Estimating missing values with kernel regression

Check out this video to learn how to estimate values for datasets with missing values using the NumXL non-parametric kernel regression function in Microsoft Excel. We'll use the Gaussian kernel weight function in the demonstration and instruct NumXL to calculate the optimal kernel bandwidth.

Video script

Scene 1:

Hello, and welcome. In this demo, we will use the non-parametric NumXL kernel regression function to calculate values for observations with empty or missing values.

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First, add the X values we wish to calculate in the adjacent column.

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Next, select the top cell in the column adjacent to the X-values and type NxKREG in the formula bar. Then, press the FX button on the left of the Excel function.

Scene 4:

The Excel function arguments Window pops up.

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For the X-argument, select the cells in column A.

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For the Y-argument, select the cells in volume B.

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For the polynomial order argument, type two(2) for quadratic.

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For the kernel weight function argument, type six(6) for the Gaussian kernel.

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For the kernel bandwidth argument, type one or leave it empty.

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For the optimization switch argument, type one(1) or true to compute an optimal kernel bandwidth value.

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Next, select the cells with the desired x values in column D for the Target argument.

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Finally, enter zero(0) or leave it blank for the return type argument to return the fitted (AKA) forecast values.

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Now, press the OK button.

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Here you go. The function returns all fitted values using the kernel weights function with optimal bandwidth.

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Great. Check out the fitted values and original data points on one graph.

That is it for now. Thank you for watching!



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