message to take you to “File->Info”: Now, press on the “Enable Content” button, then select “Advanced Options”: Now, you can add Spider Financial as a “Trusted Publisher,” and the security warning will not reappear.The advantages of this approach are that it does not require heightened security permissions, meaning there is no need to contact the system administrator to add Spider Financial as a trusted publisher in the end-user list, unlike the local machine.IMPORTANT: This approach may not work if the MIS/IT department enforces additional policies that prevent end-users from adding to the trusted publisher’s list or instructs Microsoft Office to disregard the end-user’s “Trusted Publishers” list. In that case, let’s consider the second approach.Resolution II: Starting with version 1.69.5, the NumXL installer, using its elevated permission, attempts to add the public certificate to the local machine’s “Trusted Publisher” list. For older NumXL

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This is the first tutorial on time series spectral analysis. In this entry, we examine the Discrete Fourier Transform (DFT) and its inverse, as well as data filtering using DFT outputs. The DFT is basically a mathematical transformation and maybe a bit dry, but we hope that this tutorial will leave you with a deeper understanding and intuition through the use of NumXL functions and wizards.In future entries, we will dedicate more time to discrete data filters, their construction, and of course, application.BackgroundYou have probably occasionally transformed your data to stabilize the variance (e.g. log transform) or to improve the distribution of the values in the sample data.$$x_t=\{ x_1,x_2, \cdots , x_T \}$$$$y_t=\log(x_t)$$$$y_t=\{y_1,y_2, \cdots , y_T\}$$In mathematics, the discrete Fourier transform (DFT) converts a finite list of equally-spaced samples of a function into a list of coefficients of a finite combination of complex sinusoids, ordered by their frequencies, which have those same sample values. DFT converts the sampled function from its original domain (often time or position along a line) to the frequency domain.In sum, the Fourier transform has the following properties:The transformed data is no longer in the time domain.The transformation operates on the whole data set. It is not a point-by-point transformation as we have seen with earlier transformations in the time domain.$$x_t=\{ x_1,x_2, \cdots , x_T \}$$$$Y= \mathcal{F}(\{ x_1,x_2, \cdots , x_T \})$$$$Y=\{ y_1,y_2, \cdots , x_k \}$$$$\{ x_1,x_2, \cdots , x_T \} = \mathcal{F}^{-1}(\{ y_1,y_2, \cdots , x_k \})$$The transformed data is complex (not real-valued).What is the Discrete Fourier Transform (DFT)?In plain words, the discrete Fourier transform decomposes the input time series into a set of cosine functions.$$x_m=\frac{1}{N}\sum_{k=1}^N A_k \times \cos(\phi_k+k\times \omega \times m)$$So, you can think of the k-th output of the DFT as the $A_k \angle \phi_k$. The $A_k$ is referred to as the amplitude and the $\phi_k$ as the phase (in radians).The input time series can now be expressed either as a time-sequence of values or as a frequency-sequence of $[A_k \angle \phi_k]$. pairs. Knowing the set of $[A_k \angle \phi_k]$, we can recover the exact input time series.What is $\omega$?$\omega$ is the fundamental or the principal radian frequency. IT is expressed as follows:$$\omega = \frac{2\pi}{T}$$Where:$T$ is the number of observations in the equally-spaced input time series.What is N?N is the number of $[A_k \angle \phi_k]$ pairs we need to have, so we can recover the original input time series with a floor value of $\frac{T}{2}$.Note that the zero-frequency component (i.e. k=0) is always real-value, and in the case of even-sized time series, the last frequency component is also real-value, which brings the total number of values (amplitude and phase) to T. There is no gain or loss of information or storage requirement. Download Now! NumXL 1.68.7.1. Home; Windows; Business; Office Add-ins; NumXL; NumXL 1.68.7.1. Download Now! Direct link. NumXL is a Microsoft Excel add-in for Numxl Free Download Crack For 16. Download. Numxl Free Download Crack For 16. NumXL Free Trial Download Get the full version of . NumXL - NumXL is a Microsoft Excel add-in for econometrics and financial time series analysis, designed to make financial modeling and time series easier to ab2f6753c0 . Numxl Free Download Crack For 16. 2 / 2

