5. Simulate the following AR(2) processes:Yt=1+0.3 Yt-1 +0.7 Yt-2+ɛtYt-1-0.3 Yt-1 -0.7 Yt-2+εtfor øpt → N(0, 1). Comment on their differences: Contrast their time series and their autocorrelationfunctions. Comment on the covariance-stationary properties of both processes.41%

The objective of the question is to simulate two AR(2) processes, compare their time series and…

Answered: 5. Simulate the following AR(2)…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Use a software of your own choice to calculate the daily log prices (lp) and daily log returns (Ir) i. Examine the descriptive statistics of both lp and Ir. What do you conclude about the distributions of lp and Ir? Is lp normally distributed? Is Ir normally distributed? Obtain the correlograms, and examine the autocorrelations and partial autocorrelations for both lp and Ir. What do you conclude about the behaviour of lp and Ir? Are they stationary/non-stationary? ii. iii. Are your conclusions about stationary/non-stationary of lp and lr confirmed by appropriate unit root tests?
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A potential customer has described the following scenario to you:We need to keep track of the data that our manufacturing firm needs to run smoothly.We make a number of products, each of which is manufactured by a different de-partment. Products are made up of a number of parts. Parts are bought fromsuppliers.You have been asked to perform a preliminary analysis of the scenario:a) Identify four sets of values from the scenario. b) Identify three relations from the scenario. c) Draw an entity-relationship diagram to visualise the sets and relations you have identi-fied. d) Use the relations you identified in part b) to define the following:i) A relation that records the parts that a department needs. ii) A relation that records the suppliers that a department deals with
1. Consider a linear regression model y = XB + € with E(e) = 0. The bias of the ridge estimator of 3 obtained by minimizing Q(B) = (y — Xß)¹ (y — Xß) + r(BTB), for some r > 0, is ——(X²X + r1)-¹8 1 (X¹X +rI)-¹3 r -r(XTX+rI) ¹8 r(X¹X+r1) ¹3
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5. Simulate the following AR(2) processes: Yt=1+0.3 Yt-1 +0.7 Yt-2+ɛtYt-1-0.3 Yt-1 -0.7 Yt-2+εt for øpt → N(0, 1). Comment on their differences: Contrast their time series and their autocorrelation functions. Comment on the covariance-stationary properties of both processes. 41%