Assignment #1 STA457H1S/2202H1S
due Friday February 3, 2017
Instructions: Students in STA457 do problems 1 through 4; those in STA2202 do all 5
problems.
1. Daily Canadian/U.S. dollar exchange rates ($US/$CAN) from Jan. 2, 1997 to Dec. 29,
2000 are given
STA457 Practice Problems - Week 5
1) Consider the simple moving average filter with weights aj = (2q+1)-1 , -1 q 1.
q
a) Let Xt = c0 + c1t . Show that
a
j = q
j
X t j = X t .
b) Let Zt be independent random variables with mean 0 and variance 2. Show that
Solutions to Assignment #1
STA457H1S/2202H1S
1. (a), (b) See the plots on the following pages.
(c) Periodogram of original data: maximum near 0, generally decreasing as frequency increases. Correlogram: decays very slowly. (This is quite consistent with w
STA457 Fall 2001
Test #1 - Solutions
(5) 1. True or False
a. A white noise process is a weakly stationary process.
b. A random walk is a weakly stationary process.
c. There exists a stationary process which satis es (3) 6= (;3)
d. Assume the population To
Assignment #1 STA457H1S/2202H1S
due Wednesday February 3, 2016
Instructions: Students in STA457S do problems 1 through 3; those in STA2202S do all 4 problems.
1. Daily Japanese yen/US dollar exchange rates (JPY/USD) from Dec. 1, 1978 to Apr.29, 2005
are g
ARMA models
ARMA
Gloria Gonzlez-Rivera
University of California, Riverside
and
Jess Gonzalo U. Carlos III de Madrid
White Noise
White
A sequence of uncorrelated random variables is called a white noise
process. cfw_ a : E ( a ) =
(normally = 0)
t
t
a
a
PartA.DEFINITION[16%]
1. [4%] Define the DurbinWatson test and explain why we tend to reject the null hypothesiswhenthevalueoftheteststatisticissmall. Answer: (1) [3%]Assumethedatageneratingprocessof cfw_ y t followsthemodel , , | | 0, 1, .Thestatistica
JEN-WEN LIN, PhD, CFA
July 11, 2017
Time Series Analysis
1
Cryer and Chan (2010),Time Series
Analysis: With Applications in R, Second
Edition, Springer.
Amazon link:
http:/www.amazon.ca/Time-AnalysisApplications-Jonathan-Cryer
2
Seasonal
variation
Cyclic
1. See solutions for mid term practice posted before.
2. Steps of time series analysis taught in class:
ANSWER:
See the slides of Lecture 3 Box-Jenkins Approach.
Step 1: Understand the problem of interest, collect the data, graph the collected data to get
Midterm practice questions (July 2015)
A. DEFINITIONS
1. Discuss the steps of time series analysis discussed in class.
2. Describe the components of classical decomposition in time series analysis.
3. Define a weakly stationary time series.
4. Describe th
A Little Book of R For Time Series
Release 0.2
Avril Coghlan
May 27, 2015
Contents
1
2
How to install R
1.1 Introduction to R . . . . .
1.2 Installing R . . . . . . . .
1.3 Installing R packages . . .
1.4 Running R . . . . . . . .
1.5 A brief introduction
The Shape Parameter of a Two-Variable Graph
WILLIAM S. CLEVELAND, MARYLYN E. McGILL, and ROBERT McGILL*
The shape parameter of a two-variable graph is the ratio of the horizontal and vertical distances spanned by the data. For at
least 70 years this param
20 SIMPLE DESCRIPTIVE METHODS OF ANALYSIS
values. For example, by not joining successive points in Figs 1.4 and 1.5
we drew the readers attention to the gaps and jumps in the series which
are an intrinsic feature of the data. .
Another choice which can af
Date: July 26, 2017
JEN-WEN LIN, PhD, CFA
1. Vector autoregressions (VAR) is a generalization of
the autoregressive and moving average (ARMA)
process.
2. Christopher A. Sims (1980) introduced VAR to
econometricians and won the Nobel prize in 2011.
The ac
STA457 Practice Problems - Week 3
1) Question 2.7 on page 31 from the text book. Use Minitab for graphs and calculations.
2) Let cfw_Xt be the MA(2) process given by:
Xt = Zt + Zt-2 where Zt is WN(0, 2)
a) Find the autocovariance function and the autocorr
STA457 Practice Problems - Week 4
1) Let cfw_Xt be the AR(1) process given by:
X t = X t 1 + Z t
where Zt is WN(0, 2)
a) Find the autocovariance function and the autocorrelation function for this process when
= 0.9 . Is the process stationary?
b) Compute
A Hands-on Guide to Google Data
1
Seth Stephens-Davidowitz
Hal Varian
2
Google, Inc.
4
September 2014
Revised: March 7, 2015
5
Abstract
3
This document describes how to access and use Google data for social science research. This document was created usin
JEN-WEN LIN, PhD, CFA
July 28, 2017
This presentation aims at introducing a new onestep estimation method for estimating factor
loadings of alternative assets using appraisal
returns.
For the purpose of self-containedness, we will also
review the existing
STA457 Practice Problems - Week 6
1) Determine which of the following ARMA processes are causal and which of them are
invertible. In each case Zt denote white noise.
a) X t + 0.2 X t 1 0.48 X t 2 = Z t .
b) X t + 1.9 X t 1 + 0.88 X t 2 = Z t + 0.2 Z t 1 +
JEN-WEN LIN, PHD, CFA
AUGUST 14, 2017
STA457 assignment due on August 14th,
2017.
TA will come collect assignments in class at
around 7pm.
Final exam info:
Date: August 18th, 2017 (Friday)
Time: 7:00-10:00 pm
Location: EX200
State-space models, ori
JEN-WEN LIN PhD, CFA
Date: July 09, 2017
Box-Jenkins analysis
1
Start with a time series realization
1.
Identify a preliminary time series model
2.
Estimation of the model parameters
Understand the
problem of interest
Collect data
Plot time series data
JEN-WEN LIN, PhD, CFA
04/07/2017
1
Time/place: M,W 6-9pm/BA1160
Email: [email protected]
Textbook (optional):
Shumway & Stoffer (2010), Time Series Analysis and Its
Applications: With R Examples, Springer Texts in Statistics
Others:
Slides w
ARCH/GARCH MODEL
1.
NOTATION
An autoregressive conditional heteroscedastic process of order , denoted as (), may be given by
) = ) + ) ) ,
6
1
4 )54
,
)1 = 3 +
478
where ) ~ 0,1 and ) is sometimes referred to as the mean equation. Note that we requir