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R. Kelley Pace
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| LREC Endowed Chair of Real Estate |
| Department of Finance, Louisiana State University |
| Baton Rouge, LA 70803-6308 |
| OFF: (225)-578-6256, FAX: (225)-578-6095 |
| kelley@pace.am, www.spatial-statistics.com |
| James P. LeSage |
| Department of Economics, University of Toledo |
| Toledo, OH 43606 |
| www.spatial-econometrics.com |
| KEYWORDS: spatial statistics, spatial autoregression, nearest neighbor, maximum likelihood, sparse matrices, log determinant bounds, matrix determinant approximations, doubly stochastic, spatial data mining.
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We address this issue using an overparameterized model of spatial dependence that assumes a monotonic decline in spatial dependence with the order of the neighboring observations. Estimation of the model relies on the intuition that bounds to the log-determinant term in the log-likelihood can provide penalties to overfitting both the level and pattern of spatial dependence. In our approach, the overall weight matrix results from a convex combination of spatial basis matrices. The term spatial basis matrices arises by way of analogy with B-splines, that employ linear combinations of basis vectors to produce a vector with a flexible form (see Härdle (1990) for more information on splines). Similarly, linear combinations of spatial basis matrices can produce a spatial weight matrix with a flexible form. Each spatial basis matrix reflects a different weight specification assigned to neighboring observations. The spatial basis matrices used in the linear combination are symmetric and doubly stochastic (each row and column sums to 1), resulting in an overall spatial weight matrix that is both symmetric and doubly stochastic. Symmetric and doubly stochastic weight matrices have a number of appealing properties that facilitate semiparametric modeling of spatial dependence. We derive a mathematical expression for bounds on the log-determinant term that appears in the likelihood function for this symmetric doubly stochastic weight matrix. Conveniently, the bounds can be expressed as a function of a weighted sum of the traces of pairwise products among the spatial basis matrices. From a computational standpoint, calculating these traces does not require forming the pairwise products explicitly, so solving for maximum likelihood estimates at the bounds becomes a simple optimization exercise involving two quadratic forms based on small matrices (e.g., 23 by 23). An intuitively pleasing aspect of our approach is that the objective functions (bounded log-likelihoods) contain one quadratic form equal to the sum-of-squared errors that measures the quality of fit, and another quadratic form that reflects a penalty to overfitting spatial dependence. Overfitting spatial dependence can arise from overfitting the level of spatial dependence (holding the pattern constant) as well as fitting an overly flexible pattern of spatial dependence (holding the level of dependence constant). Again, this bears resemblance to the type of objective functions used in fitting some forms of splines in nonparametric regression. The ability to observe semiparametric estimates of the weights given to neighboring observations at both the lower and upper bounded log-likelihood provides a feel for the nature and extent of possible spatial dependence that may prove useful in estimation and inference, or as an exploratory tool. We label our approach the “Doubly Spatial Model” to emphasize the double bounding of the log-likelihood and the use of doubly stochastic spatial weight matrices. In addition to the advantages noted above, inference can be simplified as well, while remaining in the traditional likelihood framework. Given upper and lower bounded log-likelihoods for a model and any restricted sub-model, one can carry out likelihood ratio tests that take into account the range of the Jacobian term. In some cases, one can reject or fail to reject a null hypothesis with the same validity as in cases where the true log-likelihood is known. For example, suppose the unrestricted lower bound log-likelihood is -1000 and the unrestricted upper bound log-likelihood is -900. In addition, assume that imposing one restriction causes the restricted lower bound log-likelihood to fall to -1300 while the restricted upper bound log-likelihood falls to -1200. This difference of 200 between the lower bound of the unrestricted log-likelihood and upper bound of the restricted log-likelihood implies that the difference between the true unrestricted and true restricted log-likelihoods must equal or exceed 200. Such a difference certainly exceeds the threshold required for significance of a single hypothesis. A notable aspect of our approach is that we can carry out inference that considers the range of the Jacobian term in the problem, without explicit computation of the Jacobian term, which greatly accelerates computation of estimates. To demonstrate scalability of the technique, we semiparametrically estimate the spatial dependence for a housing model using 57,647 observations. Section 2 develops log-determinant bounds as a function of individual basis matrices and sets up the optimization problem. A small dataset involving 506 observations is analyzed in section 3 to examine our approach in a setting where true likelihood function values are known. We show that weights based on the bounds enclose the true weights. Section 3 also provides an application of our semiparametric estimates to a housing model using all continental US census tracts. Section 4 concludes with the key results. 2 The Doubly Spatial Model 
observations and thus its specification and properties greatly affect spatial model computation, estimation, as well as inference.
