•October 18, 2020
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•Dali Laxton
Multiple Regression Analyses: Statistical Inference
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•We are going to discuss how hypotheses about coefficients can be tested in regression models
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•We will explain what significance of coefficients mean
•We will learn how to read regression output
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–Wooldridge Chapter 4;
–Studenmund Chapter 5.1-5.4
Today’s Lecture

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•Statistical inference in the regression model
§Hypothesis tests about population parameters
§Construction of confidence intervals
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•Sampling distributions of the OLS estimators
§The OLS estimators are random variables
§We already know their expected values and their variances
§For hypothesis testing we need to know their distribution
Multiple Regression Analyses: Inference

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Inference: Sampling distributions of the OLS Estimators
•Assumption 6 (Normality of error terms)
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independently of
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It is assumed that the unobserved
factors are normally distributed around the population regression function.
The form and the variance of the distribution does not depend on
any of the explanatory variables.

Give example of impact of age on fighters’ performance

Show normality of the error terms in GRETL
•Open GRETL load sample data “Engel”
•Run regression Ols foodexp income
•Generate residuals:
•genr resid =foodexp-( $coeff(const) + $coeff(income)*income)
•Display distribution of residuals:
•freq resid --plot=display
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•Discussion of the normality assumption
•The error term is the sum of „many“ different unobserved factors
•Sums of independent factors are normally distributed (CLT)
•Problems:
§How many different factors? Observations large enough?
§Possibly very heterogenuous distributions of individual factors
§How independent are the different factors?
•The normality of the error term is an empirical question
•At least the error distribution should be „close“ to normal
•In many cases, normality is questionable or impossible by definition
Inference: Sampling distributions of the OLS Estimators

Show in gretl normality of residuals. The central limit theorem states that if you have a
population with mean μ and standard deviation σ and take sufficiently large random samples from the
population with replacement  (>30), then the distribution of the sample means will be approximately
normally distributedwith mean equal to the population mean, regardless of the fact whether the
original population distribution was normal or not.

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•Discussion of the normality assumption (cont.)
•Examples where normality cannot hold:
•Wages (nonnegative; also: minimum wage)
•Unemployment (indicator variable, takes on only 1 or 0)
•In some cases, normality can be achieved through transformations of the dependent variable
•Under normality, OLS is the best (even nonlinear) unbiased estimator
•Important: For the purposes of statistical inference, the assumption of normality can be replaced
by a large sample size (CLT)
Inference: Sampling distributions of the OLS Estimators

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•We cannot prove that a given hypothesis is “correct” using hypothesis testing
•All we can do is to state that a particular sample conforms to a particular hypothesis
•We can often reject a given hypothesis with a certain degree of confidence
•In such a case, we conclude that it is very unlikely the sample result would have been observed if
the hypothesized theory were correct
Multiple Regression Analyses: Hypothesis Testing

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•Step 1: state explicitly the hypothesis to be tested
•Null hypothesis: statement of the range of values of the regression coefficient that would be
expected to occur if the researcher‘s theory were not correct
•Alternative hypothesis: specification of the range of values of the coefficient that would be
expected to occur if the researcher‘s theory were correct
•In other words, we define the null hypothesis as the result we do not expect
Multiple Regression Analyses: Hypothesis Testing

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•It would be unrealistic to think that conclusions drawn from regression analysis will always be
right
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•There are two types of errors we can make:
–Type I: we reject a true null hypothesis
–Type II: We fail to reject a false null hypothesis
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Type I and Type II Errors

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Example:
•H0: The defendant is innocent
•HA: The defendant is guilty
–Type I error: sending an innocent person to jail
–Type II error: freeing a guilty person
•Lowering the probability of Type I error means increasing the probability of Type II error;
•In hypothesis testing, we focus on Type I error and we ensure that its probability is not
unreasonably large
Type I and Type II Errors

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Type I and Type II Errors
A picture containing text, person, newspaper Description generated with high confidence

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•Testing hypotheses about a single population parameter
•Theorem (t-distribution for standardized estimators)
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•Null hypothesis (for more general hypotheses, see below)
Under assumptions 1 – 6:
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If the standardization is done using the estimated standard deviation (= standard error), the
normal distribution is replaced by a t-distribution
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The population parameter is equal to zero, i.e. after controlling for the other independent
variables, there is no effect of xj on y
Note: The t-distribution is close to the standard normal distribution if n-k-1 is large.
Inference: The t Test

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•t-statistic (or t-ratio)
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•Distribution of the t-statistic if the null hypothesis is true
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•Goal: Define a rejection rule so that, if it is true, H0 is rejected only with a small probability
(= significance level, e.g. 5%)
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The t-statistic will be used to test the above null hypothesis. The farther the estimated
coefficient is away from zero, the less likely it is that the null hypothesis holds true. But what
does „far“ away from zero mean?
This depends on the variability of the estimated coefficient, i.e. its standard deviation. The
t-statistic measures how many estimated standard deviations the estimated coefficient is away from
zero.
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Inference: The t Test

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•Testing against one-sided alternatives (greater than zero)
Test                    ag      against                   .
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Reject the null hypothesis in favour of the alternative hypothesis if the estimated coef-
ficient is „too large“ (i.e. larger than a criti-
cal value).
Construct the critical value so that, if the
null hypothesis is true, it is rejected in,
for example, 5% of the cases.
In the given example, this is the point of the t-distribution with 28 degrees of freedom that is
exceeded in 5% of the cases.
! Reject if t-statistic greater than 1.701
Inference: The t Test