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Versions or if the installer fails to add the certificate, you should manually add it, as described below. To add or remove a public certificate from the “Trusted Publishers” list, launch the Microsoft Management Console (MMC) application by typing “mmc.exe” in a command window. The MMC requires elevated security permission, so you must call your administrator to complete this method. Next, use the menu to press “File” and select “Add/Remove Snap-in.” Select “certificates” from the list of Snap-ins and press the “Add >” button (between the two panels). Then, select the “Computer Account” for your “local machine.” Press the OK to complete. Now, select “Trusted Publishers” in the tree view in the left panel, and with your mouse, right-click in the right-hand panel and select “All Tasks->Import” from the popup menu. The certificate import wizard appears and asks for the certificate file. The NumXL public certificate file for the code-signing key can be found in the NumXL installation folder (e.g., C:\Program Files\NumXL). That’s all. The “Trusted Publishers” list should include Spider Financial.

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To examine a given time series for signs of the ARCH Effect, we conduct a statistical test for evidence of white noise (Ljung-Box Test) between the squared input time series.Similar to the regular white noise test, the hypothesis in question is:$$H_{o}: \rho_{1}=\rho_{2}=...=\rho_{m}=0 $$$$H_{1}: \exists \rho_{k}\neq 0$$NumXL provides an intuitive interface to help Excel users conduct an ARCH effect using several lag orders. In this tutorial, we’ll demonstrate the steps to perform a thorough ARCH test using NumXL functions and wizards in Excel.ProcessSelect an empty cell to store the ARCH Effect tests results table.Locate the Statistical Test (STAT TEST) icon in the toolbar (or menu in Excel 2003) and click on the down-arrow. When the drop-down menu appears, select “ARCH Effect Test”.The ARCH Effect Test dialog box appears.Select the cell range for the input data.Click the “Options” tab and pick a maximum lag order for this test.If your data include one or more intermediate observations with missing values, click the “Missing Values” tab.Click “OK”.OutputThe test wizard generates the ARCH Effect Test statistics for different lag scenarios.

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In a statistical sense, a time series ${x_t}$ is characterized as having a weak white test in Excel (white noise) if ${x_t}$ is a sequence of serially uncorrelated random variables with zero mean and finite variance. Strong white noise also has the quality of being independent and identically distributed, which implies no autocorrelation.In particular, if $x_t$ is normally distributed with mean zero and standard deviation $\sigma$, the series is called a Gaussian white noise (white test in Excel).Statistical TestingTo examine the data series for evidence of any serial correlation, we use Ljung‐Box statistical test and the modified $Q^*(m)$ statistics.The Ljung‐Box Test:$$H_o:\rho_1=\rho_2=\rho_3=...=\rho_m=0 $$ $$ H_1:\exists \rho_{1\leqslant k\leq m} \neq 0$$Where:$H_o$: Null Hypothesis (White noise)$H_1$: Alternative hypothesis (not white noise)$m$: Upper lag limit of the test. The upper lag limit can either be set by us or left for the function to pick a proper limit. Practitioners use $\ln{T}$ as a proper value for $m$.Ljung‐Box StatisticsThe Ljung‐Box statistics (i.e. $Q^*(m)$) is an enhancement on the classical $\chi^2$ test especially for small samples size.$$Q^*(m)=T(T+2)\sum_{j=1}^m{\frac{\rho_j^2}{T-j}} Q^*(m)\sim \chi_{\nu=m}^2$$This is a one‐tail statistical test, A p‐value greater than the significance level ($\alpha$) leads us to not‐to‐reject the null hypothesis or, in other words, the time series is a white test in Excel (white noise).WNTestWNTest function in NumXL computes the P‐value for the $Q^*(m)$ statistics for our sample data. The upper lag limit ($m$) is set by default to $\ln{T}$, but the user can override this value by passing a value for this parameterExample: Using the NumXL descriptive statistics form, we computed the various summary statistics and performed Ljung-Box white noise test (WNTest) among others.The summary statistics and tests:The P-Value of the Ljung-Box white noise test (white test in Excel) is greater than significance level (i.e. $\alpha$), so we don't reject the white noise hypothesis ($H_o$), or, simply stated; there is no statistical evidence of a serial correlation, so the data can be white noise.Tutorial Video. Download Now! NumXL 1.68.7.1. Home; Windows; Business; Office Add-ins; NumXL; NumXL 1.68.7.1. Download Now! Direct link. NumXL is a Microsoft Excel add-in for

