Stata tip 137: Interpreting constraints on slopes of rank-deficient design matrices

A rank-deficient design matrix of explanatory variables X is not of full-column rank when there is one or more linear dependencies, meaning that X ′ X is singular and its inverse does not exist; thus, there is no unique solution to b = (X ′ X)⁻¹ X ′ y. Rank deficiency is sometimes referred to as “perfect collinearity”.

There are two ways to enable the use of X in regression analysis. If there is one linear dependency, then the standard approach is to reduce the dimension of X by identifying a zero-parameter constraint on one of its columns. This is the default treatment in Stata and other software, that is, to arbitrarily remove one column of X.

The alternative approach is to expand X by adding an extra column through the identification of a linear constraint across the parameters. Then, X can be used in constrained least squares via the Stata command cnsreg. Both approaches yield the same fully identified model, but the interpretation of their estimated coefficients depends entirely on the imposed constraint.

Much of the relevant econometric literature focuses on the identification of rankdeficient matrices of mutually exclusive binary variables and the interpretation of their intercepts, a problem so ubiquitous and well understood that it has earned its colloquial moniker of “dummy variable trap”. The interpretation of constrained intercepts is indeed elementary because it is a simple matter of weighted constants.

However, as I discuss in Christodoulou (2018), a more rigorous discussion on the effect on slope coefficients of a rank-deficient X seems to evade the literature. When b involves slope coefficients, the reduction of X by imposing zero-parameter constraints or the expansion of X by imposing linear constraints amounts to an imposition of a structural relation on the parameters to be estimated. The interpretation of the constrained slopes then becomes conditional on the validity of the structural constraint.

Consider the question of how capital investment in operating assets affects sales revenue in fixed asset-intensive firms. Companies with high stakes in tangible assets rely on capital investment to boost revenue, but the more the assets are used in operations, the more their value is depleted and needs to be replenished. The economic transactions describing this relation are captured by the accounting identity

${\text{ppe}}_{it}{ }_{-\text{1}}+{\text{ cpx}}_{it}-{\text{dep}}_{it}\equiv {\text{ppe}}_{it}$

1

or equivalently stated as $\Delta pp{e}_{it}\equiv cp{x}_{it}{ }_{+1}-de{p}_{it}{ }_{+1}$ , where ppe _t is the stock in property plant and equipment, cpx _{t +1} is new capital expenditure, and dep _{t +1} is depreciation plus other events that may deplete assets, such as the sale of assets. Naturally, increases in capital stock are expected to bring more sales. Indeed, one could argue that the variation in the period’s sales could be explained by the average capital investment used from t to t + 1.

The following simple simulation generates data that describe this scenario:

The parameters of the random normal distributions are selected so that they appear somewhat realistic, considering that these are a result of log-transformations from originally log-normally distributed variables.

Let’s say that someone is interested in learning how much revenue would change if a company decides to spend more in new capital expenditure and, at the same time, how depletion would affect sales, conditional of course on the capital investment stock. Then, the variation of sales revenue could be written as a function of the structural relation of (1) plus a random-error term:

${\text{sales}}_{it}=a+b_{\text{1}}{\text{ppe}}_{it}{ }_{-\text{1}}+b_{\text{2}}{\text{cpx}}_{it}+b_{\text{3}}{\text{dep}}_{it}+b_{\text{4}}{\text{ppe}}_{it}+{\epsilon }_{it}$

2

Given the rank deficiency in X, Stata will estimate this regression by imposing a zero-parameter restriction to one of the explanatory variables and also issue a warning that a variable was omitted because of perfect collinearity:

Stata decided to drop the variable dep1, but this could have been another explanatory variable; for example, changing the seed to 1235 would drop cpx1. The interpretation of the remaining estimated slope parameters depends on the validity of the zero-parameter restriction on b ₃ = 0 on dep1, a highly doubtful assumption even with real data. Let’s see what happens when we estimate all competing specifications with zero-parameter constraints, that is, each time omitting one explanatory variable:

Note how the magnitudes of the estimated slopes switch place depending on the variable that is omitted from estimation. This is because each restriction sways estimation so that the collection of all estimated slopes remains parallel to the null vector that describes the linear dependency (for an illustration, see figure 1 in Christodoulou [2018]). This sort of behavior makes any discussion on marginal effects entirely meaningless.

