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  • 0 posts edited
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  • 19 votes cast
Oct
2
comment Signing a Derivative of an Expectation
@joriki Thanks!
Oct
2
accepted Signing a Derivative of an Expectation
Sep
7
asked Signing a Derivative of an Expectation
Aug
18
comment How to compare experimental data with teorethical prediction
This question is not clear. Is there a theory as to what the coefficient on each polynomial coefficient should be? What is it you want to test?
Jul
27
comment combining multiple regression outputs
If neither $\beta$ nor $\alpha$ depends on $t$ then, in theory at least, all results should be equal to the true approximation. The reason is because you've effectively drawn a random sample for each regression. In that case, all you need to do to recover $\beta^*$ is take the average and the only weighting would likely be a function of the sample sizes.
Jul
16
comment Approximate/Find Function
Kernel Regression
Jul
9
comment Underlying utility function behind a linear two-product demand curve
My guess is that in the second case you automatically assumed the budget constraint is binding, but that in fact depends on the value of $m$.
Jul
7
comment Identification of non-linear functions:polynomial+exponential
Maybe you wrote something wrong in the question, but it seems to me the answer is clearly $\alpha_2 = \alpha_1 = \alpha_0\ = \beta=0$ Or is that supposed to be a $Y$ on the RHS
Jul
7
comment Comparing Percentiles of 2 Samples Drawn from the Same Distribution
Interesting, thanks! Just so I'm clear, the discontinuities in the graph occur at the points where $np$ is a whole number, because at the next value for $n$ we round all the way up.
Jul
7
accepted Comparing Percentiles of 2 Samples Drawn from the Same Distribution
Jul
7
asked Comparing Percentiles of 2 Samples Drawn from the Same Distribution
Jul
2
answered Significance of dummy variables in probit regression
Jul
1
comment Bayesian Updating - plug in previous posterior for prior?
Just to be clear, is the new information the entire sequence or just the terms indexed by $n+1$, e.g. $a_{n+1}$ and $b_{n+1}$
Jul
1
comment Least squares with known error in y
Measurement error in $y$ variables does not bias regression coefficients (i.e. in $x$). So, at least in theory, it should not make a difference. If that result is affected by a small sample size, I can't say for sure.
Jun
28
answered In a simple regression model estimated using OLS, the covariance between the estimated errors and regressors is zero by construction
Jun
28
comment In a simple regression model estimated using OLS, the covariance between the estimated errors and regressors is zero by construction
Yes, its true. Intercepts do not matter.
Jun
28
comment Polynomial least squares fit — restrictions on order?
The example in the link you provided is solving a simple regression with a constant term and a variable x. ($y=a+bx$) This is a regression with 2 independent variables. That's why the matrix is 2x2. (Using the formula in my previous comment, X'X is a 2 x 2 matrix). But, you want to know (or at least I think you want to know) largest order you can include in a regression of y on x, $x^2$, $x^3$, and so forth. This will require solving higher order matrices. Edit: Here's a better link. mathworld.wolfram.com/LeastSquaresFittingPolynomial.html
Jun
28
comment Polynomial least squares fit — restrictions on order?
The standard solution in a linear regression is $(X'X)^{-1}X'y$ where X is an $ N x K$ matrix with N observations and K parameters. In order to be valid the matrix $X'X$ must be invertible. This requires , by definition, that $N \geq K$.
Jun
27
revised Polynomial least squares fit — restrictions on order?
added 148 characters in body
Jun
27
answered Polynomial least squares fit — restrictions on order?