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A unified perspective on some autocorrelation measures in different fields: A note

  • Hiroshi Yamada EMAIL logo
Published/Copyright: April 1, 2023

Abstract

Using notions from linear algebraic graph theory, this article provides a unified perspective on some autocorrelation measures in different fields. They are as follows: (a) Orcutt’s first serial correlation coefficient, (b) Anderson’s first circular serial correlation coefficient, (c) Moran’s r 11 , and (d) Moran’s I . The first two are autocorrelation measures for one-dimensional data equally spaced, such as time series data, and the last two are for spatial data. We prove that (a)–(c) are a kind of (d). For example, we show that (d) such that its spatial weight matrix equals the adjacency matrix of a path graph is the same as (a). The perspective is beneficial because studying the properties of (d) leads to studying the properties of (a)–(c) at the same time. For example, the bounds of (a)–(c) can be found from the bounds of (d).

MSC 2010: 62M10; 62H11; 05C50

1 Introduction

In this article, using notions from linear algebraic graph theory, we provide a unified perspective on some autocorrelation measures in different fields. They are as follows:

  1. Orcutt’s first serial correlation coefficient [1],

  2. Anderson’s first circular serial correlation coefficient [2,3],

  3. Moran’s r 11 [4], and

  4. Moran’s I [57].

The first two are autocorrelation measures for one-dimensional data equally spaced, such as time series data, and the last two are for spatial data.[1] In this article, we prove that (a)–(c) are a kind of (d). For example, we show that (d) such that its spatial weight matrix equals the adjacency matrix of a path graph is the same as (a). The perspective is beneficial because studying the properties of (d) leads to studying the properties of (a)–(c) at the same time. For example, the bounds of (a)–(c) can be found from the bounds of (d).

This article is organized as follows. In Section 2, we introduce some notions for documenting our main results. In Section 3, we present the main results of the article. Section 4 concludes the article. Appendix A provides the proof of the main results.

2 Preliminaries

In this section, for later exposition,

  1. we explicitly present the four autocorrelation measures, (a)–(d),

  2. we review three undirected graphs: a path graph, a cycle graph, and a two-dimensional lattice graph, and

  3. we represent (d) in matrix form.

Let y 1 , , y n be observations such that i = 1 n ( y i y ¯ ) 2 > 0 , where y ¯ = 1 n i = 1 n y i . In addition, when n = p q for p , q N , denote y q ( i 1 ) + j by x i , j for i = 1 , , p and j = 1 , , q . Accordingly, x ¯ = 1 p q i = 1 p j = 1 q x i , j equals y ¯ .

2.1 Four autocorrelation measures

In this subsection, we explicitly present the four autocorrelation measures, (a)–(d).

(a) and (b) Orcutt’s first serial correlation coefficient and Anderson’s first circular serial correlation coefficient are, respectively, defined by

(1) ϕ = n n 1 i = 2 n ( y i y ¯ ) ( y i 1 y ¯ ) i = 1 n ( y i y ¯ ) 2

and

(2) ψ = i = 1 n ( y i y ¯ ) ( y i 1 y ¯ ) i = 1 n ( y i y ¯ ) 2 ,

where y 0 = y n .

(c) and (d) Moran’s r 11 is defined by

(3) r 11 = p q 2 p q p q i = 1 p j = 1 q 1 z i , j z i , j + 1 + i = 1 p 1 j = 1 q z i , j z i + 1 , j i = 1 p j = 1 q z i , j 2 ,

where z i , j = x i , j x ¯ for i = 1 , , p , and j = 1 , , q . Moran’s I is defined by

(4) I = n i = 1 n j = 1 n w i , j i = 1 n j = 1 n w i , j ( y i y ¯ ) ( y j y ¯ ) i = 1 n ( y i y ¯ ) 2 ,

where w i , j for i , j = 1 , , n denote nonnegative spatial weights.

2.2 Three undirected graphs

In this subsection, we review three undirected graphs: a path graph, a cycle graph, and a two-dimensional lattice graph. These undirected graphs are related to (a)–(c), respectively. Let V = { 1 , , n } .

