1. Cross sectional dependence, times series dependence, panel structure

Memory, no memory, short memory, long memory, white noise process, random walk, law of large

numbers, central limit theorem, consistency, information asymmetry, adverse selection, moral hazard

AR process, MA process, ARMA process

Contagion: when is full connectedness is desirable/ undesirable?

Summary:

1.

Cross sectional dependence refers to the relationship/dependence between units at a given point in

time. For example the dependence between countries A, B, C in period t.

Times series dependence refers to the dependence between one unit and more time periods, or, in

other words the behavior of one unit during time, over a particular period. Example: A over period t,

t+1, t+2, ... t+n.

Panel structure is a cross sectional dependence over time, meaning, dependence between more units

over a period of time. Ex: A, B, C over period t, t+1, … t+n.

2.

Memory – (dependence over time) another way of saying to which extend the past is useful in the

future. Ex: price has memory means that the price from the past is useful in predicting the price in the

future.

No memory (white noise) - there is no dependence over time, the past doesn`t help in the future.

Short memory –

Long memory – everything is important

White noise process -refers to the fact that there is no correlation between the random variables.

Random walk – the random variables have the same distribution and they don`t depend one on each

other, so the past cannot be used in order to predict the future. Ex: the future price of a stock cannot be

determined with the help of the past price. “The value today should not be different from yesterday If it

is different, there must be new information”(Lecture)

Law of large numbers – as the sample of the population grows, its mean will get closer to the population

average.

Central limit theorem – the average of the sample tends to be normally distributed. The sum of the

variables will have a normal distribution, regardless of how variables change.

Importance-

Consistency – if �̂� converges to β, it means that �̂� is a consistent estimator. If it converges to something

else, then is an inconsistent estimator.

Information asymmetry – a situation in which one party knows more than the other, or has more and

better information about a common interest, so that the one that has more information can benefit

from the one that doesn`t. Ex: I sell you a non-functional sewing machine. I benefit from your lack of

knowledge. I generate unnecessary loss on your side. I am better off and you are worse off. Can take

two forms: adverse selection and moral hazard.

Adverse selection – the case in which one person is insured and he/she behave differently. It is prior to

the transaction, so sometimes it might prevent the transaction from occurring.

Moral hazard -

AR process – current values are influenced by the past values. There exists memory that is important for

explaining the future. (1) the past value is important, (2) the past two values are important

MA process – refers to the fact that the shock generated cannot be absorbed in one single period, so it

has to be distributed on more periods. For example a hock that occurred in period t-3 will affect t-2 but

also t-1. The shock has to be distributed between a wider time period

ARMA process – the past information influences the present and, by splitting the shocks over multiple

periods. Takes the characteristic of AR and MA process.

3. I would say that full connectedness is desirable in the case in which there is full transparency and the

information is equally known by all the participants of the network. This way, the entities will trust each

other and the level of the confidence is high. ( I trust you that you don`t have today the money that you

owe me, but I know for sure that tomorrow you will pay me back.) the risk burden is shared by all the

participants. Helps weak entities to stay in.

I would say that full connectedness is not desirable in the case is which there does not exist full

transparency, and the information is not known by everyone. The entities will not trust each other. (You

tell me that you will pay me back tomorrow but you don`t, so I will never borrow you money again. ) The

risk burden affects only one particular entity that eventually will collapse.