Final Exam Flashcards

1
Q

What is an eigenfactor?

A

The degree to which eigenvectors are blown up or squashed.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What happens to the AR(1) model when |B(1)| = 1?

A

The process drift off with infinite variance.

The so-called “random walk”.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

When the eigenvalues of A(1) are less than 1, what is true of VAR(1)?

A

It is stable.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What is a stationary process?

A

One that’s autocovariances do not depend on time.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Mathematically, how does the MA(1) process show that older shocks matter less?

A

The A(1) coefficients, which are necessarily less than one, are raised to increasingly higher powers.

So they approach zero.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

The autocorrelations of the stochastic process can be reformulated to create a (…)

A

VAR

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

When can you use OLS on a systems of equations?

A

When all the regressors are the same.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

How do you estimate the betas of a VAR?

A

(i) Force in the form: X = BZ + U; (i) use the formula: Bhat = XZ’(ZZ’)^(-1).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What does a correlation between a shock to inflation and GDP mean in the real world?

A

That the shock to GDP is also a shock to inflation in that % of cases.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

What does theory say an IRF should measure?

A

The isolated effect of a shock to one variable on another.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

If we don’t orthogonalize, what does the IRF actually measure?

A

The cumulative effect of a shock to one variable on all other variables in the system.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What do all positive definite matrices have?

A

A Choleski Decomposition

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

When is a matrix positive definite?

A

When: z’A(z) > 0

or: all eigenvalues > 0

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

What does positive defintieness mean, intuitively?

A

That matrix A will not reflect vector x in the opposite direction.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

What is true of the variance-covariance matrix of w(t-i)?

A

It is diagonal.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

What is the orthogonal representation of our VAR?

A

x(t) = Mu + Sum(O(i)w(t-i))

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

How does one caculate the effect of a shock post-orthogonalization?

A

IRF: x(t) = Mu + o(i)P

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

What is the decomposition of w(t-i)?

A

w(t-i) = P^(-1)u(t-i)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

What is the decomposition of O(i)?

A

O(i) = o(i)P

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

What does the orthogonalized shock w(1, t) depend on?

A

The unorthogonalized shock u(1, t)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

What does the orthogonalized shock w(k, t) depend on?

A

All of the unorthogonalized shocks u(1,t), u(2,t), … ,u(k,t)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

What is the heirarchical structure of assumptions?

A

That the order of the variables in an orthogonalized VAR matters, with the first being most important and affected by no other variables.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

What are the two main identification strategies for dealing with the heirarchical structure of assumptions?

A

(i) Argue that the order of the variables is jusfied by economic thoery; or (ii) caculate the orthogonalized VAR for every possible ordering and show that the answer does no depend on it.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

What is L^(2)x(t)?

A

x(t-2)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

What is the equation for the theorectical VAR?

A

x(t) = M(1)x(t-1) + psi(t)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

Why can we remove the constant from the theorectical VAR?

A

Because we only need to cnsider data as deviations from the mean.

At least as far as identification is concerned.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

How can the theorectical VAR model be modified to allow for simultaneous effects to the variables?

A

M(0)x(t) = M(1)x(t-1) + psi(t)

This also allows shocks to be identified seperately as Sum(psi) = I.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

Why can’t we allow for theorectical shocks to be correlated?

A

Because it leads to models that don’t make any definite predictions.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

How do we identify the theorectical VAR with the empirical one?

A

Solve u(t) = M(o)^(-1)psi(t)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

What does var[u(t)] equal?

A

E[u(t)u(t)’] = Sum(u)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

What does u(t) = M(o)^(-1)psi(t) solve to?

A

Sum(psi) = M(o)Sum(u(t))[M(o)]’

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

What matrix contains our known quantities?

A

Sum(u)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

What matrix term contains our unknown quantities?

A

M(0)Sum(psi)[M(0)]’

34
Q

Post-identification, how many unknown quantities are there?

A

#unknowns = K^2 + K(K+1)/2

The K^2 is the # in M & K(K+1)/2 is the # above the Sum(psi) diagonal.

35
Q

Post-identification, how many known quantities are there?

A

#knowns = K(K+1)/2

36
Q

What is #unknowns - #knowns?

37
Q

How many addtional knowns do we need to identify a VAR (that we don’t have)?

38
Q

What are the two innocuous restrictions we can impose to reduce the number of additional knowns needed?

A

(i) that Sum(psi) is diagonal (decreasing unknowns by K(K+1)/2); and (ii) that the diagonal elements of M(0) are all 1.

Result: only K(K+1)/2 unknowns

39
Q

What is the case-specific restriction we can impose to identify VARs?

A

If two variables are known to be unrelated, set their (contemporaneous) model coefficents to zero.

e.g. Taxes in year one will not depend on GDP in year one.

40
Q

What is the trade-off we face when choosing the order of a VAR model?

A

The one between in-sample fit and out-of-sample accuracy.