Path graph. Consider an undirected graph G p = ( V , E p ) , where E p = { { 1 , 2 } , , { n 1 , n } } . Then, G p = ( V , E p ) is a path graph with n vertices. Figure 1 depicts G p = ( V , E p ) for n = 6 . Denote the adjacency matrix of G p = ( V , E p ) by A p . Then, A p is a tridiagonal symmetric matrix as follows:

A p = 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 R n × n .

We remark that, e.g., ( 1 , 2 ) and ( 2 , 1 ) entries of A p are equal to 1 as vertex 1 is adjacent to vertex 2, as shown in Figure 1. Likewise, ( 1 , 3 ) and ( 3 , 1 ) entries of A p are equal to 0 as vertex 1 is not adjacent to vertex 3. For more details of linear algebraic graph theory, see, e.g., [911].

Figure 1 
                  A path graph with six vertices.
Figure 1

A path graph with six vertices.

Cycle graph. Consider an undirected graph G c = ( V , E c ) , where E c = { { 1 , 2 } , , { n 1 , n } , { n , 1 } } . Then, G c = ( V , E c ) is a cycle graph with n vertices. Figure 2 depicts G c = ( V , E c ) for n = 6 . Denote the adjacency matrix of G c = ( V , E c ) by A c . Explicitly, it is

A c = 0 1 0 0 1 1 0 1 0 0 0 0 1 0 1 1 0 0 1 0 R n × n .

Figure 2 
                  A cycle graph with six vertices.
Figure 2

A cycle graph with six vertices.

Two-dimensional lattice graph. Consider an undirected graph G g = ( V , E g ) , where E g = E g E g . Here, E g = i = 1 p { { q ( i 1 ) + 1 , q ( i 1 ) + 2 } , , { q ( i 1 ) + ( q 1 ) , q ( i 1 ) + q } } = i = 1 p j = 1 q 1 { q ( i 1 ) + j , q ( i 1 ) + j + 1 } and E g = i = 1 p 1 { { q ( i 1 ) + 1 , q i + 1 } , , { q ( i 1 ) + q , q i + q } } = i = 1 p 1 j = 1 q { q ( i 1 ) + j , q i + j } . Figure 3 depicts G g = ( V , E g ) for p = 2 and q = 4 . Denote the adjacency matrix of G g = ( V , E g ) by A g . For example, when p = 2 and q = 4 , given E g = { { 1 , 2 } , { 2 , 3 } , { 3 , 4 } , { 5 , 6 } , { 6 , 7 } , { 7 , 8 } } and E g = { { 1 , 5 } , { 2 , 6 } , { 3 , 7 } , { 4 , 8 } } , it follows that

A g = 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 R 8 × 8 .

Figure 4 depicts two subgraphs of G g = ( V , E g ) for p = 2 and q = 4 , i.e., G g = ( V , E g ) (left) and G g = ( V , E g ) (right).

Figure 3 
                  A two-dimensional lattice graph: 
                        
                           
                           
                              
                                 
                                    G
                                 
                                 
                                    g
                                 
                              
                              =
                              
                                 (
                                 
                                    V
                                    ,
                                    
                                       
                                          E
                                       
                                       
                                          g
                                       
                                    
                                 
                                 )
                              
                           
                           {G}_{g}=\left(V,{E}_{g})
                        
                      for 
                        
                           
                           
                              p
                              =
                              2
                           
                           p=2
                        
                      and 
                        
                           
                           
                              q
                              =
                              4
                           
                           q=4
                        
                     .
Figure 3

A two-dimensional lattice graph: G g = ( V , E g ) for p = 2 and q = 4 .

Figure 4 
                  Subgraphs of 
                        
                           
                           
                              
                                 
                                    G
                                 
                                 
                                    g
                                 
                              
                              =
                              
                                 (
                                 
                                    V
                                    ,
                                    
                                       
                                          E
                                       
                                       
                                          g
                                       
                                    
                                 
                                 )
                              
                           
                           {G}_{g}=\left(V,{E}_{g})
                        
                      for 
                        
                           
                           
                              p
                              =
                              2
                           
                           p=2
                        
                      and 
                        
                           
                           
                              q
                              =
                              4
                           
                           q=4
                        
                     .
Figure 4

Subgraphs of G g = ( V , E g ) for p = 2 and q = 4 .