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Q1: Can we use EWMA to estimate (or forecast) volatility more than one step ahead?The EWMA volatility representation does not assume a long-run average volatility, and thus, for any forecast horizon beyond one-step, the EWMA returns a constant value:$$\sigma_n^2=(1-\lambda)r_{n-1}^2+\lambda\sigma_{n-1}^2$$ $$E[\sigma_{n+1}^2]=(1-\lambda) E[r_{n}^2]+\lambda \sigma_{n-1}^2$$ $$E[\sigma_{n+1}^2]=(1-\lambda)\sigma_n^2+\lambda \sigma_{n-1}^2=\sigma_n^2$$ $$E[\sigma_{n+k}^2]=\sigma_n^2$$Q2: What is the initial value of the variance (i.e. $\sigma_1^2$) in the NumXL EWMA function? Can I set a different value?Currently, we set the value to zero, but we set the variance at the end of first period equal to the square of return on that period to start the EWMA.$$\sigma_0^2=0$$ $$\sigma_1^2=r_1^2$$ $$\sigma_2^2=(1-\lambda)r_1^2 + \lambda \sigma_1^2= r_1^2$$ $$\sigma_3^2=(1-\lambda)r_2^2 + \lambda \sigma_2^2= r_1^2$$ $$\cdots$$ $$\sigma_n^2=(1-\lambda)r_{n-1}^2 + \lambda \sigma_{n-1}^2$$For a large data set, the value has very little impact on the calculated value.Going forward, we are planning to avail an argument to accept user-defined initial volatility value.Q3: What is EWMA’s relationship to ARCH/GARCH Model?EWMA is basically a special form of an ARCH() model, with the following characteristics:The ARCH order is equal to the sample data size.The weights are exponentially declining at rate $\lambda$ throughout time.Q4: Does EWMA revert to the mean?No. EWMA does not have a term for the long-run variance average; thus, it does not revert to any value.Q5: What is the variance estimate for horizon beyond one day (or step) ahead?As in Q1, the EWMA function returns a constant value equal to the one-step estimate value.Q6: I have weekly/monthly/annual data. Which value of I should use?You may still use 0.94 as a default value, but if you wish to find the optimal value, you’d need to set up an optimization problem for minimizing the SSE or MSE between EWMA and realized volatility.See our volatility 101 tutorial in “Tips and Hints” on our website for more details and examples.Q7: if my data does not have a zero mean, how can I use the function?No worry, the NumXL implementation of EWMA removes the mean automatically on your behalf.

