Time Series
Fall 2013
Lecture Notes
1
Lecture 10
6
Stationary Processes in the Frequency Domain
Derivation of Special Density Function
3 Autoregressive Process AR(1):
Xt = Xt1 + Zt ,
2
with X
=
t
z2
.
Time Series
Fall 2013
Lecture Notes
1
Lecture 2
2
2.4
Time Series Analysis and Forecasting
More Examples of Time Series
Los Angeles Annual Rainfall
W
.S
ha
r
ab
at
i
# The package TSA should be instal
Time Series
Fall 2013
Lecture Notes
1
Lecture 13
9
Linear Systems
9.1
Linear Systems in the Time Domain
Identify a model for the input and output.
Input Xt
Example 1. Xt :
reactor.
Physical System
Te
Time Series
Fall 2013
Lecture Notes
1
Lecture 1
1
Time Series Analysis and Forecasting
1.1
Introduction to Time Series
1.2
Learning Objectives
Learn about different time-series forecasting models: mo
Time Series
Lecture Notes
Fall 2013
1
Lecture 14
10
State-Space Models and The Kalman Filter
10.1
State-Space Models
Observation = Signal + Noise.
In statistical language, this is equivalent to
Data =
Time Series
Lecture Notes
Fall 2013
1
Lecture 8
5
Forecasting
5.1
Introduction
Introduction
Forecasting: The prediction problem from the observed values of a time series at
past points X1 , , Xt pred
Time Series
Lecture Notes
Fall 2013
1
Lecture 12
8
Bivariate Processes
8.1
Cross-Covariance and Cross-Correlation
Consider (Xt , Yt ), where Xt and Yt are maximum/minimum daily temperature
respective
Time Series
Fall 2013
Lecture Notes
1
Lecture 3
3
Probability Models for Time Series
3.1
Stochastic Processes and Their Properties
Stochastic (Random) Process
For each t, Xt is treated as a value of
Time Series
Fall 2013
Lecture Notes
1
Lecture 7
4
Fitting Time Series Models In The Time Domain
4.3
Fitting an Autoregressive Process
Estimating Parameters of an AR Process
1 Ordinary Regression Model
Time Series
Fall 2013
Lecture Notes
1
Lecture 11
6
Spectral Analysis
6.1
Methods for Estimating the Spectrum
Fourier Analysis
The approximation of a function by taking sum of sine and cosine terms, is
Time Series
Fall 2013
Lecture Notes
1
Lecture 6
4
Fitting Time Series Models In The Time Domain
4.1
Estimating Autocovariance and Autocorrelation Functions
Estimating Autocovariance and Autocorrelatio
Time Series
Fall 2013
Lecture Notes
1
Lecture 5
3.3
Invertible and Stationary Processes
Stationarity of AR(p)
AR(p)
Xt = 1 Xt1 + + p Xtp + Zt .
Xt = (1 B + + p B p ) Xt + Zt .
Let (B) = 1 1 B p B p .
Time Series
Fall 2013
Lecture Notes
1
Lecture 9
6
Stationary Processes in the Frequency Domain
6.1
Introduction
The autocovariance and autocorrelation functions describe the evolution of a process th
Time Series
Lecture Notes
Fall 2013
1
Lecture 4
3
Probability Models for Time Series
3.3
Properties of The Autocorrelation Function
For the stationary stochastic process X(t) or Xt we have
=
= 2.
0
1
about where current theory may fall short. The [How Bad Are
Mistakes?] discussions are great. The quantitative aspects of the
mistakes are not found in alternative books. Evgeny Lyandres Boston
Univer
world so that we can act in the world. Thus, one should prefer a theory
that, ceteris paribus, yields several policy implications to one that yields
few. 4.6 Parsimony Parsimony is a criterion that is
individualized Study Plan. With the Study Plan, students learn to focus
their energies on the topics they need to be successful in class, on exams,
and, ultimately, in their future careers. Powerful I
(e.g., President Bushs appointees, their partisanship, judicial records,
etc.). Put differently, causal explanations are explicitly theoretical
whereas descriptive explanations are implicitly theoreti
regularities and referring to it as a theory. I adopt a more restricted and
explicit definition of theory, making use of the distinction that Zinnes
drew between collections of internally consistent s
present body of knowledge is not Truth, but rather a research program
that may or may not have rivals, may or may not be progressive (see
below), etc. Thus, the face validity of assumptions criterion
knowledge claims about politics then we need to specify a set of criteria
that enables us to evaluate those claims. That is, since political scientists
are interested in using science to develop knowl
to professors as downloadable PDFs and Word files at
www.pearsonhighered.com/irc. It is also available in printed form, and
in the TestGen programan easy-to-use testing software that allows
instructor
When it is not, then the theoryas presently constitutedis not
considered useful for explaining the phenomenon under investigation.
This discussion requires a couple of clarifications. Lakatos (1970)
r
here should be considered. 4.2 Generality The next criterion to consider
is generality. This criterion concerns the breadth of the scope of the
explanans (i.e., the phenomenon we are trying to explain
REAL-WORLD APPLICATION 387 CHAPTER 12 Capital Budgeting
Applications and Pitfalls 389 CHAPTER 13 From Financial Statements
to Economic Cash Flows 445 CHAPTER 14 Valuation from
Comparables and Some Fin
different implication). Thus, we tentatively accept the theory and
continue testing. Lakatos (1970) labeled Poppers argument naive
falsificationism because Popper did not appreciate that we cannot use
Students often enter the class with a fear of formulas, of theory, and of
jargon. The author believes that deep down, finance is simple and that
everyone can understand it. Welch wants to dispel such
not an abstraction, but an agglomeration of ideas that have value in the
conduct of everyday business and everyday life. Mei Zhang Mercer
University xvii Because practice with homework problems is cru
gain by getting paid. But despite the prospect of mutual gain, it is not
easy for them to cooperate.The agent has interests of his ownfor
example, in leisure or professionalismthat give him incentives
efficiently. A more detailed description can be found on pages xviiixix.
SOLUTIONS MANUAL FOR THE STUDENT This manual is the same
as the solutions portion of the Instructors Manual with Solutions. It