2.3 Moran’s I in matrix form

In this subsection, we represent Moran’s I in (4) in matrix form. It can be represented as follows:

I = n ι W ι y Q ι W Q ι y y Q ι y ,

where y = [ y 1 , , y n ] , ι = [ 1 , , 1 ] R n , W = [ w i , j ] R n × n , which denotes the spatial weight matrix, and Q ι = I n ι ( ι ι ) 1 ι . Here, I n denotes the identity matrix of order n . See, e.g., [12, p. 129].

3 Main results

In this section, we present the main results of the article.

Proposition 1

Denote Moran’s I for the case in which W equals A i by I i for i = p , c , g , i.e.,

I i = n ι A i ι y Q ι A i Q ι y y Q ι y , i = p , c , g .

Then, it follows that

  1. Anderson’s first circular serial correlation coefficient, ψ in (2) equals I c ,

  2. Orcutt’s first serial correlation coefficient, ϕ in (1) equals I p , and

  3. Moran’s r 11 in (3) equals I g .

Proof

See Appendix A.□

We make three remarks on Proposition 1.

  1. The perspective on the four autocorrelation measures in different fields provided by Proposition 1 is beneficial because studying the properties of Moran’s I leads to studying the properties of the other three autocorrelation measures. We give one such example. De Jong et al. [13] derived the bounds of Moran’s I . Then, given the results in Proposition 1, we can find the bounds of the other autocorrelation measures.[2]

  2. Proposition 1(ii) is of particular interest. This is because it is an example showing that time series analysis can be seen as a kind of spatial analysis. See also [14,15] for more such examples.

  3. Let I i = n ι ( w A i ) ι y Q ι ( w A i ) Q ι y y Q ι y for i = p , c , g , where w is a positive real number. Then it follows that I i = I i for i = p , c , g . Therefore, I i in Proposition 1 can be replaced by I i .

4 Concluding remarks

In this article, using notions from linear algebraic graph theory, we have provided a unified perspective on some autocorrelation measures in different fields, namely, Orcutt’s first serial correlation coefficient, ϕ in (1), Anderson’s first circular serial correlation coefficient, ψ in (2), Moran’s r 11 in (3), and Moran’s I in (4). The main results of this article are demonstrated in Proposition 1.


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Acknowledgments

The author would like to thank Kazuhiko Hayakawa, Ryo Okui, and four anonymous referees for their valuable comments on an earlier version of this article. The usual caveat applies.

  1. Funding information: The Japan Society for the Promotion of Science supported this work through KAKENHI Grant No. 20K20759.

  2. Ethical approval: The conducted research is not related to either human or animal use.

  3. Conflict of interest: The author states no conflict of interest.

Appendix A Proof of Proposition 1

In this section, we provide the proof of Proposition 1. Let e i be the i th column of I n , i.e., I n = [ e 1 , , e n ] and P ι = ι ( ι ι ) 1 ι .

A.1 Proof of Proposition 1(i): ψ = I c

Let J c = [ e 2 , , e n , e 1 ] . Then, given that A c = J c + J c and ι A c ι = 2 n , I c can be represented as

(A1) I c = n 2 n y Q ι ( J c + J c ) Q ι y y Q ι y = 1 2 y Q ι ( J c + J c ) Q ι y y Q ι y .

Next, given y 0 = y n , it follows that

( J c P ι ) y = J c y P ι y = y n y 1 y n 1 y ¯ y ¯ y ¯ = y 0 y ¯ y 1 y ¯ y n 1 y ¯ ,

which leads to i = 1 n ( y i y ¯ ) ( y i 1 y ¯ ) = ( Q ι y ) { ( J c P ι ) y } = y Q ι ( J c P ι ) y . Thus, ψ in (2) can be represented in matrix form as ψ = y Q ι ( J c P ι ) y y Q ι y . Given J c ι = ι , it follows that J c Q ι = J c J c P ι = ( J c P ι ) , which yields ψ = y Q ι J c Q ι y y Q ι y . Moreover, given that y Q ι J c Q ι y is a scalar, it follows that y Q ι J c Q ι y = y Q ι J c Q ι y , which yields y Q ι J c Q ι y = 1 2 y Q ι ( J c + J c ) Q ι y . Thus, we have

(A2) ψ = 1 2 y Q ι ( J c + J c ) Q ι y y Q ι y .