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In an earlier entry, we discussed the histogram as a non-parametric method for the probability distribution inference of a random variable. In this tutorial, we go over the empirical distribution function and estimate its values for the different points in the sample.For sample data, we generated a data set of 29 randomly generated values from the Gaussian distribution.BackgroundThe empirical distribution function (EDF) or empirical cdf is a step function that jumps by 1/N at the occurrence of each observation:$$EDF(x)=\frac{1}{N}\sum_{i=1}^N I\{x\leq x_i\}$$Where$\{A\}$ is the indicator of an event function$I\{x\leq x_i\}=\begin{cases} 1 & \text{ if } x \leq x_i \\ 0 & \text{ if } x \gt x_i \end{cases}$By definition, the EDF function computes the cumulative distribution of the underlying random number.Why do we care?The EDF estimates the true underlying cumulative density function of the points in the sample; it is virtually guaranteed to converge with the true distribution as the sample size gets sufficiently large.ProcessFirst, let’s organize our input data. We can start by placing the values of the sample data in a separate column. The sample may contain one or more missing values.Now we are ready to construct our EDF Plot First, select the empty cell in your worksheet where you wish the output table to be generated, then locate and click on the “Descriptive Statistics” icon in the NumXL tab (or toolbar). Then, select the “Empirical Distribution Function” item from the drop-down menu.The EDF Wizard pops up.Select the cells-range for the values of the input variable.Notes:The cell range includes (optional) the heading (“Label”) cell, which would be used in the output tables where it references those variables.By default, the output table cells range is set to the currently selected cell in your worksheet.By default, the output graph cells range is set to the 7 cells right of the currently selected cell in your worksheet.Finally, once we select the input data (X) cells range, the “Options” and “Missing Values” tabs become available (enabled).Next, select the “Options” tab.Initially, the tab is set to the following values:“Overlay Normal distribution” is checked. This option in effect instructs the wizard to generate a second curve for the Gaussian distribution for comparison purposes. Leave this option checked.Now, click on the “Missing Values” tab.In this tab, you can select an approach to handle missing values in the data set (X’s). By default, any observation with a missing value would be excluded from the analysis.This treatment is a good approach for our analysis, so let’s leave it unchanged.OutputNow, click “OK” to generate the output tables.Notes:The values of all observations are sorted in ascending order and placed in column E.The X-Bar and Y-Bar columns carry no special statistical meaning; they are merely computed to assist us in generating. Download Now! NumXL 1.68.7.1. Home; Windows; Business; Office Add-ins; NumXL; NumXL 1.68.7.1. Download Now! Direct link. NumXL is a Microsoft Excel add-in for

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Scene 1:Hello and welcome to the exponential smoothing tutorial series. In this tutorial we will resume the demonstration of Brown's linear exponential smoothing function, and use the built-in optimizer in NumXL. For the sample data we are using the same weekly demand volume data of a hypothetical product.Let's begin by selecting cell D9. examine the cell formula in the formula toolbar. Notice we already have a call for the linear exponential smoothing function, so press F2 to edit. Click on the FX button found on the left side of the equation toolbar, this will invoke the function arguments dialog box for the linear exponential smoothing function.Scene 2:Notice that the cell D2, is used for the optimize function, so we don't need to change the formula just the value in D2 to turn on the optimizer. Please click cancel to go back to your worksheet.Scene 3:Select the D2 cell and type in true or 1 then hit enter when done.Scene 4:Since we have the automatic calculation on all of the values of the smooth time series are recalculated using optimal smoothing factor known as alpha. Notice the change in mean squared error mean, absolute scaled error and symmetric mean absolute percentage error.The calibrated linear exponential smoothing has a 21 percent lower mean absolute error than that of a naive reference model. Note that the function calculates the optimal value for alpha using the available information or data as a training set. The available data increases with time so the function calculates a new value each time.What about the alpha value in D1? Well when the optimizer flag is turned on the function uses the value of alpha as a starting value for the optimizer.Now let's examine the value of the smoothing parameter at each period. Select the E10 cell and start typing in the function =LESMTH(. Click on the FX button found on the left side of the equation toolbar, this will invoke the function arguments dialog box for the linear exponential smoothing function.Scene 5:Then specify the input cell range, this is the same cell range we used earlier for the forecast in column D. Lock the starting cell in the input range by pressing F4, enter a value of true or one in the order field.Scene 6:For the initial value of alpha let's use the value in D1, then lock the cell by pressing F4. For the optimized switch let's use the value in cell D2 up the cell by pressing F4.Scene 7:Set the forecast time to zero or set the value in cell A10, and lock the cell for column movement. For the return type in one for returning the value of the smoothing parameter.Scene 8:The built-in optimizer requires a few non-missing observations to run or it will return the starting alpha value as it is the case here.Scene 9:Now copy the formula to the cells below it.Scene 10:The value of the smoothing parameter at each period is calculated, let's plot the values of the smoothing parameter versus time.