Definition 1. Let  represent a sequence of  individual nearest neighbor weight matrices such that  if observation  is the  -th nearest neighbor to observation   and 0 otherwise  . Let
 -th spatial basis matrix where  represent the weights given to the 1st, 2nd, and up to the th neighbor and  is a diagonal by matrix that insures that  is doubly stochastic (i.e.,  and  where  is a column vector of ones). Furthermore, assume that:
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Define the overall spatial weight matrix  as a combination of the  primitive spatial basis matrices:
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where:
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Finally, let  represent a by matrix containing all possible traces of pairwise multiplications of the basis matrices. Specifically,  for  .
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The diagonal elements of each spatial basis matrix 
equal 0 to preclude observations from directly influencing their own predictions in the statistical model. By the definition of a symmetric doubly stochastic matrix, and , where is a column vector of ones. Since is comprised of a limited number of very sparse individual neighbor matrices, it is also sparse. Note, the weights 
used in constructing stay the same or decline in value with the order of neighbors, giving a monotonic character. In summary, each of the spatial basis matrices is a sparse, doubly stochastic, symmetric, non-negative, by matrix with a trace equal to 0. The overall spatial weight matrix inherits these properties as well. See Bavaud (1998), Griffith and Lagona (1998), Tiefelsdorf, Griffith, and Boots (1999) for recent discussions concerning spatial weight matrices. A numerical example may help clarify construction of 
Let 
represent a spatial basis matrix with equal weight given to the four nearest neighbors. 
represent a spatial basis matrix where different weights are given to the four nearest neighbors. 
and 
equal 0.5, we can define 
and are doubly stochastic and symmetric, as is the overall example spatial weight matrix 
The functions to produce these doubly stochastic scalings as well as to estimate various spatial models reside at www.spatial-statistics.com or www.spatial-econometrics.com. Turning attention to the character of the weights underlying the spatial basis matrices, Figure 1 shows a plot of the weights 
associated with 
. Each of the continuous lines in Figure 1 depicts weights for one of the 22 spatial basis matrices. For example, the line extending away from 0.25 represents weights for four nearest neighbors receiving 0.25 each (similar to 
The fifth nearest neighbor has a weight of 0 which accounts for the segment connecting 0.25 at neighbor 4 to 0 at neighbor 5. Some of the weights in the figure exhibit a linear decline with the order of neighbor, while others take on constant values over a range of neighbors. Obviously, a number of shapes can be produced by assigning different weights to the basis matrices. In implementing the doubly spatial model, users are of course free to select weights other than those employed here. Reliance on doubly stochastic matrices is not inconsistent with the usual approaches to defining spatial weight structures because any symmetric matrix with enough positive entries in each row and column can be transformed to doubly stochastic form. This is accomplished using iterative scaling of each row and column by a diagonal matrix containing the inverse of the square root of the row sums (which equal the column sums). The use of too few neighbors can however lead to a matrix for which no scaling will yield a doubly stochastic matrix. This might be a problem in situations where, for example, observations are located along a major transportation route and only one or two neighbors are used. For the case of a single neighbor, the weight matrix is asymmetric so the techniques described here do not apply. For computationally efficient solutions to single neighbor problems based on a closed-form solution see Pace and Zou (2000). The case of two symmetric neighbors is also unlikely to permit doubly stochastic scaling, but problems involving a few neighbors (e.g., edge sites, roads, and peninsulas) can take advantage of sparsity using the methods described in Pace and Barry (1997) to produce rapid log-determinant computations. We note that problems encountered with doubly stochastic scaling for small numbers of neighbors provided the motivation for use of the basis matrix approach described here. The most sparse basis matrix used here contains four neighbors, but we have been able to form doubly stochastic spatial weight matrices for typical contiguity matrices constructed using Delaunay triangles. It appears that the benefits of doubly stochastic scaling can be combined with the interpretive benefits associated with the usual contiguity matrix. See Bapat and Raghavan (1997, p. 261-263) for more on doubly stochastic scaling algorithms. Finally, combining different matrices to model dependence has been employed in statistics generally and in spatial statistics specifically (e.g., Streitberg 1979, Cressie 1993, p. 94). Discontinuities and problems of ensuring positive definiteness sometimes impede these attempts. The doubly stochastic scaling automatically ensures positive definiteness of 
. The identical scaling of all the matrices, the quadratic log-determinant bounds, and the use of a grid to set the overall level of dependence appear to have made this easier than the general problem. 2.2 Log-determinant Bounds
with eigenvalues 
and 
, and 
 | (3) |
 and  since  equals the first two terms of the series (given  while  for  , since is nonnegative. Hence,  and thus the upper bound is proved.