Therefore, from (A1) and (A2), we obtain ψ = I c .

A.2 Proof of Proposition 1(ii): ϕ = I p

Let J p = [ e 2 , , e n , 0 ] R n × n . Then, given that A p = J p + J p and ι A p ι = 2 ( n 1 ) , I p can be represented as

(A3) I p = n 2 ( n 1 ) y Q ι ( J p + J p ) Q ι y y Q ι y .

Let y 0 = y ¯ . Then, given ( y 1 y ¯ ) ( y 0 y ¯ ) = 0 , we have i = 2 n ( y i y ¯ ) ( y i 1 y ¯ ) = i = 1 n ( y i y ¯ ) ( y i 1 y ¯ ) . Let J = J p + 1 n e 1 ι . Then, it follows that

( J P ι ) y = J y P ι y = y ¯ y 1 y n 1 y ¯ y ¯ y ¯ = y 0 y ¯ y 1 y ¯ y n 1 y ¯ ,

which leads to i = 2 n ( y i y ¯ ) ( y i 1 y ¯ ) = i = 1 n ( y i y ¯ ) ( y i 1 y ¯ ) = ( Q ι y ) { ( J P ι ) y } = y Q ι ( J P ι ) y . Thus, ϕ can be represented in matrix form as ϕ = n n 1 y Q ι ( J P ι ) y y Q ι y . Given J Q ι = ( J p + 1 n e 1 ι ) Q ι = J p Q ι and J ι = ι , it follows that J p Q ι = J Q ι = J J P ι = ( J P ι ) , which yields ϕ = n n 1 y Q ι J p Q ι y y Q ι y . Moreover, given that y Q ι J p Q ι y is a scalar, it follows that y Q ι J p Q ι y = y Q ι J p Q ι y , which leads to y Q ι J p Q ι y = 1 2 y Q ι ( J p + J p ) Q ι y . Thus, we have

(A4) ϕ = n 2 ( n 1 ) y Q ι ( J p + J p ) Q ι y y Q ι y .

Therefore, from (A3) and (A4), we obtain ϕ = I p .

A.3 Proof of Proposition 1(iii): r 11 = I g

Denote the adjacency matrix of G g = ( V , E g ) [resp. G g = ( V , E g ) ] by A g [resp. A g ]. Then, it follows that A g = A g + A g and these adjacency matrices are explicitly expressed as A g = J + J and A g = J + J , where J = [ e 2 , , e q , 0 , , e q ( p 1 ) + 2 , , e p q , 0 ] R p q × p q and J = [ e q + 1 , , e 2 q , , e q ( p 1 ) + 1 , , e p q , 0 , , 0 ] R p q × p q . Accordingly, given n = p q , ι A g ι = 2 p ( q 1 ) , and ι A g ι = 2 q ( p 1 ) , it follows that

I g = p q 2 ( 2 p q p q ) y Q ι ( J + J + J + J ) Q ι y y Q ι y .

Thus, given that i = 1 p j = 1 q ( x i , j x ¯ ) 2 = i = 1 n ( y i y ¯ ) 2 = y Q ι y , if the following (A5) and (A6) hold, then I g = r 11 .

(A5) i = 1 p j = 1 q 1 ( x i , j x ¯ ) ( x i , j + 1 x ¯ ) = 1 2 y Q ι ( J + J ) Q ι y ,

(A6) i = 1 p 1 j = 1 q ( x i , j x ¯ ) ( x i + 1 , j x ¯ ) = 1 2 y Q ι ( J + J ) Q ι y .

