Lecture 7a: Vector Autoregression (VAR)

Monique L. Ueltschy Murfield - Miami University

Monique L. Ueltschy Murfield Current Vita Updated February 20, 2018 Page 6 of 9 6 Murfield, Monique L. Ueltschy and Norton Jr., William I., Emergent Leadership in Logistics Teams: The Development of a Measurement Model.

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Lecture 5a: ARCH Models - Miami University

Consider the first order autoregressive conditional heteroskedasticity (ARCH) process rt = σtet (5) et ∼ white noise(0, 1) (6) σt = √ ω + α1r2 t 1 (7) where rt is the return, and is assumed here to be an ARCH(1) process. et is a white noise with zero mean and variance of one. et may or may not follow normal distribution. 7

  Conditional, Autoregressive, Autoregressive conditional

Basic Statistical Notation - Miami University

Some Useful Intuitions Let c denotes a constant number, and x and y denote two random variables 1. The expectation or mean value (E or ) means \average".It measures the central tendency of the distribution of a random variable. 2. By …

  Notation

Matrix Algebra for OLS Estimator - Miami University

Independent Variables • Suppose there are k independent variables and a constant term. In the spreadsheet there are k +1 columns and n rows. • Mathematically that spreadsheet corresponds to an n × (k +1) matrix, denoted by X : X = 1 x11::: x1k 1 x21::: x2k 1 xn1::: xnk where xij is the i-th observation of the j-th independent variable. 4

  Matrix

Lecture 8a: Spurious Regression

The traditional statistical theory holds when we run regression using (weakly or covariance) stationary variables. For example, when we regress one stationary series onto another stationary series, the coefficient will be close to zero and insignificant if the two series are independent.

  Regression, Covariance

Lecture: IV and 2SLS Estimators (Wooldridge’s book chapter 15)

It is straightforward to account for heteroskedasticity. The robust variance-covariance matrix for bˆ 2SLS allowing for heteroskedasticity is robust var-cov(bˆ 2SLS)= X 0PX 1 X PWPX X PX 1 where W = E(UU0): To estimate the meat in the middle of that sandwich, using X0PWbPX = Xb0WbXb= n å i=1 uˆ2 i bx ibx 0 i where ˆu denotes the 2SLS ...

  Robust, Estimator, Heteroskedasticity

Chapter 7, Dummy Variable - Miami University

3. Sample mean is the estimate for population mean, so we have the following interpre-tation for the estimated coefficients in (2) ˆ 0 = ¯yD=0 (8) ˆ 1 = ¯yD=1 ¯yD=0 (9) where ¯yD=0 denotes the average Y in the sub-sample for which D = 0; y¯D=1 denotes the average Y in the sub-sample for which D = 1: Equation (2) provides a simple way to carry out a comparison of means test (or …

  Chapter, Equations, Variable, Chapter 7, Dummy, Dummy variable

Lecture 9: Panel Data Model (Chapter 14, Wooldridge Textbook)

2 Panel Data Panel data is obtained by observing the same person, firm, county, etc over several periods. Unlike the pooled cross sections, the observations for …

Teaching Notes on Impulse Response Function and …

Impulse response func-tion (IRF) tracks the impact of any variable on others in the system. It is an essen-tial tool in empirical causal analysis and policy effectiveness analysis. This note reviews important concepts related to impulse response function and structural VAR. 2 Impulse response function Let Y t be a k-dimensional vector series ...

  Into, Response, Structural, Functions, Impulse, Impulse response function and, Impulse response function and structural var, Func, Impulse response func tion, Impulse response function

Structural Vector Autoregressions

(VAR) generated by an economic model (a “structure”). 2. 2. Reduced—form representation, normalization, and identi fication ... long-run impacts allow Blanchard and Quah to identify the shocks and trace their impulse response function. ... Why is the previous discussion of the relation between the reduced and structural form of a VAR ...

  Response, Structural, Functions, Impulse, Structural and, Impulse response function

EC 823: Applied Econometrics - Boston College

VAR without constraints and graph the impulse-response functions (IRFs). The more general var command allows for constraints to be placed on the coefficients. The varsoc command allows you to select the appropriate lag order for the VAR; command varwle computes Wald tests to determine whether certain lags can be excluded; varlmar checks for

  Applied, Response, Impulse, Econometrics, Applied econometrics, Ec 823