QE Time Series

Question 1

Cointegration

Answer 1

when two or more time series variables share a common stochastic trend

Question 2

Common trend

Answer 2

A trend shared by two or more time series

Question 3

Dickey Fuller Test

Answer 3

A method for testing for a unit root in a first order regression AR(1) model

Question 4

Distributed lag model

Answer 4

a regression model in which the regressors are current and lagged values of X

Question 5

Endogenous variable

Answer 5

A variable that is correlated with the error term

Question 6

Error Term

Answer 6

The difference between Y and the population regression function

Question 7

autocorrelation

Answer 7

the correlation between a time series variable and its lagged value

Question 8

Autocovariance

Answer 8

The covariance between a time-series variable to its past (that is lagged) values

Question 9

correlation

Answer 9

the unit free measure of the extent to whch two variables move together

Question 10

Forecast Interval

Answer 10

An interval that contains the future value of a time series variable with a prescribed probability

Question 11

Granger Causality Test

Answer 11

A procedure for testing whether current and lagged values of one time series help predict future values of another time series

Question 12

Homoskedasticity

Answer 12

The variance of the error term, conditional on regressors, is constant

Question 13

Heteroskedacity

Answer 13

The variance of the error term conditional on regressors is not constant

Question 14

imperfect multicolliniarity

Answer 14

the condition in which two or more regressors are highly correlated

Question 15

Instrumental variables regression

Answer 15

A way to obtain a consistent estimator of the unknown coefficients of the population regression function when the regressor, X, is correlated with the error term

Question 16

Non-stationary

Answer 16

the joint distribution of time series variable and it’s lags changes over time

Question 17

OLS residual

Answer 17

The difference between Y and the OLS regression line

Question 18

Omitted Variable Bias

Answer 18

The bias in an estimator that has arisen because a variable that is a determinant of y and is correlated with the regressor has been omitted from the regression

Question 19

Order of integration

Answer 19

The number of times a time series variable must be differenced to make it stationary. A time series variable that is integrated of order p must be differenced p times and is denoted I(p)

Question 20

p value

Answer 20

the smallest significance level at which the null hypothesis can be rejected.

Question 21

perfect multicollinearity

Answer 21

One of the regressors is the exact linear function of another of the regressors

Question 22

Pseudo out of sample forecasting

Answer 22

A forecast computed over part of a sample using a procedure as if the sample has not yet been realised

Question 23

R^2

Answer 23

In a regression, the fraction of the sample variance of the dependent variable that is explained by the regressor

Question 24

Random Walk

Answer 24

A time series regression where the value of the variable equals the value of it in a previous period plus a random error

Question 25

Random walk with drift

Answer 25

a random walk in which the change in variable has a non-zero mean but it otherwise unpredictable

Question 26

Randomized controlled experiment

Answer 26

participants are randomly assigned to a control group which receives no treatment, or a treatment group, which receives a treatment.

Question 27

serially uncorrelated

Answer 27

a time series variable where all autocorrelations equal zero

Question 28

simultaneous causality bias

Answer 28

When, in addition to the causal link of interest from X to Y, there is a causal link from Y to X. Simultaneous causality makes X correlated with the error term in the population regression of interest

Question 29

Stationary

Answer 29

The joint distribution of a time series variable and it’s lagged values does not change over time

Question 30

Stochastic trend

Answer 30

A persistent but random long-term movement of a variable over time

Question 31

Type 1 error

Answer 31

The null hypothsis is TRUE but it’s REJECTED

Question 32

type 2 error

Answer 32

The null hypothesis isn’t rejected but it’s FALSE

Question 33

Granger causality test

Answer 33

In an ADL model, an indication of how useful X values are in predicting Y. It’s an F-test

Question 34

ADL Model

Answer 34

Autoregressive Distributed Lag model, where there is another variable in an AR(p) model

Question 35

Mean Squared Forecast Error

Answer 35

Expected value of the squared difference between the fitted values implied by the predictive function B(hat) and the unobserved function B