QUESTION 1

Autocorrelation function is invariant in time, i.e. correlation between error e(t) and e(t-k) only depends on k not t.

Yes

No

1 points

QUESTION 2

Autocorrelation always decays exponentially with k between errors e(t) and e(t-k)

Yes

No

1 points

QUESTION 3

In a linear model y=x*beta+e, with T observation, k bona fide exogenous variables, and n lagged dependent variables, the covariance matrix of errors has dimensions

A.Tx(k+n+1)

B.Txk

C.TxT

D.(k+1)x(k+1)

3 points

QUESTION 4

Autocorrelation function r(k) always depends only on the distance between errors e(t) and e(t-k), not the time t.

Yes

No

1 points

QUESTION 5

Durbin Watson test statistic is from chi-square distribution

Yes

No

1 points

QUESTION 6

Weakly stationary condition is impossible to prove or test, it's a pure theoretical concept

Yes

No

1 points

QUESTION 7

Unless both y and x are stationary, OLS cannot be applied to a linear model y=x*beta+e

Yes

No

2 points

QUESTION 8

ARIMAX and regARIMA are synonyms

Yes

No

1 points

QUESTION 9

Apply backshift operator (1-2B+4B^2) to x(t-2)

A.x(t)-2 x(t-1)+4 x(t-2)

B.x(t-2)-2 x(t-2)+4 x(t-2)

C.x(t-2)-2 x(t-2)+4 [x(t-2)]^2

D.x(t-2)-2 x(t-3)+4 x(t-4)

2 points

QUESTION 10

Regression with ARIMA errors is an inferior model to ARIMAX

Yes

No

1 points

QUESTION 11

GARCH is a stochastic volatility model because it has a stochastic term r(t-1)^2, a square of return

Yes

No

1 points

QUESTION 12

Seasonality is a strictly annual pattern, i.e. the time series vary with season of the year

Yes

No

1 points

QUESTION 13

In trend and seasonality decomposition, the trend and seasonality are added to recover the original time series

Yes

No

2 points

QUESTION 14

low pass filter Removes low frequency components from the signal, and passes only high frequency components

Yes

No

1 points

QUESTION 15

Simple moving average is an example of low pass filter

Yes

No

1 points

QUESTION 16

Seasonality should never be removed from y or x variables, and instead SARIMA model should be applied to handle it in your model

Yes

No

1 points

QUESTION 17

Update parameter lambda in EWMA volatility metric cannot be estimated, it's arbitrarily chosen by an expert based on experience or industry practice

Yes

No

1 points

QUESTION 18

Pick the components of GARCH estimate of volatility v(t)

There are more than one answer to a question

A.previous estimate of volatility v(t-1)

B.Long run variance

C.square return r(t-1)

D.squared return r(t)