41
Q

How many degress of freedom does y = x + z have?

42
Q

What are the degrees of freedom for n=5, mu=1.

43
Q

What happens to a model’s fit when you increase the number of parameters?

A

It goes up.

44
Q

What happens to a model’s degrees of freedom when you increase the number of parameters?

A

They go down.

45
Q

What is parismony?

A

Using the fewest parameters possible that result in a statisfactory compromise between fitting the data and keeping the analysis simple.

46
Q

How do we assess the likelihood that a model is true?

A

By comparing them to a model with a variance of sigma^2.

47
Q

What is the step-by-step procedure for assessing whether a model is true?

A

(i) Obtain the residuals; (ii) caculate each residual’s probability by comparing with a normal distribution; and (iii) multiply the probability to obtain the model’s likelihood.

48
Q

What two concepts does VAR specification rely on?

A

Degrees of freedom and model likelihood

49
Q

What is true of complex models?

A

They consume more degrees of freedom and are therefore less likely to be correct.

50
Q

What is the Akaike criteria for specifying VARs?

A

AIC = -2[log(L) - k/T]

Where L is likelihood, k is the # of paramters, and T is the # of obs.

51
Q

What is the Hannan-Quinn criteria for specifying VARs?

A

HQ = -2[log(L) - k*log(log(T))]

Where L is likelihood, k is the # of paramters, and T is the # of obs.

52
Q

What is the Schwartz criteria for specifying VARs?

A

SC = -2[log(L) - K*log(T)/T]

Where L is likelihood, k is the # of paramters, and T is the # of obs.

53
Q

What is real investment?

A

The aggregate level of investment from a macroeconomic point of view.

54
Q

What is the formula for expenditure?

A

E = I + C +G

55
Q

How do you derive the IS curve?

56
Q

If an investment has a return of 6% and inflation is -2%, what is my real return?

57
Q

How does inflation alter investment decisions?

A

It causes people to choose high risk, high return endeavours.

58
Q

What is the consumption function?

A

C = c(0) + c(1)[Y-T]

Where c(0) is autonomous consumption and c(1) is MPC.

59
Q

What is the LM curve?

A

M = P*L(i-r, Y)

60
Q

What is the investment function?

A

I = v(0) - v(r)

Where v is the elasticity of investment.

61
Q

How is “crowding out” due to government spending represented in the IS-LM model?

A

It causes the IS-LM model to reach a new equailibirum at a higher interest rate and slightly higher output.

Without it, Y would be higher and r would be unchanged.

62
Q

How is an increase in output automatically dampened by other variables?

A

An increase in Y causes an increase in M(d), which in turn increases r and thereby reduces I. The reduction in I reduces E and therefore Y.

But not enough to fully erase the gain to Y (need stage 2 for that)

63
Q

What is aggregate demand?

A

The relationship between the price level and output that is consistent with both the goods and money market equilibrium.

64
Q

What is the one shift in the IS-LM model that will not also shift AD?

A

When LM shifts due to prices.

65
Q

What two things do Type I Firms use to set their prices?

A

(i) The general price level (P); & (ii) the phase of the business cycle (Y-Ybar).

Given: p(1) = P + a(Y-Ybar)

66
Q

What do Type II Firms use to set their prices?

A

Forecasts of the price level and growth.

Given: p(2) = P(e) + a(Y(e)-Ybar)

67
Q

How can the pricing decisions of Type II firms be simplified?

A

If the firms set their prices in the long-run, then Y(e)-Ybar = 0 and p(2) = P(e).

68
Q

What is true of firms in the long-run?

A

They are all Type I.

69
Q

What is a recession mathematically?

70
Q

What are adaptive expectations?

A

P(e) = P(t-1)

71
Q

What is the steady state mathematically?

A

Where Y = Ybar & P = P(e)

72
Q

Why do we reframe the IS-LM-AS model as changes to output and price level?

A

To simplify it by removing regular and predictable increases in growth and expectations.

73
Q

What is NIPA

A

National Income and Production Accounts

Consistent of 7 accounts showing the distribution of income.

74
Q

What is double-entry accounting?

A

That each data term is entered twice, once on each side of the table.

75
Q

What is GDI?

A

Measure of the incomes and costs incurred in the production of GDP.

It should be equal to GDP.

76
Q

When should durables be considered an investment?

For reference, PCE is divided in durables, non-durables, and services.

A

Over shorter time spans

77
Q

What is Gross Private Domestic Investment?

A

All investment in fixed assets by firms and non-profit organizations

78
Q

How do you caculate NDP?

A

NDP = GDP - CFC

CFC: Consumption of Fixed Capital

79
Q

When estimating a VAR, what time do we start at?

80
Q

What is orthogonalization, exactly?

A

Forcing the residuals to have a diagonal variance-covariance matrix.

81
Q

What is true about the economy when there are no Type II firms?

A

There are no recessions as Y = Ybar.