Such ad hoc imposed constraints, whose only purpose is to enable mere estimation, are dangerous practices when applied on rank-deficient design matrices involving slope coefficients. A zero-parameter restriction on a slope suggests a zero marginal effect, and in this case such restrictions are simply untenable.

Another way to enable estimation is to expand X by imposing a linear constraint that specifies a structural relation across all parameters. For example, one could suggest that (2) behaves like a homogeneous function of some degree. The structure of the data does not allow us to estimate which degree this is, so we need to assume the degree as a constraint. We could claim that the slope coefficients add to some fixed c, thus effectively imposing a homogeneous function of degree c, meaning that a fixed change in all independent variables would change the dependent variable by that value raised to the power of c.

For example, for c = 0, a fixed change would result in no change in the dependent variable. For c = 1, a fixed change would result in a linear change in the dependent variable, or what the economists call a “constant return to scale” within the right context; for c < 1, we have decreasing returns to scale, and for c > 1 we have increasing returns to scale. Consider the following examples:

The option collinear tells Stata to keep perfectly collinear variables, thus ensuring reporting of all estimated coefficients.

Note how the addition of all estimated slopes is always the same, at $\widehat{b_{\text{1}}}+\widehat{b_{\text{2}}}+\widehat{b_{\text{3}}}+\widehat{b_{\text{4}}}=0.\text{1}0\text{449514}$ , regardless of the imposed constraint. This is the same constant to the addition of the coefficients as with the estimates with zero-parameter constraints, as above. Regardless of the constraint, the coefficients must add up to the same constant.

The coefficients are simply scaled up or down by a fixed amount as c changes. This means that because the constraints are needed for identification, the rank-deficient nature of the data does not allow one to say which structural constraint is most appropriate. One must assume it.

The model with the homogeneous function of degree zero, with c = 0, can also be fit using the Moore–Penrose pseudoinverse (for example, see Mazumdar, Li, and Bryce [1980]; Searle [1984]), using the pinv() Mata function as follows:

These are identical coefficients to those reported in the table just above under the heading c0, in that order. Using the Moore–Penrose pseudoinverse, we can recover every other solution that is parallel to the null vector. Given that there are four coefficients, k = 4, then the imposition of an assumed degree for the homogeneous function c must be equally allocated across the k coefficients. For instance, for k = 0.75, it holds that

and similarly for any other c. Similarly, because the Moore–Penrose pseudoinverse gives the solution for c = 0, we could use this result to see what would be the set of estimates for any given zero-parameter restriction. Here is the case of the zero-parameter restriction on the coefficient of ppe _{it −1}, which is the same as that reported in the first column of the first estimates table above:

Finally, an important note about standard errors—they remain the same across all specifications. As shown in Greene and Seaks (1991), the individual standard errors of regressions involving rank-deficient design matrices are no longer informative. We cannot speak of coefficient-specific statistical significance. For example, in the first table of estimates reported, the coefficient on cpx1 appears as statistically insignificant with a p-value of 0.488. This standard-error estimate is of course nonsensical, given the nature of the simulated data. In specifications with rank-deficient design matrices, we can speak only about the fit of the overall model, as in the root mean squared error and the estimated log likelihood, which remain identical regardless of the type of constraint. There is no such thing as coefficient significance.

In Christodoulou and McLeay (2014, 2019), we use Stata to explain how this lack of insight has proven to be an acute problem in financial research that relies on inputs from the rank-deficient accounting data matrix of articulated financial statements. Accounting data, governed by a double-entry data-generating process whereby a transaction is recorded twice, is purposefully designed to be rank deficient of order one. This is a matter of structural nonidentification and requires the additional specification of a suitable constraint to enable estimation. If the constraint is arbitrarily imposed, then inference is entirely useless.