Timeline for Meaning of Additively Separable, Linear in X
Current License: CC BY-SA 3.0
14 events
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| Apr 13, 2017 at 12:51 | history | edited | CommunityBot |
replaced http://economics.stackexchange.com/ with https://economics.stackexchange.com/
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| Apr 9, 2017 at 18:50 | comment | added | Alecos Papadopoulos | @FrankSwanton This is very fundamental: it has to do with applying the "principle of analogy", "as in population, so in sample". It is justified because we are looking for samples representative of the population. If they are, then it must be the case that what holds at population level, should ~hold at sample level. | |
| Apr 9, 2017 at 18:50 | comment | added | Frank Swanton | @AlecosPapadopoulos Yes, I was going to in CrossValidated, but I have 1 reputation. Let me try to post there, in the meantime any reference article recommendation would be great. Thanks. | |
| Apr 9, 2017 at 18:48 | comment | added | Alecos Papadopoulos | @FrankSwanton It would be better to formulate this questions as a new Question here or better, over at CrossValidated, since these are statistical foundations rather than econometrics. | |
| Apr 9, 2017 at 18:48 | comment | added | Frank Swanton | @AlecosPapadopoulos Any good reference article regarding the concept of "matching population moments with sample moments" in the context of econometrics would be helpful. Not sure if Wooldrige or the like has something like that. | |
| Apr 9, 2017 at 18:44 | comment | added | Frank Swanton | @AlecosPapadopoulos In particular, because we start off with the classical linear regression model, then make a leap to relaxing those assumptions and jump to ergodic stationarity to deal with time series, I have a challenging time patching all these in a nice way... Would appreciate your help. | |
| Apr 9, 2017 at 18:43 | comment | added | Frank Swanton | @AlecosPapadopoulos Alecos, I came across your response in explaining Method of Moments in stack exchange and wonder if I could ask few questions. Basically, I am trying to conceptually understand what Hayashi is keep mentioning in his Chapter 3 where he states something like "orthogonality conditions mean s set of population moments are all zero and MoM principle is to choose the parameter estimate so that corresponding sample moments are also equal to zero." I think I kind of understand this when he shows with equations, but don't really understand conceptually. | |
| Apr 6, 2017 at 2:51 | vote | accept | Frank Swanton | ||
| Apr 1, 2017 at 17:56 | history | edited | Alecos Papadopoulos | CC BY-SA 3.0 |
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| Apr 1, 2017 at 17:39 | comment | added | Alecos Papadopoulos | @Oliv This is a nice addition. I only mention that additive separability touches also relations that do not have to do with preferences (e.g. factors of production). | |
| Apr 1, 2017 at 16:22 | comment | added | Oliv | To complete this answer, it might be worth noting that the behavioral implications of additive separability are: \begin{equation} \text{ for all } (x,y,z,w), (x,y) \succ (z,y) \Leftrightarrow (x,w) \succ (z,w) \end{equation} and \begin{equation} \text{ for all } (x,y,z,w), (x,z) \succ (x,w) \Leftrightarrow (y,z) \succ (y,w) \end{equation} This means that preferences over argument 1 are independent of the value of argument 2, and vice versa, which gives behavioral content to your point that there is no ``cross'' effect. | |
| Apr 1, 2017 at 16:17 | history | edited | Alecos Papadopoulos | CC BY-SA 3.0 |
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| Apr 1, 2017 at 15:42 | history | edited | Giskard | CC BY-SA 3.0 |
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| Apr 1, 2017 at 15:30 | history | answered | Alecos Papadopoulos | CC BY-SA 3.0 |