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1
Q

Formula for t-stat

A

t = [r √(n-2)] / [√(1-r^2)]

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2
Q

Confidence Intervals

A

Predicted Y +/- (critical t-value)*(standard error)

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3
Q

R^2 =

A

RSS/SST or (SST - SSE) / SST

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4
Q

RSS/SST =

A

(SST - SSE) / SST or R^2

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5
Q

SST =

A

RSS + SSE

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6
Q

MSR =

A

RSS / k ; (k = # of independent variables)

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7
Q

MSE =

A

SSE / (n-k-1) ; (k = # of independent variables)

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8
Q

SEE =

A

√(MSE) = Standard Error of Estimate

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9
Q

F =

A

MSR/ MSE or (RSS/k) / (SSE/(n-k-1))

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10
Q

Conditional Heteroskedasticity… its effect…

A

Residual variance related to level of independent variables… Too man Type 1 errors

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11
Q

Type 1 errors

A

The incorrect rejection of a true null hypothesis (a “false positive”)

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12
Q

Type 2 errors

A

The failure to reject a false null hypothesis (a “false negative”)

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13
Q

Serial Correlation… its effect…

A

Residuals are correlated… Type 1 errors (for positive correlation)

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14
Q

Multicollinearity.. its effect…

A

Two or more independent variable are correlated… Too many Type 2 errors

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15
Q

6 Misspsecifications of regression models

A

1) Omitting a variable
2) Transforming a variable
3) Incorrectly pooling data
4) Using a lagged dependent variable as an independent variable
5) Forecasting the past
6) Measuring independent variables w/ error

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16
Q

3 necessary conditions for covariance stationarity

A

1) Constant and finite expected value
2) Constant and finite variance
3) Constant and finite covariance w/ leading or lagged variables

17
Q

Tests for covariance stationarity

A

1) Scatter plot
2) AR model and test correlations
3) Dickey-Fuller test (unit root)

18
Q

If the two series each have a _______, regression results will be consistent, provided that the two series are ______.

A

Unit Root; Cointegrated

19
Q

If neither of two time series in a regression analysis has a _____, you can safely use a _______ to test the relationship between them

A

Unit Root; Linear Regression

20
Q

If ARCH exists, use ______ or other methods that correct for ______ to correctly estimate the _____ of the parameters in the time series model.

A

generalized least squares; heteroskedasticity; standard error