fixing the intercept in multiple regression I am interested in using OLS regression to model the relationship between two predictors (X1 and X2) and a response variable (Y).  However, for theoretical reasons I know that when X1 = 0, Y must also equal 0. Furthermore, for theoretical reasons I also know Y must be positively related to X1. So I expect a positive correlation between X1 and Y, but I don’t know the value for the slope, or the strength of the relationship. Indeed, what I am most interested in is whether X2 is related to Y, once the variation explained by X1 is taken into account. So I have been considering fixing the intercept to run through the origin. I know from reading other posts that fixing the intercept in this way leads to a number of issues (e.g. high r2 values). However, because I know that the regression line must run through the origin, a model which doesn't do this seems unsatisfactory. 
This leads me to my main query – my limited understanding of regression would have me believe that in fixing the intercept at zero, I am also specifying that the intercept for the relationship between Y and X2 is zero (R code: lm(Y ~ 0 + X1 + X2)). Is this right? If so, then then the approach seems flawed, because I don’t have prior knowledge about the relationship between Y and X2. Could people suggest an alternative approach?
Thank you very much in advance for any help, and I hope my question was clear/made sense.
NB The response variable y is bounded between 0 and 1. But it is not the product of trials with discrete outcomes (actually it is the value for an index), so I have been considering using linear regression with angular transformation of the response, rather than a binomial glm.
 A: As @Dougal was pointing out, by excluding the intercept, you have forced 
$\hat Y = 0$ if $X_1 = X_2 = 0$, which is not the same as forcing $\hat Y = 0$ if $X_1 = 0$. 
Further more, one should be very, very cautious in deciding to exclude an intercept, even if we are sure that $E[Y|X = 0] = 0$ (let's just consider one covariate for a moment to simplify this explanation). For example, suppose we know that $E[Y|X = 0] = 0$, but the relation between $X$ and $Y$ is not exactly linear. Then if we include an intercept (that may then become statistically significantly different than 0), at least we have a first order approximation of the relation between $X$ and $Y$, centered about the mean of our $X$. We should be wary about making estimates for values of $X$ that are far from the center, but we should already be afraid of that anyways (i.e. extrapolating), and we should get reasonable estimation for values of $X$ relatively close to it's mean. But if we leave out the intercept and have a non-linear relation, the only values of $X$ where we can expect reasonable estimation is close to $X = 0$ (and even then, we don't actually have an approximation of the change in $Y$ given $X$ when $X$ is close to 0 unless most of our data was centered around 0). This is then a waste of effort, because apparently we already knew that $E[Y|X = 0] = 0$ for some reason. 
Plus, estimating an intercept only costs you one degree of freedom, so there's not much loss in estimating it. If your relation between $X$ and $Y$ is truly linear, and $E[Y| X = 0] = 0$ is really true, by not estimating the intercept, we basically get one more observation. Not a big deal for reasonably sized datasets (especially when compared to the cost of robustness to assumptions). 
