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Q:

What happens if we include variables in our OLS regression that don't belong?

A:

OLS remains unbiased, but will not have minimum variance

Q:

Autoregressive distributed lag models include

A:

Current and lagged values of the error term.

Q:

The interpretation of the slope coecient in the model lnYi = β0 + β1 ln(Xi) + ui is as follows

A:

a 1% change in X is associated with a β1% change in Y

Q:

Consider the following AR(1) model Yt = 100 + 0:5Yt1 + ut with the error terms being a white noise series with variance 2.

A:

If YT = 100 then the forecast of YT+1 given information at time T is 150

Q:

The interpretation of the slope coecient in the model lnYi = β0 + β1Xi + ui is as follows:

A:

a 1% change in X is associated with a β1% change in Y

Q:

The regression R2 is a measure of

A:

the goodness of t of your regression line.

Q:

The t-statistic is calculated by dividing

A:

the estimator minus its hypothesized value by the standard error of the estimator.

Q:

What types of data generally exist?

A:

1. Cross-sectional

2. Panel data

3. Time-series

Q:

What is the definition of panel data?

A:

A panel dataset contains observations on multiple entities (individuals, states, companies. . . ), where each entity is observed at two or more points in time. Hypothetical examples:

- Data on 420 California school districts in 1999 and again in 2000, for 840 observations total.
- Data on 50 U.S. states, each state is observed in 3 years, for a total of 150 observations.
- Data on 1000 individuals, in four dierent months, for 4000 observations total.

Q:

For what do we need to calculate the t-statistic or the t-ratio?

A:

If we want to test whether a mean of a variable Y (i.e., Y) with n observations is equal to a certain value (νY,0),

Q:

What is panel data?

A:

- we can pool random cross sections and treat similar to a

normal cross section. New dimension: time dierences. - we can follow the same random individual observations over

time known as panel data or longitudinal data

Q:

The interpretation of the slope coefficient in the model Yi = β0 + β1 ln(Xi) + ui is as follows:

A:

a 1% change in X is associated with a β1% change in Y.

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