network profile
| bio | website | |
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| location | ||
| age | ||
| visits | member for | 1 year, 10 months |
| seen | Jan 17 at 17:04 | |
| stats | profile views | 13 |
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Aug 8 |
awarded | Yearling |
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Nov 22 |
answered | Why is an inverted yield curve a problem? |
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Oct 25 |
revised |
How many explanatory variables is too many? added 144 characters in body |
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Aug 15 |
comment |
How many explanatory variables is too many? The bullet point after "within the range of the data" means don't extrapolate. That's different than out-of-sample. Key point there is does not affect $\hat{y}$ .... |
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Aug 15 |
comment |
How many explanatory variables is too many? "The holdout sample won't necessarily catch multicollinearity" is precisely right. And irrelevant for prediction. A random Google search confirms, with among others "If interest is only in estimation and prediction, multicollinearity can be ignored" public.iastate.edu/~alicia/stat328/Model%20diagnostics.pdf |
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Aug 15 |
comment |
How many explanatory variables is too many? The point is that you arrived at humidity down/uptown by trying out a bunch of regressors and chancing on one that fit that dataset. Therefore, the holdout sample is unlikely to show the regressor as being good, and you'll see the problem. If it does work in the holdout sample, maybe you've just found a new regressor that actually does work. It doesn't matter whether it's causal or not--for prediction covariance is good enough as long as it consistently covaries. |
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Aug 15 |
comment |
How many explanatory variables is too many? You want to know how it performs out-of-sample. Therefore you agree in advance that when developing your model you will only use, say 2/3 of your data. That tricks nature into giving you 1/3 of your data as out-of-sample data that you actually can observe. This is a common technique in predictive applications, and it works quite well when you've got a lot of data--as is likely the case here. |
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Aug 14 |
comment |
How many explanatory variables is too many? This is a good start, but ultimately it's still in-sample. If what you're interested in is prediction, it's hard to beat actually predicting out-of-sample and seeing how good it gets. Since you don't care about the coefficients just the results, avoiding multicollinearity isn't really the answer here. |
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Aug 13 |
answered | How many explanatory variables is too many? |
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Aug 11 |
awarded | Self-Learner |
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Aug 10 |
answered | What data sources are available online? |
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Aug 10 |
awarded | Scholar |
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Aug 10 |
accepted | Closed-form formula for approximate maximum duration of a bond? |
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Aug 10 |
awarded | Teacher |
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Aug 10 |
comment |
Modified Durations of Different Noncallable Bonds and function of Maturity The horizontal asymptote will always be at duration=(1 + 1/i) where i is the current market interest rate expressed in proportion terms (e.g. i=.05 for a 5% rate). |
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Aug 10 |
comment |
Modified Durations of Different Noncallable Bonds and function of Maturity See the answers to this question about how to calculate the maximum duration for some more references and graphs: quant.stackexchange.com/questions/1624/… |
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Aug 10 |
revised |
Closed-form formula for approximate maximum duration of a bond? added 7 characters in body |
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Aug 10 |
revised |
Closed-form formula for approximate maximum duration of a bond? solution |
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Aug 9 |
revised |
Closed-form formula for approximate maximum duration of a bond? added 9 characters in body |
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Aug 9 |
comment |
Closed-form formula for approximate maximum duration of a bond? Thanks for this. I'm still sorting through all this, but in re-reading both the Pianca paper and the Hawawini paper I am starting to realize that my previous lack of progress on this issue results from my assumption that the closed-form formulae for duration were approximations. By contrast, Pianca seems to use them as though they are exact. I'll have to work through the derivations and figure out whether they are, in fact, exact. If so, I can likely avoid the whole iterative algorithm and just go directly from maturity to duration. |
