regression

Question 1

regression analysis

Answer 1

a statistical method for examining relationships among variables

Question 2

linear regression

Answer 2

a statistical model that assumes a linear relationship between two variables

Question 3

population linear regression model

Answer 3

decribes the relationship that holds between Y and X in the population

Question 4

X

Answer 4

the independent variable or regressor

Question 5

Y

Answer 5

the dependent variable

Question 6

Beta 0

Answer 6

the intercept, it measures the point at which the regression line intercepts the Y axis.

Question 7

Beta 1

Answer 7

the slope of the regression line. It measures the difference in Y associated with a one unit change in X

Question 8

u i

Answer 8

regression residual

Question 9

Prediction

Answer 9

using the observed values of a given variable to predict the value of another variable

Question 10

causal inference

Answer 10

to determine whether and to what extent a cause-and-effect relationship exists between variables

Question 11

causality

Answer 11

an action is said to cause an outcome in the outcome is the direct consequence of that action

Question 12

treatment group

Answer 12

recieve the treatment

Question 13

control group

Answer 13

does not recieve treatment (counterfactual)

Question 14

observational data

Answer 14

surveys, administrative records, financial reports

Question 15

cross-sectional data

Answer 15

• data collected at a single point in time for different entities
• reflects a snapshot of variables at that point
• we can use this data so study differences across intities in a single time period

Question 16

panel data

Answer 16

• data collected for multiple entities at multiple points in time
• captures the dynamics of change over time
  -allows for the analysisi of temporal effects across entities

Question 17

time series

Answer 17

• data collected for a single entity at multiple time points
• allows for the analysis of temporal effects and forecasting

Question 18

ordinary least squares (OLS)

Answer 18

it identifies the prameters that minimize the sum of the squared residuals

Question 19

residual

Answer 19

the vertical distance from the regression line

Question 20

the sign (±) on Beta 1 for an independent variable

Answer 20

the direction of its association with the dependent variable

Question 21

Central limit theorem

Answer 21

when the sample is large and properly drawn, the sample mean is distributed normally around the true mean

Question 22

standard error

Answer 22

it represents the average distance that the observed values fall from the regression line

Question 23

t-statistic

Answer 23

the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error

Question 24

95% confidence interval

Answer 24

an interval that is a function of the data that contains the true parameter value 95% of the time in repeated samples

Question 25

omitted variable bias (OVB)

Answer 25

the bias in the OLS estimator that occurs as a result of an omitted variable (Z)

Question 26

statistical inference entails

Answer 26

1. estimation of the coefficients of interest 2. hypothesis testing and confidence intervals

Question 27

R-squared

Answer 27

a measure of the regression model fit

Question 28

R-squared value of 1

Answer 28

perfect explanation of variance

Question 29

R-squared value of 0

Answer 29

the model explains none of the variance

Question 30

adjusted R-squared

Answer 30

a modified version of the R-squared that does not necessarily increase when a regressor is added to the regression

Question 31

control variable

Answer 31

controls for an omitted causal factor in the regression but itself not necessarily have a causal effect on Y

Question 32

endogeneity

Answer 32

a situation where the explanatory variable is correlated with the error term

Question 33

omitted variable bias (endogeneity)

Answer 33

when a model fails to include one or more relevant variables that influence the dependent variable

Question 34

selection bias (endogeneity)

Answer 34

the data sample is not randomly selected

Question 35

reverse causality or simultaneity (endogeneity)

Answer 35

two-way causation exists between independent and dependent variables

Question 36

measurement error (endogeneity)

Answer 36

if an independent variable is measured with error

Question 37

difference in difference (DiD)

Answer 37

this is used to asses the causal effect of an event by comparing the set of units where the event happened (treatment group) in relation to units where the event did not happen (control group).

Question 38

knowledge diffusion

Answer 38

disclosure of technical knowledge in the patent document