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Mathematics for Economists RS –Ch4-1 –Linear Algebra Mathematics for Economists Chapters 4-5 Linear Models and Matrix Algebra The Nine Chapters on the Mathematical Art Johann Carl Friedrich Gauss (1777–1855) (1000-200 BC) Math for Economists & Finance Modern economics and finance is analytical and mathematical in structure. Modern economics theory is presented in terms of mathematical economic models, with a set mathematical relationship used to clarify assumptions and implications. Within the Social Sciences, economics and finance are fields where math is a natural tool. Economics and Finance deal with a lot of metric concepts: Prices, quantities, income, interest rates, etc. Math allows economists to form systematic relations between metric concepts and study the implications of these relations. Math allows us to develop and test testable propositions. Remember: Math econ is like love – a simple idea but it can get complicated. 2 1 RS –Ch4-1 –Linear Algebra 4. Linear Algebra Some early history: The beginnings of matrices and determinants goes back to the second century BC although traces can be seen back to the fourth century BC. But, the ideas did not make it to mainstream math until the late 16th century The Babylonians around 300 BC studied problems which lead to simultaneous linear equations. The Chinese, between 200 BC and 100 BC, came much closer to matrices than the Babylonians. Indeed, the text Nine Chapters on the Mathematical Art written during the Han Dynasty gives the first known example of matrix methods. In Europe, 2x2 determinants were considered by Cardano at the end of the 16th century and larger ones by Leibniz and, in Japan, by Seki about 100 years later. 4. What is a Matrix? A matrix is a set of elements, organized into rows and columns rows a b columns c d • a and d are the diagonal elements. • b and c are the off-diagonal elements. • Matrices are like plain numbers in many ways: they can be added, subtracted, and, in some cases, multiplied and inverted (divided). Arthur Cayley (1821 – 1895, England) 2 RS –Ch4-1 –Linear Algebra 4. Matrix: Details Examples: a11 a 21 A ; b b1 b 2 b3 a12 a 22 • Dimensions of a matrix: numbers of rows by numbers of columns. The Matrix A is a 2x2 matrix, b is a 1x3 matrix. • A matrix with only 1 column or only 1 row is called a vector. • If a matrix has an equal numbers of rows and columns, it is called a square matrix. Matrix A, above, is a square matrix. • Usual Notation: Upper case letters matrices Lower case vectors 5 4. Matrix: Geometric Intuition In n-dimensional spaces with a fixed a coordinate system (origin), an n-dimensional vector, ⃗, is an arrow from the origin to a point in space whose coordinates correspond to the elements of ⃗. ⃗ ⃗ As such, vectors have direction and magnitude (a scalar). The notation, ⃗, incorporates the first feature. 6 3 RS –Ch4-1 –Linear Algebra 4. Matrix: Geometric Intuition If no origin is fixed, a vector represents displacements, that is, the position of one point relative to another. The vector = ⃗ represents the displacement from point A to point B. If the same displacement ⃗ is applied from point C, we end up in D. Alternatively, a vector is a way of measuring an amount of an oriented 1-dimensional subspace of a vector space. That is, it picks out an oriented line through an origin and specifies a length. 7 4. Matrix: Geometric Intuition A "bivector" is a way of measuring an amount of an oriented 2- dimensional subspace of a vector space. That is, it picks out an oriented plane through the origin and specifies an area. Two (non- parallel) vectors give a bivector (a matrix of rank 2!). Similarly, there are “trivectors,” and so on. But, in economics and finance, the geometric interpretation is not very intuitive. We use vectors and matrices as a tool to organize and manipulate information. 8 4 RS –Ch4-1 –Linear Algebra 4. Matrix: Information Information is described by data. A tool to organize the data is a list, which we call a vector. Lists of lists are called matrices. That is, we organize the data using matrices. We think of the elements of X as data points (“data entries”, “observations”), in economics, we usually have numerical data. We store the data in rows. In a Txk matrix, X, over time we build a database: ⋯ row 1 = k entries at time 1 X ⋮⋱⋮ ⋯ row T = k entries at time T Once the data is organized in matrices it can be easily manipulated: multiplied, added, etc. (this is what Excel does). 