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For the lower bound, symmetry of implies real eigenvalues  such that  for  . Even powers have all nonnegative eigenvalues since  for positive integer . Recall the trace equals the sum of the real eigenvalues,  and for positive integer  ,  Since the maximum value for  is 1 for any positive integer and since ,  .
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In particular,  . Since  . However,  . Moreover,  . Hence,  and thus the lower bound is proved. QED.
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Since  and meets the conditions of Proposition 1, the Corollary is proved. QED.
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In terms of implementation, 
where 
denotes elementwise or Hadamard multiplication. This requires a maximum of 
operations (there is a maximum of 
elements in each row of 
, there are rows, and sparse matrix methods avoid unnecessary multiplications of the zero elements). As a result, computation of 
requires 
operations, since the cyclical redundancy of the trace requires only 
basis matrix traces. Note also that given , computation of for another set of weights requires only 
operations. While we mainly dwell upon the utility of (3) for more complicated combinations of spatial weight matrices, one could employ the bounds to estimate conventional single weight matrix models. An anonymous reviewer estimated the Cressie (1991, p. 386-389) SIDS data using the exact log-determinant, two versions of the approximation proposed by Griffith and Sone (1995), and an approximation based upon the average of the lower and upper log-determinant bounds from (3). The reviewer obtained estimates of 0.4958, 0.5041, 0.4982, and 0.4792 using these approaches. Insofar as the crude approximation based upon the average of the lower and upper log-determinant bounds from (3) comes reasonably close to the exact estimate, this suggests a potential exists for other simple approximations such as the ones proposed by Griffith and Sone (1995), Martin (1993), as well as Barry and Pace (1999). Whatever approximation used, (3) can provide bounds on the accuracy of the approximation. Note, the bound computations (and also using an average of the bounds as an approximation) for a single weight matrix requires fewer operations than these other, typically more accurate, approximations. In fact, the use of these bounds and an approximation comprised of an average of the bounds can lead to a closed-form solution for the mixed regressive spatially autoregressive estimates at the bounds as well as the approximation. Specifically, the bounds lead to a cubic first-order condition and the approximation comprised of the average of the bounds leads to a quartic first-order condition and these all have closed-form solutions. The speed of these solutions could help in spatial data mining applications. Finally, one could use some of these simple approximations and bounds to estimate spatial autoregressions using commercial statistical packages (Griffith (1988)). 2.3 The Spatial Autoregressive Profile Log-likelihood
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(4) |
represents the x1 vector of observations on the dependent variable, 
represents observations on variables, and 
represents a scalar autoregressive parameter. Let 
, an idempotent matrix. One can write the residuals 
as the product of an by 
matrix 
containing the residuals from regressing and 
, for 
onto the columns of and a 
by 
vector, 
, comprised of a 1 followed by complement of the scalar times the element column vector 
. 
 | (5) |
is a by (
matrix. The profile log-likelihood becomes:  | (6) |
in (6) represents a constant (Anselin 1988 p. 182). Let 
denote the matrix augmented with a leading row and column of zeros as shown in (7). Note that 
, so the term 
combines the overall level of spatial dependence embodied in as well as the pattern of spatial dependence found in .  | (7) |
and 
as in (8). 
