Lecture 1 Flashcards
General Keynesian consumption function equation
Y=β₀+β₁x + ε
β₀ is intercept
β₁is MPC
ε is random variable to represent other factors influencing consumption.
2 moments
Mean and variance
Mean notation
E(x) (expected value) or μ
The area under the density function (curve) can be used to find probabilities e.g of income being between A and B.
Covariance
Covariance looks at how variables x and Y are associated
Can covariance be positive or negative or both?
And what does positive and negative mean
Both
When positive - when X increases Y also increases
When negative - when X increases Y decreases
Correlation coefficient
Same as covariance but also shows strength of relationship.
(both dont mean causation between 2 variables!)
Correlation values
-1<=p<=1
1 is strong pos
-1 is strong neg
0 is no relationship
E.g income and consumption correlation coefficient would be near 1 as strong positive relationship
Normal distribution notation
X~N (μ,σ²)
(Mean and variance)
Probability of getting a value of X within 1SD, 2SD, 3SD of the mean in a normal distribution
1SD 68%
2SD 85%
3SD 99.7%
Z equation for normal distribution if mean is 0, and SD is 1…
what is this also known as
X-μ / σ ~ N(0,1)
So value - mean / standard deviation
A standard normal!!! A special case of normal distribution.
2 main types of data
Cross sectional
Time series
Cons of cross sectional data
Costly and time consuming.
Sampling distribution of sample mean
X~N (μ,σ²/N)
(the mean of the sample we took is just 1 value from a bunch of possibilities)
Central limit theorem
If sample size N is large, then there will be normal distribution