9 4. Matrix: Details In econometrics, we have a model y = f(x1, x2, ... xk), which we want to estimate. We collect data, say T (or N) observations, on a dependent variable, y, and on k explanatory variables, X. Under the usual notation, vectors will be column vectors: y and xk are Tx1 vectors: ⋮ ⋮ &xj j = 1,..., k ⋯ X is a Txk matrix: X ⋮⋱⋮ ⋯ Its columns are the kTx1 vectors xj. It is common to treat x1 as vector of ones, ί. 10 5 RS –Ch4-1 –Linear Algebra 4.1 Special Matrices: Identity and Null • Identity Matrix: A square matrix with 1’s along 1 0 0 the diagonal and 0’s everywhere else. Similar to 0 1 0 scalar “1.” 0 0 1 • Null matrix: A matrix in which all elements are 0 0 0 0’s. Similar to scalar “0.” 0 0 0 0 0 0 • Both are diagonal matrices off-diagonal elements are zero. Both are examples of symmetric and idempotent matrices. As we will see later: - Symmetric: A = AT 2 3 - Idempotent: A = A = A = … 11 4.1 Matrix: Elementary Row Operations • Elementary row operations: – Switching: Swap the positions of two rows – Multiplication: Multiply a row by a non-zero scalar – Addition: Add to one row a scalar multiple of another. • An elementary matrix is a matrix which differs from the identity matrix by one single elementary row operation. • If the matrix subject to elementary row operations is associated to a system of linear equations, then these operations do not change the solution set. Row operations can make the problem easier. • Elementary row operations are used in Gaussian elimination to reduce a matrix to row echelon form. 12 6 RS –Ch4-1 –Linear Algebra 4.1 Matrix multiplication: Details Multiplication of matrices requires a conformability condition The conformability condition for multiplication is that the column dimensions of the lead matrix A must be equal to the row dimension of the lag matrix B. If A is an (mxn) and B an (nxp) matrix (A has the same number of columns as B has rows), then we define the product of AB. n a b AB is (mxp) matrix with its ij-th element is j1 ij jk What are the dimensions of the vector, matrix, and result? b11 b12 b13 aB a a c c c c 11 12 b b 11 12 13 21 22 23 a11b11 a12b21 a11b12 a12b22 a11b13 a12b23 • Dimensions: a(1x2), B(2x3) c(1x3) 13 4.1 Transpose Matrix The transpose of a matrix A is another matrix AT (also written A′) created by any one of the following equivalent actions: –write the rows (columns) of A as the columns (rows) of AT –reflect A by its main diagonal to obtain AT Formally, the (i,j) element of AT is the (j,i) element of A: T [A ]ij = [A]ji If A is a m × n matrix AT is a n × m matrix. (A')' = A Conformability changes unless the matrix is square. 3 1 3 8 9 A A Example : 8 0 1 0 4 9 4 14 7 RS –Ch4-1 –Linear Algebra 4.1 Transpose Matrix: Example – X’ In econometrics, an important matrix is X’X. Recall X: ⋯ X ⋮⋱⋮ a (Txk) matrix ⋯ Then, ⋯ X’ ⋮⋱⋮ a (kxT) matrix ⋯ 15 4.1 Basic Operations Addition, Subtraction, Multiplication a b e f ae b f Just add elements c d g h cg d h a b e f ae b f Just subtract elements c d g h cg d h a be f ae bg af bh Multiply each row by c dg h ce dg cf dh each column and add a b ka kb k Multiply each c d kc kd element by the scalar 16 8 RS –Ch4-1 –Linear Algebra 4.1 Basic Matrix Operations: Examples 2 1 3 1 5 2 Matrix addition 7 9 0 2 7 11 A2x2 B2x2 C2x2 Matrix subtraction 2 1 1 0 1 1 7 9 2 3 5 6 Matrix multiplication 2 1 1 0 4 3 x 7 9 2 3 26 27 A xB C Scalar multiplication 2x2 2x2 2x2 1 2 4 1 4 1 2 17 8 6 1 3 4 1 8 17 4.1 Basic Matrix Operations: X′X A special matrix in econometrics, X′X (a kxk matrix): ⋯ ⋯ Recall X (Txk): X= ⋮⋱⋮& X’ ⋮⋱⋮ ⋯ ⋯ ∑ ⋯ ∑ X’ X ⋮⋱⋮= ∑ ⋯ ∑ ⋯ = ∑ ⋮⋱⋮= ⋯ ∑ ⋮ ⋯ = ∑ ′ 18 9 RS –Ch4-1 –Linear Algebra 4.1 Basic Matrix Operations: ί′X Recall ί is a column vector of ones (in this case, a Tx1 vector): 1 1 ί = … 1 Given X (Txk), then ί’X is a 1xk vector: ⋯ ί’X 1…1 ⋮⋱⋮= ∑ … ∑ ⋯ Note: If x1 is a vector of ones (representing a constant in the linear classical model), then: ί’x1 = ∑ = ∑ 1 = T (“dot product”) 19 4.1 Basic Matrix Operations: R Many ways to create a vector (c, 2:7, seq, rep, etc) or a matrix (c, cbind, rbind).
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