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(8) |
Note, the term contains the sum of squared weights as part of the overall penalty and thus reduces the variance in the estimated weights. As noted earlier, this resembles the penalty approach used with smoothing splines. The lower bound likelihood can be interpreted as a penalized likelihood since it has a larger penalty than the actual log-determinant. This will result in less variance among the estimated weights than the weights associated with the actual log-determinant. If the log-determinant term did not exist in the likelihood, one might have invented it anyway to smooth the estimated weights. A number of ways exist to model the independent variables. We examine both a non-spatial specification for commonly known as the spatial autoregressive model in (9) and a fully mixed model shown in (10).
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(9) |
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(10) |
nests a spatial error autoregression since spans 
. 
restrictions are imposed upon the model. The conventional likelihood ratio test would compute the deviance (twice the difference in the log-likelihoods) and this would asymptotically follow a 
distribution (Fan, Hung, and Wong (2000)). Let 
represent the restricted log-likelihood and 
the unrestricted log-likelihood; then 
. By construction, 
and 
where the superscripts 
and 
denote upper and lower bounds. Hence, if 
exceeds the selected critical value of the chi-squared distribution with degrees of freedom, this implies that the deviance based on the actual log-likelihoods, 
, would also exceed this selected critical value. From this we see that in some cases one would accept the same inference based on the bounded log-likelihoods as with the actual log-likelihoods. This resembles the likelihood dominance criteria discussed by Pollack and Wales (1991). Note also that this condition is likely to be met in a spatial model that contains a large number of observations with explanatory variables that are highly significant. 3 Empirical Performance of the Doubly Spatial ModelA number of researchers have used these data to illustrate various statistical points. For example, Krasker, Kuh, and Welch (1983), Subramanian and Carson (1988), Breiman and Friedman (1985), Lange and Ryan (1989), and Breiman et al. (1993) have used the data to examine robust estimation, normality of residuals, nonparametric, and semiparametric estimation. Gilley and Pace (1996) augmented these data with the corresponding spatial coordinates and corrected some mistakes in the data, and we use this version of the data for our analysis. We selected these data since (1) they have substantial spatial dependencies; (2) the data are well-known; and (3) the number of observations is small enough to permit estimation of the weights given to each spatial basis matrix using the exact log-determinant. Table 1 contains the estimates from OLS, from the lower bounded log-likelihood, the exact log-likelihood, and the upper bounded log-likelihood. The maximum likelihood estimates used the autoregressive model for the dependent variable. As one would hope, the results from the exact approach fall between the bounded estimates. Figure 2 displays the weights given to the different numbers of neighbors at the bounds and for the exact log-likelihood. Qualitatively, all estimation approaches produced the same shape. Note, estimation results at the bounds would enable a researcher to reject independence in favor of spatial dependence, since the lower bound of the unrestricted log-likelihood equals -584.4, which is significantly different from the exact restricted log-likelihood equal to -700.4 under independence.
Table 2 contains estimation results from the autoregressive model based on 5 independent variables and Table 3 presents results based on the mixed model that includes 93 independent variables (five independent variables, plus 22 basis matrices multiplied by the 4 non-constant independent variables). Comparing the non-spatial least-squares estimates based on 5 non-spatial independent variables to a least-squares model with 93 spatial and non-spatial independent variables resulted in an increase in the log-likelihood from -266,505 to -260,176. In contrast, the use of the 22 spatial basis matrices plus the parameter in addition to the 5 independent variables resulted in an increase in the log-likelihood from -266,505 to a point within the interval [-232,456, -230,764]. Adding the extra 88 spatial independent variables shifted this interval to [-227,218, -223,995]. Because these intervals are not close to overlapping, one can reject the autoregressive model in favor of the mixed model using a likelihood dominance argument. The number of added variables (i.e., 88) in going from the autoregressive to the mixed model may seem excessive. However, each parameter has over 600 observations (i.e., 
). Nevertheless, one could add additional penalties to the likelihood function to enforce more parsimony. For example, one could simply increase the weight given to the log-determinant bounds to yield a more parsimonious solution (penalized likelihood approach). A second approach would be to add other model selection penalties of the type embodied in the Akaike information criterion (AIC) or Schwarz criterion (see Judge et al. 1985, p. 244) that would also lead to more parsimonious models. Figures 3 and 4 show the estimated weight curves for the mixed and autoregressive models. Both curves show relatively smooth declines that appear almost linear near the origin and display a geometric decline away from the origin. Figure 5 displays the bounded profile log-likelihoods for the mixed model, that also exhibit a smooth shape. In determining the optimal weights, we used 100 values of ranging from 0 to 1 in 0.01 increments to implement our estimation method, and computed the optimal weights at each one of these values of . In addition, a random starting set of weights was used for each value of . Note, problems with multiple optima would appear in this curve as dips or downward jumps in the curve, which would alert the practitioner to potential problems. Also, if for some reason the optimization procedure failed at a particular value of , this would cause the “optimal” log-likelihood to lie below its true value at this value of . The overall procedure would then pick a level of on either side of the value where optimization failed, unless the optimization failed for a compact range of values that actually contained the optimal value of . Near the optimum the derivative of the objective function is near zero, so the value of the objective function at the neighbors would be similar. This means that choosing the neighboring value rather than the optimal value of would not result in a substantial loss for a relatively fine grid of values. As a result, we have a robust approach to optimization that simultaneously provides an informative profile likelihood curve.
