The type I error associated with testing a hypothesis is equal to:

We are 95% sure that the interval contains the true value of the parameter

We are 95% sure that our estimate of the coefficient is correct

We are 95% sure that the interval contains our estimate of the coefficient

In repeated samples, we would derive the same estimate for the coefficient 95% of the time

It captures omitted determinants of the dependent variable

To allow for the non-zero mean of the dependent variable

To allow for errors in the measurement of the dependent variable

To allow for random influences on the dependent variable

The probability of a Type I error is increased

The probability of a Type II error is increased

The rejection criterion has become more strict

The null hypothesis will be rejected less often

The residuals are positively autocorrelated

The residuals are negatively autocorrelated

There is no autocorrelation in the residuals

The test statistic has fallen in the intermediate region

The assumption that the regressors are non-stochastic is violated

A model with many lags may lead to residual non-normality

Adding lags may induce multicollinearity with current values of variables

The standard errors of the coefficients will fall as a result of adding more explanatory variables

Close to zero

Close to two

Close to four

Close to one

A white noise process

A covariance stationary process

An autocorrelated process

A moving average process

All roots of the characteristic equation must lie outside the unit circle

All roots of the characteristic equation must lie inside the unit circle

All roots must be smaller than unity

At least one of the roots must be bigger than one in absolute value

The current value of y

Zero

The historical unweighted average of y

An exponentially weighted average of previous values of y

-0.1

0.27

-0.34

-0.31