1 vote
45 views

interpreting results of a log-linear regression [duplicate]

I am running a log-linear regression model, using the Fatalities data set, where estimated regression is an outcome variable (Fatalities) and contains fixed effects for the year and state. I am taking ...
• 111
231k views

What is the reason why we use natural logarithm (ln) rather than log to base 10 in specifying function in econometrics?

What is the reason why we use natural logarithm (ln) rather than log to base 10 in specifying functions in econometrics?
44k views

Regression: Transforming Variables

When transforming variables, do you have to use all of the same transformation? For example, can I pick and choose differently transformed variables, as in: Let, $x_1,x_2,x_3$ be age, length of ...
• 7,332
19k views

How to calculate confidence interval for a geometric mean?

Apologies if this is confusing at all, I'm very unfamiliar with geometric means. For context, my data set is 35 month-end portfolio values. I found the month to month growth rate [Month(N)/Month(N-1)] ...
• 123
8k views

Why can I interpret a log transformed dependent variable in terms of percent change in linear regression?

Looking at resources such as this one and this one, you see claims like "Exponentiate the coefficient, subtract one from this number, and multiply by 100. This gives the percent increase (or ...
• 484
1 vote
4k views

Should I use 'median' instead of 'mean' for nonparametric distributions?

I have a dataset that I determined was nonparametric. If I want to do calculations like '% change', and find the average percentage change, should I really find the median percentage change instead of ...
• 31
564 views

Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$

I have a time series $x_t$. If I use the transformation $u_t = log(x_t) - log(x_{t-1})$, my new time series $u_t$ has properties of white noise (random). I wonder whether there is any practical ...
• 2,184
1 vote
2k views

Cholesky Shock - Interpretation of logs in IRF Models

I came across a few articles here and there that conclude: When the data (say variables X, Y) for an impulse response function are on log level, the y-axis depicts the % response of Y to a 1% shock ...
Consider we have a population regression function (log-level model) with only one independent variable: $$\log(y) = B_0 + B_1 \times x_1+u$$ In order to find the relationship between the increase of \$...