4 ConclusionTo illustrate our method, we estimated a very flexible functional form of spatial dependence constructed using a convex combination of spatial basis matrices. Because the overall errors on a convex combination of parts of the dependent variable are just the convex combination of the errors on each part, the overall sum-of-squared errors is a quadratic form involving a small matrix. Further, the size of this matrix does not rise with the number of observations. Similarly, the penalty term is a quadratic form based upon a small matrix of traces containing pairwise basis matrix products. Again, the size of this small matrix does not rise with the number of observations, preserving the tractability of our approach as the sample size increases. We initially illustrated the method using 506 observations on the Boston housing market. In this setting, weights obtained via the doubly spatial model using bounds enclosed weights based on the true log-likelihood. Using a likelihood dominance argument, estimated bounds allowed us to reject spatial independence in favor of the spatial autoregressive model, without knowledge of the actual likelihood. As a more computationally challenging illustration, we estimated the spatial dependence among neighbors using a sample of 57,647 US Census tracts that contained complete information on housing characteristics. It took under 8.5 minutes to estimate the semiparametric model that embodied both nonparametric dependence as well as parametric regression. In our application, the estimated dependence showed an almost linear decline for nearby neighbors and a more geometric decline for higher order neighbors. In support of our approach, the estimated dependence at both lower and upper bounds demonstrated this same pattern of decay in spatial influence. Using a likelihood dominance argument, estimated bounds allowed us to reject a spatial autoregressive model in favor of a mixed model, without knowledge of the actual likelihood. A fortiori, we are also able to reject independent errors as well. In conclusion, modest modifications to numerical aspects of likelihood estimation can transform the computationally difficult problem of estimating the pattern and level of spatial dependence into a relatively simple problem, for which estimators exist. Such estimators can provide a means of visualizing spatial dependence through less tinted glasses than current methods. In addition to its exploratory role, the doubly spatial model may also be used to reject some hypotheses of interest via likelihood dominance arguments.
| Anselin, Luc (1988). Spatial Econometrics: Methods and Models. Dorddrecht: Kluwer Academic Publishers.
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| Bapat, R.B., and T.E.S. Raghavan (1997). Nonnegative Matrices and Applications. Cambridge: Cambridge University Press.
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| Barry, Ronald, and R. Kelley Pace (1999). “A Monte Carlo Estimator of the Log Determinant of Large Sparse Matrices.” Linear Algebra and its Applications 289, 41-54.
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| Bavaud, Francois (1998). “Models for Spatial Weights: A Systematic Look.” Geographical Analysis 50, 155-171.
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| Bell, Kathleen P., and Nancy E. Bockstael (2000). “Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data.” Review of Economics and Statistics 87, 72-82.
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| Breiman, Leo and Jerome Friedman (1985). “Estimating Optimal Transformations for Multiple Regression and Correlation.” Journal of the American Statistical Association 80, 580-619.
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| Breiman Leo, Jerome Friedman, R. Olshen, and C.J. Stone (1993). Classification and Regression Trees. New York: Chapman and Hall.
