3. Hypothesis testing

Hypothesis testing in regression analysis is basically a formal way of testing the quality of our results. It can be used to perform standard tests, or to test your own theories in a formal way.

Before doing hypothesis testing, we should consider if our model can stand up to the classical linear model assumptions. If not, the results from the hypothesis test will likely be invalid.

Procedure

Hypthesis testing is generally performed through the following steps:

1. Define the null hypothesis

The null hypothesis (H0) is a statement that there is no relationship between two things. It's sometimes called the "no difference" hypothesis, because if true, it means that the claimed relationship doesn't exist.

It's often written like this:
H0: β = 0
Which, in this example, would mean that our null hypothesis is that some coefficient (β) is equal to zero.

An important part of hypothesis testing is designing a good null hypothesis, as it will form the basis for the whole test.

2. Define the alternative hypothesis

The alternative hypothesis (H1) is generally the opposite of the null hypothesis, i.e. when the null hypothesis is not true. Thus, it's often just defined as the opposite of the null hypothesis.

For example:
H0: β = 0
H1: β # 0

As H1 is just the opposite of H0, we could even write:
H1: H0 is not true

This is called a two-tailed test because all values that are not the null hypothesis are covered.
A one-tailed test would have only half of the values covered, e.g:
H1: β > 0
or
H1: β < 0

3. Decide which test is appropriate

Th

Concepts

t-distribution: Similar to a normal distribution.

Null hypothesis (H0): There is no relationship between two variables. This conclusion is often reached by testing the variables, possibly rejecting it.

Alternative hypothesis (H1): The opposite of the null hypothesis.

Significance level: The probability of rejecting H0 when it is in fact true. In other words, the probability of the result being incorrect. Most commonly 5%. “With a 5% significance level, the definition of ”sufficiently large” is simply the 95th percentile in a t distribution with n-k-1 df. “ H0 is rejected if t > c. We get c by looking at tables for significance level and degrees of freedom (df).

Degrees of freedom (df): n-k-1, i.e. number of observations minus number of explanatory variables minus one.

t-value: Lower means less significance of variable(?)

p-value: The smallest significance level at which we would be able to reject the null hypothesis. Given the observed value of the t statistic, what is the smallest significance level at which the null hypothesis would be rejected? The p-value is the probability of observing a t value as extreme as we did if the null hypothesis is true. Thus, small p-values are evidence against the null hypothesis. If the p-value is, say, 0.04, we might say there’s significance at the 5% level (actually at the 4% level) but not at the 1% level (or 3% or 2% level).

Confidence interval (CI): Provides a range of likely values for the unknown βj.

F test: Testing multiple restrictions. Similar to t-test, but with several hypothesis at the same time. Commonly test that all coefficients = 0. Example:

H0: B1 = 0, B2 = 0, B3 = 0, …, B2 = 0.
H1: H0 is not true

If all are equal to 0, none would explain the model, i.e. same as saying no of the included variable explains the model. We would only have the constant. We want to test that our original model explains more than the model under the null hypothesis, i.e. without influence from any of the variables (the restricted model). If we get a low p-value/significance level, this means they’re not likely to be 0, i.e. null hypothesis can be rejected at low significant levels.

Unrestricted model: Original model with all variables
Restricted model: Some variables removed to impose restrictions on the model.

R^2: Higher when more variables are included, so lower for restricted models. R^2 = 1 - SSR/SST. In other words, SSR decreases with more variables, and increases with the restricted model.

If SSR increases a lot when you exclude variables, those variables have significant explanatory power, and should not be omitted.

Chow test: Doing an F test for a restricted model setting all coefficients to equal.

F-test in Stata:
First to regression, then directly after type:
“test excluded_var_1 excluded_var_2”

Automatically defines null hypothesis for these variables and does the F-test. Returns F-value “F(x, y)” and p-value “Prob > F”. Top-right of a regression, the F-value for all variables = 0 are shown, as well as the the p-value.

Compare with c (found in table), if F-value > c we can reject H0.

F value = F(q, df, p)
q = Number of restricted variables
df = Denominator degrees of freedom = n - k - 1
n = Number of observations
k = Number of variables in regression
p = Significance level between 0-1

Stat command to get c-value
invttail(n, p)
invftail(q,n,p)

When reporting the regression, we should include at least standard errors and t-statistics.


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