Accordingly, we prove them in order. First, given that y Q ι J Q ι y = y Q ι J Q ι y , (A5) is equivalent to i = 1 p j = 1 q 1 ( x i , j x ¯ ) ( x i , j + 1 x ¯ ) = y Q ι J Q ι y . Here, given

y Q ι J Q ι y = { ( y 1 y ¯ ) ( y 2 y ¯ ) + + ( y q 1 y ¯ ) ( y q y ¯ ) } + + { ( y q ( p 1 ) + 1 y ¯ ) ( y q ( p 1 ) + 2 y ¯ ) + + ( y p q 1 y ¯ ) ( y p q y ¯ ) } = i = 1 p j = 1 q 1 ( y q ( i 1 ) + j y ¯ ) ( y q ( i 1 ) + j + 1 y ¯ ) ,

x i , j = y q ( i 1 ) + j for i = 1 , , p and j = 1 , , q , and x ¯ = y ¯ , we have (A5). Second, given that y Q ι J Q ι y = y Q ι J Q ι y , (A5) is equivalent to i = 1 p 1 j = 1 q ( x i , j x ¯ ) ( x i + 1 , j x ¯ ) = y Q ι J Q ι y . Here, given

y Q ι J Q ι y = { ( y 1 y ¯ ) ( y q + 1 y ¯ ) + + ( y q y ¯ ) ( y 2 q y ¯ ) } + + { ( y q ( p 2 ) + 1 y ¯ ) ( y q ( p 1 ) + 1 y ¯ ) + + ( y q ( p 1 ) y ¯ ) ( y p q y ¯ ) } = i = 1 p 1 j = 1 q ( y q ( i 1 ) + j y ¯ ) ( y q i + j y ¯ ) ,

x i , j = y q ( i 1 ) + j for i = 1 , , p and j = 1 , , q , and x ¯ = y ¯ , we have (A6).

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Received: 2022-09-03
Revised: 2023-02-03
Accepted: 2023-03-06
Published Online: 2023-04-01

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  86. On the quadratic residues and their distribution properties
  87. On average theta functions of certain quadratic forms as sums of Eisenstein series
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  94. On a class of stochastic differential equations driven by the generalized stochastic mixed variational inequalities
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  96. Generalized Lie n-derivations on arbitrary triangular algebras
  97. Markov decision processes approximation with coupled dynamics via Markov deterministic control systems
  98. Notes on pseudodifferential operators commutators and Lipschitz functions
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  100. Strong limit of processes constructed from a renewal process
  101. Construction of analytical solutions to systems of two stochastic differential equations
  102. Two-distance vertex-distinguishing index of sparse graphs
  103. Regularity and abundance on semigroups of partial transformations with invariant set
  104. Liouville theorems for Kirchhoff-type parabolic equations and system on the Heisenberg group
  105. Spin(8,C)-Higgs pairs over a compact Riemann surface
  106. Properties of locally semi-compact Ir-topological groups
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  112. Schur-power convexity of integral mean for convex functions on the coordinates
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  114. On Bohr's inequality for special subclasses of stable starlike harmonic mappings
  115. Properties of meromorphic solutions of first-order differential-difference equations
  116. A double-phase eigenvalue problem with large exponents
  117. On the number of perfect matchings in random polygonal chains
  118. Evolutoids and pedaloids of frontals on timelike surfaces
  119. A series expansion of a logarithmic expression and a decreasing property of the ratio of two logarithmic expressions containing cosine
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  121. Stability result for Lord Shulman swelling porous thermo-elastic soils with distributed delay term
  122. Approximate solvability method for nonlocal impulsive evolution equation
  123. Construction of a functional by a given second-order Ito stochastic equation
  124. Global well-posedness of initial-boundary value problem of fifth-order KdV equation posed on finite interval
  125. On pomonoid of partial transformations of a poset
  126. New fractional integral inequalities via Euler's beta function
  127. An efficient Legendre-Galerkin approximation for the fourth-order equation with singular potential and SSP boundary condition
  128. Eigenfunctions in Finsler Gaussian solitons
  129. On a blow-up criterion for solution of 3D fractional Navier-Stokes-Coriolis equations in Lei-Lin-Gevrey spaces
  130. Some estimates for commutators of sharp maximal function on the p-adic Lebesgue spaces
  131. A preconditioned iterative method for coupled fractional partial differential equation in European option pricing
  132. A digital Jordan surface theorem with respect to a graph connectedness
  133. A quasi-boundary value regularization method for the spherically symmetric backward heat conduction problem
  134. The structure fault tolerance of burnt pancake networks
  135. Average value of the divisor class numbers of real cubic function fields
  136. Uniqueness of exponential polynomials
  137. An application of Hayashi's inequality in numerical integration
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