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| Cressie, Noel (1993). Statistics for Spatial Data. New York: Wiley.
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| Fan, Jianqing, Hui-Nien Hung, and Wing-Hung Wong (2000). “Geometric Understanding of Likelihood Ratio Statistics.” Journal of the American Statistical Association 95, 836-841.
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| Gilley, O. W. and R. Kelley Pace (1996). “On the Harrison and Rubinfeld Data.” Journal of Environmental Economics and Management 31, 403-405.
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| Griffith, Daniel (1988). “Estimating Spatial Autoregressive Model Parameters with Commercial Statistical Packages.” Geographical Analysis 20, 176-186.
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| Griffith, Daniel, and F. Lagona (1998). “On the Quality of Likelihood-based Estimators in Spatial Autoregressive Models when the Data Neighborhood Structure is Misspecified.” Journal of Statistical Planning and Inference 69, 153-174.
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| Griffith, Daniel and Akio Sone (1995). “Trade-offs Associated with Normalizing Constant Computational Simplifications for Estimating Spatial Statistical Models.” Journal of Statistical Computation and Simulation 51, p. 165-183.
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| Judge, George, W.E. Griffiths, R. Carter Hill, Helmut Lütkepohl, and Tsoung-Chao Lee (1985). The Theory and Practice of Econometrics. New York: Wiley.
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| Härdle, Wolfgang (1990). Applied Nonparametric Regression. Cambridge: Cambridge University Press.
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| Harrison, D. and D.L. Rubinfeld (1978). “Hedonic Prices and the demand for Clean Air.” Journal of Environmental Economics and Management 5, 19-40.
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| Krasker, William S., Edwin Kuh, and Roy E. Welch (1983). “Estimation for Dirty Data and Flawed Models.” Handbook of Econometrics. Amsterdam: North-Holland, 651-698.
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| Lange, Nicholas and Louise Ryan (1989). “Assessing Normality in Random Effects Models.” Annals of Statistics 17, 624-42.
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| Martin, R. (1993). “Approximation to the Determinant in Gaussian Maximum Likelihood Estimation of Some Spatial Models.” Communications in Statistics: Theory and Methods 22, 189-205.
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| Pace, R. Kelley, and Ronald Barry (1997). “Quick Computation of Regressions with a Spatially Autoregressive Dependent Variable.” Geographical Analysis 29, 232-247.
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| Pace, R. Kelley, and Dongya Zou (2000). “Closed-Form Maximum Likelihood Estimates of Nearest Neighbor Spatial Dependence.” Geographical Analysis 32, 154-172.
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| Pollack, R.A., and T.J. Wales (1991). “The Likelihood Dominance Criterion: A New Approach to Model Selection.” Journal of Econometrics 47, 227-242.
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| Streitberg, B. (1979). “Multivariate Models of Dependent Spatial Data.” Exploratory and Explanatory Statistical Analysis of Spatial Data, Edited by CPA Bartels and RH Ketellaper. Martinus Nijhoff, 139-177.
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| Subramanian, Shankar and Richard T. Carson (1988). “Robust Regression in the Presence of Heteroskedasticity.” Advances in Econometrics 7. JAI Press, 85-138.
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| Tiefelsdorf, M., Daniel Griffith, and Barry Boots (1999). “A Variance Stabilizing Scheme for Spatial Link Matrices.” Environment and Planning A 31, 165-180.
