Lecture 2 Key Terms

Question 1

Static regression model

Answer 1

describes the contemporaneous relationship between variables y and z

Question 2

finite distributed lag model

Answer 2

describes the relationship between past realisations of the variable as well as the present

Question 3

stochastic process

Answer 3

a sequence of random variables indexed by time defined on a common probability space

Question 4

weak stationarity

Answer 4

mean, variance and autocovariances are stable; mean and variance are constant over time and the covariance only depends on the lag between the variables and not the initial starting point

Question 5

autocorrelation function / correlogram

Answer 5

characterises dependencies among observations, and depicts the length and strength of the memory of the process

Question 6

white noise process

Answer 6

a process with zero mean and constant variance

Question 7

serial (un)correlation

Answer 7

zero correlation across time periods

Question 8

strong exogeneity

Answer 8

assumption that the error at time t is uncorrelated with each explanatory variable across every time period

Question 9

stability condition

Answer 9

theta | < 1 in the ar(1) model. Maintains stationarity in the model and prevents high persistence

Question 10

weak dependence

Answer 10

autocorrelation tends towards zero as the gap between the variables increases

Question 11

Contemporaneous exogeneity

Answer 11

errors and their contemporary explanatory variables are uncorrelated