Suppose that ∆Yt follows the AR(1) model ∆Yt = β0 +β1∆Yt−1 +ut . Show that Yt follows an AR(2) model.
Suppose that ∆Yt follows the AR(1) model ∆Yt = β0 +β1∆Yt−1 +ut . Show that Yt...
Suppose that ∆Yt follows the AR(1) model ∆Yt = β0 +β1∆Yt−1 +ut . Show that Yt follows an AR(2) model.
Consider a population linear regression model: Yt=β0 + β1Xt + ut Calculate: 1. Variance 2. Covariance of ut and Xt 3. β0 4. β1
3. Suppose Δ1t follows the AR (1) model ΔΥ-30 + λίΔΙǐ-it . Show that Yt follows AR(2) model Derive the AR (2) coefficients for y, as a function of Ao and λι 3. Suppose Δ1t follows the AR (1) model ΔΥ-30 + λίΔΙǐ-it . Show that Yt follows AR(2) model Derive the AR (2) coefficients for y, as a function of Ao and λι
Consider the following model 1. Consider the following AR(1) model: a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. b. Show that 1 t-2 2. Consider the following random walk model: ytBo yt-1 +ut, t 0,1,...,T Show that ye 3o yt-3 + ut + Ut-1 +t-2 Suppose that yo - 0, show that yt - tPo + ut + ut-1++u, Suppose that that yo -0, and ut for all t...
Consider the following AR(1) model: 1. a. Explain why this dynamic model violates TS'3 ZCM assumption made for the unbiasedness of the FDL model estimators. the following random 2. Consider walk model: yeBo yt-1 +ut, t-0,1,..,T a. Show that yt-3βο + yt-3 + ut + ut-1 + ut-2. b. Suppose that 0-0, show that y.-t βο +4 + ut-1 + + u! c. Suppose that that yo -0, and ut for all t are ii.d. with mean 0 and variance...
Use the knowledge of "Introductions to Econometrics" to answer the following questions: Yt=β0+β1Xt+Ut Show that for the Least Square Estimators: a) The sum of the residuals equals zero. b) The sum of the product of the independent variable and residuals equals zero. Step by step
Consider the model, Yt = BO + p1 Yt-1 + Ut, select the assumption(s) that are needed to prove unbiased parameter estimates. (A. E[Ut Us |X, Yt-1, Yt-2, ... ] = 0 B. |p1|< 1 C. E[ Ut? |X, Yt-1, Yt-2, ... ] = su? D. E[ Ut |X, Yt-1, Yt-2, ... ] = 0
You obtain the following estimates for an AR(2) model of some returns data yt = 0.803yt−1 + 0.682yt−2 + ut Where ut is a white noise error process. By examining the characteristic equation, check the estimated model for stationarity.
Consider the model defined by, Yt = BO + B1 Yt-1 + B2 Xt + Ut. Compute the long-run coefficients (2 decimals) for the model: Short-Run Long-Run BO 1.38 B1 0.60 B2 -5.26
Consider the linear regression model Yi = β0 + β1 Xi + ui Yi is the ______________, the ______________ or simply the ______________. Xi is the ______________, the ______________ or simply the ______________. is the population regression line, or the population regression function. There are two ______________ in the function (β0 & β1 ). β0 is is the ______________ of the population regression line; β1is is the ______________ of the population regression line; and ui is the ______________. A. Coefficients...