midterm 3

Question 1

temporal ordering

Answer 1

past values can influence future values, but not vice versa. In time series analysis, this distinguishes it from cross-sectional data.

Question 2

data frequency

Answer 2

the interval at which data points are collected: annual, quarterly, monthly, daily

Question 3

static model

Answer 3

a regression mode where the effect of the explanatory variable Z on the dependent one is immediate, with no lagged effects.

yt=beta0 + beta1Zt + ut

Question 4

finite distributed lag model FDL

Answer 4

a regression model that includes both current and lagged values of an explanatory variable to capture delayed effect

yt = alfa0 + delta0Zt + delta1Zt-1 + ut

Question 5

order of FDL

Answer 5

the number of lagged values of the explanatory variable included in an FDL model.
if its 2 it means Zt, Zt-1, Z-2

Question 6

impact propensity/ impact multiplier

Answer 6

the immediate effect of a one-unit change in the explanatory variable Z on the dependant variable

Question 7

long-run propensity

Answer 7

the total cumulative effect of a permanent one-unit change in Z on y over time

Question 8

trending time series

Answer 8

a time series that shows a consistent upward or downward movement over time. like GDP

Question 9

spurious regression

Answer 9

occurs when we ignore a trend, and suggest a relationship when there is none. Leads to biased estimation. Affect both the dependent and independent. Adding the appropriate time trend eliminates the problem

Question 10

linear trend

Answer 10

steady increase or decrease
trendt= alfa0 + alfa1t

Question 11

quadratic trend

Answer 11

u shaped, accelerating or decelerating
trend= alfa0 + alfa1t + alfa2t”2

Question 12

exponential trend

Answer 12

rapid (%) growth
trendt= alfa0e”alfa1t

Question 13

detrended variables

Answer 13

removing long-term trends tp focus on short-term variations. Helps avoid spurious regression result.

Question 14

hump-shaped (inverted u shape) trend

Answer 14

a trend where a variable initially increases, reaches a peak and then decreases.

Question 15

u shaped trend

Answer 15

a trend where a variable decrease initially, hits a min, and then increases

Question 16

quarterly data

Answer 16

time series data collected every 3 months

Question 17

seasonality

Answer 17

recurring patterns in a time series tied to specific times of the year

Question 18

deseasonalised data

Answer 18

data adjusted to remove seasonal effects, making it easier to analyse trends and other factors. extending the model with seasonal dummies

Question 19

seasonally adjusted data

Answer 19

similar to deseasonalised data, but explicitly adjust to remove both upward and downward seasonal fluctuations.

Question 20

seasonal dummies

Answer 20

binary variables representing specific seasons in regression models. D1, D2, D3, D4 one of them is the based season

Question 21

base/benchmark season

Answer 21

the omitted season when using seasonal dummies. All the other seasonal effects are interpreted relative to this base season

Question 22

contrast variable

Answer 22

alternative to seasonal dummies to avoid the dummy variable trap (perfect collinearity). Express the difference between seasons instead of using absolute levels.

Question 23

standardised OLS coefficient (regression)

Answer 23

its obtained after standardising all the variables. Involves converting variables to have a mean of 0 and a standard deviation of 1. This makes the coefficient scale-independent, allowing comparison across variables with different units of measurement

Question 24

relative importance measure

Answer 24

the higher the absolute value of the standard OLS coef. the more important is the particular regressor in terms of dependant var.

Question 25

stochastic time series analysis

Answer 25

studies time series with inherent randomness, focusing on autoregressive (past levels affect the present) and moving average (learning from the error terms in the past) structure. Appropriate only for short-term forecasting. The series must be stable/stationary

Question 26

self-generative structure

Answer 26

the present value is influenced by its own past values and past shocks or errors.

Question 27

autoregressive process (AR)

Answer 27

the current value (yt) depends on its previous values (yt-1) and a random error term (ut) p lag

Question 28

moving average process (MA)

Answer 28

the current value is influenced by the past errors term (ut-1) q lag

Question 29

ARMA (p,q) model

Answer 29

combines AR and MA components into a single model

Question 30

ARIMA (p,i,q)

Answer 30

extends the ARMA model to handle non-stationary processes by differencing the data i times to achieve stationarity p: order of AR i: number of differences q: order of MA

Question 31

stationary/stable time series

Answer 31

a time series is stationary if its mean, variance and autocovariance do not change over time

Question 32

autocovariance

Answer 32

the covariance betwen 2 values of the time series separated by k time units

Question 33

autocorrelation function ACF

Answer 33

measures the correlation between yt ( immediate influence) and yt-k (indirect)

Question 34

partial autocorrelation function PACF

Answer 34

measures the direct effect between values, removing the indirect effect (intermediates)

Question 35

correlogram

Answer 35

graph showing ACF and PACF for different lags

Question 36

white noise

Answer 36

constant expected value and constant variance whose subsequents are uncorrelated and independent. random series with constant mean and variance, uncorrelated errors. no autocorrelation exists between observations

Question 37

ljung-box test

Answer 37

test if residuals are white noise. H0: no autocorrelation, white noise H1: there is at least 1 autocorrelation

Question 38

random walk

Answer 38

AR (1) process with unit root, nonstationary, xero constant. Current value depend solely on the previous value + a random error. ACF not collapsing to zero

Question 39

unit root process

Answer 39

non stationary, shocks persist indefinitely

Question 40

trend stationary process

Answer 40

stabilised by removing a deterministic trend. Turning it into a stationary

Question 41

difference stationary process

Answer 41

a non stationary, but can be made stationary by taking the first ( higher order) differences, meaning differencing

Question 41

augment dickey-fuller test

Answer 41

to determine if a time series has unit root, by adressing potential autocorrelation in the residual. Add a lagged difference of the dependent ( d_yt) problem: the lag order (p) is unknown and has to be determined somehow.

Question 42

unit root test

Answer 42

checks stationarity by testing for unit roots

Question 43

box-jenkins algorithm

Answer 43

for ARIMA step1: check the stability of the process step2: id needed, stabilise the time series with appropriate transformation (detrending= -trend; differencing) step3: determine the (p;q) orders for the ARIMA model and run the estimation on the transformed stationary data (akaike, or z test) step4: model diagnostics, the residual must be WN (correlogram, ljung-box test) step5: forecast future out of sample values of the time series = invert all the transformations which were previously applied

Question 44

integrated process I(i)

Answer 44

a series that becomes stationary after i differences