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Variables |
OLS |
Lower Bound |
Exact ML Lower |
Upper Bound |
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ML |
Bound Weights |
ML |
CRIM |
-0.0118 |
-0.0072 |
-0.0069 |
-0.0067 |
ZN |
0.0001 |
0.0004 |
0.0004 |
0.0004 |
INDUS |
0.0002 |
0.0009 |
0.0010 |
0.0011 |
CHAS |
0.0921 |
0.0146 |
0.0114 |
0.0077 |
NOX  |
-0.6372 |
-0.2524 |
-0.2346 |
-0.2161 |
RM |
0.0063 |
0.0072 |
0.0072 |
0.0073 |
AGE |
0.0001 |
-0.0004 |
-0.0005 |
-0.0005 |
LDIS |
-0.1978 |
-0.1643 |
-0.1616 |
-0.1599 |
LRAD |
0.0896 |
0.0592 |
0.0574 |
0.0560 |
TAX |
-0.0004 |
-0.0003 |
-0.0003 |
-0.0003 |
PTRATIO |
-0.0296 |
-0.0092 |
-0.0083 |
-0.0073 |
B |
0.0004 |
0.0003 |
0.0003 |
0.0003 |
LSTAT |
-37.4895 |
-23.3274 |
-22.4674 |
-21.7632 |
 |
0 |
0.5150 |
0.5400 |
0.5650 |
Log like |
-700.35 |
-584.4019 |
-580.1425 |
-576.7777 |
Variables |
OLS |
Restricted |
Lower |
Restricted |
Upper |
Restricted |
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Log Like |
Bound |
Log Like |
Bound |
Log Like |
Land Area |
-0.0025 |
-264,834 |
-0.0003 |
-227,572 |
0.0003 |
-224,099 |
Pop |
0.0253 |
-261,261 |
0.0200 |
-227,373 |
0.0189 |
-224,098 |
Income |
0.6628 |
-282,537 |
0.6471 |
-244,844 |
0.6400 |
-239,264 |
Age |
-0.1338 |
-260,785 |
-0.1337 |
-228,754 |
-0.1326 |
-225,448 |
| |
|
|
0.8850 |
-260,176 |
0.9850 |
-260,176 |
 |
93 |
|
93 |
|
93 |
|
 |
0 |
|
23 |
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23 |
|
Unrestricted |
|
-260,176 |
|
-227,218 |
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-223,995 |
Log Like |
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Variables |
OLS |
Restricted |
Lower |
Restricted |
Upper |
Restricted |
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|
Log Like |
Bound |
Log Like |
Bound |
Log Like |
Land Area |
-0.0850 |
-271,195 |
-0.0177 |
-232,756 |
-0.0082 |
-230,914 |
Pop |
0.1146 |
-267,184 |
0.0195 |
-232,526 |
0.0142 |
-230,803 |
Income |
1.0837 |
-288,183 |
0.4840 |
-246,472 |
0.4358 |
-240,730 |
Age |
-0.1269 |
-267,093 |
-0.0763 |
-233,199 |
-0.0700 |
-231,408 |
| |
0 |
|
0.7750 |
-266,505 |
0.8150 |
-266,505 |
| |
5 |
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5 |
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5 |
|
| |
0 |
|
23 |
|
23 |
|
Unrestricted |
|
-266,505 |
|
-232,456 |
|
-230,764 |
Log Like |
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|
www.spatial-statistics.com
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I would like to gratefully acknowledge the research support received from Louisiana State University. I would like to thank Morton O’Kelly, two anonymous referees, and Ming-long Lee for their remarks. Moreover, we would like to thank Ohio State University Press, who own the copyright to this work, for giving us permission to place this on our web site. The reference for this work is: Pace, R. Kelley, and James P. LeSage, “Semiparametric Maximum Likelihood Estimates of Spatial Dependence,” Geographical Analysis, Volume 34, Number 1, January 2002, p. 75-90.
It took 13,630 seconds to determine the optimal weights using the exact log-determinant across a grid of 100 values of 
while it took 5.2 seconds to determine the optimal bounded weights over the same grid using a 600 Mhz Pentium III running NT 4.0. This 5.2 seconds included 2.8 seconds to form the doubly-stochastic matrices, 1.8 seconds to compute the traces, and 0.56 seconds to find optimal weights. Optimal weights were determined using Fortran 90 code while all other operations were carried out with Matlab 6.0 code. This small problem illustrates that choosing optimal weights based on the exact log-likelihood is computationally tedious. The ratio between the time required for an exact solution and that needed to compute bounds would increase with the number of observations.
In terms of computation, the time required to locate neighbors needed to form the spatial basis matrices was 122.2 seconds. It took 380.9 seconds to form 22 doubly stochastic spatial basis matrices, 123.7 seconds to compute the trace matrix, and 0.41 seconds to compute estimates. In total, less than 8.5 minutes were required for the entire procedure. Additional performance improvements are possible since formulation of the 22 doubly stochastic spatial basis matrices and trace matrix lend themselves to parallel processing.
