Lecture 1 (intro)

Question 1

Multiple linear regression model

y = β₀ + β₁x₁ + β₂x₂ + ε

Answer 1

Uses multiple independent variables to predict the value of a variable

Question 2

ARMA (Autoregressive Moving Average) model

Xt = φ₁Xt-1 + Zt + θ₁Zt-1

Answer 2

Uses time series data to predict future trends

Question 3

ADL (Autoregressive Distributed Lag) Model

Yt = β₀ + β₁Yt-1 + δ₁Xt + εt

Answer 3

Captures relationship between a dependent variable Y and multiple independent variables (X) across different time lags

e.g. combination of ARMA and linear

Question 4

GARCH (Generalized Autoregressive Conditional Heteroscedasticity)

σ²t = ω + α₁ε²t-1 + β₁σ²t-1

Answer 4

Capture the variance of time series data

Question 5

Quantile regression models

Answer 5

Estimate quantile regressions e.g. 10th percentile

Question 6

Cross section data

Answer 6

Sample of data taken from an individual at a given point in time

Question 7

Time series data

Answer 7

observations on variables over time

Question 8

Panel data

Answer 8

Time series for each cross-sectional member

Question 9

Covariance