Description: Economic time series reconciliation; Di Fonzo (Univ Padua) & Marini (ISTAT)
|(undirected graph drawing)|
|number of rows||7,245|
|number of columns||7,245|
|structural full rank?||yes|
|# of blocks from dmperm||3|
|# strongly connected comp.||3|
|explicit zero entries||0|
|nonzero pattern symmetry||symmetric|
|numeric value symmetry||symmetric|
|author||T. Di Fonzo, M. Marini|
|Additional fields||size and type|
Economic statistics are often published in the form of time series, as a collection of observations sampled at equally-spaced time periods (months, quarters). Economic concepts behind such statistics are often linked by a system of linear relationships, deriving from the economic theory. However, these restrictions are rarely met by the original time series for various reasons. Then, data sets of real-world variables generally show discrepancies with respect to prior restrictions on their values. The adjustment of a set of data in order to satisfy a number of accounting restrictions -and thus to remove any discrepancy -is generally known as the reconciliation problem. The matrix comes from a real application composed of 183 quarterly time series observed over 28 quarters, which form the system of European national accounts by institutional sectors (EURQSA). Then, the number of observations to be reconciled is n = 28 x 183 = 5124. The variables are connected by a system of 30 linear relationships. Moreover, each quarterly time series must be in line with the same variables observed yearly (due to different compilation practices quarterly and annual estimates might differ). The total number of constraints of the system is k = 2121. On the whole, matrix A has dimension 7245, with block (1,1) of dimension 5124.
|nnz(chol(P*(A+A'+s*I)*P')) with AMD||153,155|
|Cholesky flop count||1.3e+07|
|nnz(L+U), no partial pivoting, with AMD||299,065|
|nnz(V) for QR, upper bound nnz(L) for LU, with COLAMD||1,174,854|
|nnz(R) for QR, upper bound nnz(U) for LU, with COLAMD||2,203,468|
|null space dimension||210|
|full numerical rank?||no|
|singular value gap||3.03054e+08|
|singular values (MAT file):||click here|
|SVD method used:||s = svd (full (A)) ;|
For a description of the statistics displayed above, click here.
Maintained by Tim Davis, last updated 12-Mar-2014.
Matrix pictures by cspy, a MATLAB function in the CSparse package.
Matrix graphs by Yifan Hu, AT&T Labs Visualization Group.