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•Example: Wage equation
•Test whether, after controlling for education and tenure, higher work experience leads to higher
hourly wages
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Standard errors
Test                              against                           .
One would either expect a positive effect of experience on hourly wage or no effect at all.
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Inference: The t Test

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•Example: Wage equation (cont.)
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„The effect of experience on hourly wage is statistically greater than zero at the 5% (and even at
the 1%) significance level.“
t-statistic
Degrees of freedom;
here the standard normal
approximation applies
Critical values for the 5% and the 1% significance level (these are conventional significance
levels).
The null hypothesis is rejected because the t-statistic exceeds the critical value.
Inference: The t Test

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•Testing against one-sided alternatives (less than zero)
Test                    a       against                   .
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Reject the null hypothesis in favour of the alternative hypothesis if the estimated coef-
ficient is „too small“ (i.e. smaller than a criti-
cal value).
Construct the critical value so that, if the
null hypothesis is true, it is rejected in,
for example, 5% of the cases.
In the given example, this is the point of the t-distribution with 18 degrees of freedom so that
5% of the cases are below the point.
! Reject if t-statistic less than -1.734
Inference: The t Test

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•Example: Student performance and school size
•Test whether smaller school size leads to better student performance
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Test                              against                            .
Do larger schools hamper student performance or is there no such effect?
Percentage of students
passing maths test
Average annual tea-
cher compensation
Staff per one thou-sand students
School enrollment
(= school size)
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Inference: The t Test

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•Example: Student performance and school size (cont.)
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One cannot reject the hypothesis that there is no effect of school size on student performance (not
even for a larger significance level of 15%).
t-statistic
Degrees of freedom;
here the standard normal
approximation applies
Critical values for the 5% and the 15% significance level.
The null hypothesis is not rejected because the t-statistic is not smaller than the critical value.
Inference: The t Test

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•Example: Student performance and school size (cont.)
•Alternative specification of functional form:
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Test                                      against                                    .
R-squared slightly higher
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Inference: The t Test

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•Example: Student performance and school size (cont.)
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The hypothesis that there is no effect of school size on student performance can be rejected in
favor of the hypothesis that the effect is negative.
t-statistic
Critical value for the 5% significance level ! reject null hypothesis
How large is the effect?
+ 10% enrollment  ! -0.129 percentage points students pass test
(small effect)
Inference: The t Test

Interpretation is different with linear-log -> if we increase x by 1%, y increases by Beta/100
units. log-linear -> if we increase x by 1, y increases by 100%*Beta

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•Testing against two-sided alternatives
Test                             against                   .
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Reject the null hypothesis in favour of the alternative hypothesis if the absolute value
of the estimated coefficient is too large.
Construct the critical value so that, if the
null hypothesis is true, it is rejected in,
for example, 5% of the cases.
In the given example, these are the points
of the t-distribution so that 5% of the cases
lie in the two tails.
! Reject if absolute value of t-statistic is less than -2.06 or greater than 2.06
Inference: The t Test

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•Example: Determinants of college GPA
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Lectures missed per week
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The effects of hsGPA and skipped are significantly different from zero at the 1% significance
level. The effect of ACT is not significantly different from zero, not even at the 10% significance
level.
 For critical values, use standard normal distribution
Inference: The t Test

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•„Statistically significant“ variables in a regression
•If a regression coefficient is different from zero in a two-sided test, the corresponding variable
is said to be „statistically significant“
•If the number of degrees of freedom is large enough so that the normal approximation applies, the
following rules of thumb apply:
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„statistically significant at 10 % level“
„statistically significant at 5 % level“
„statistically significant at 1 % level“
Inference: The t Test

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•Guidelines for discussing economic and statistical significance
•If a variable is statistically significant, discuss the magnitude of the coefficient to get an
idea of its economic or practical importance
•The fact that a coefficient is statistically significant does not necessarily mean it is
economically or practically significant!
•If a variable is statistically and economically important but has the „wrong“ sign, the regression
model might be misspecified
•If a variable is statistically insignificant at the usual levels (10%, 5%, 1%), one may think of
dropping it from the regression
•If the sample size is small, effects might be imprecisely estimated so that the case for dropping
insignificant variables is less strong
Inference: The t Test

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•Testing more general hypotheses about a regression coefficient
•Null hypothesis
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•t-statistic
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•The test works exactly as before, except that the hypothesized value is substracted from the
estimate when forming the statistic
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Hypothesized value of the coefficient
Inference: The t Test

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•Example: Campus crime and enrollment
•An interesting hypothesis is whether crime increases by one percent   if enrollment is increased
by one percent
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The hypothesis is rejected at the 5% level
Estimate is different from one but is this difference statistically significant?
Inference: The t Test

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•Computing p-values for t-tests
•If the significance level is made smaller and smaller, there will be a point where the null
hypothesis cannot be rejected anymore
•The reason is that, by lowering the significance level, one wants to avoid more and more to make
the error of rejecting a correct H0
•The smallest significance level at which the null hypothesis is still rejected, is called the
p-value of the hypothesis test
•A small p-value is evidence against the null hypothesis because one would reject the null
hypothesis even at small significance levels
•A large p-value is evidence in favor of the null hypothesis
•P-values are more informative than tests at fixed significance levels
Inference: The t Test

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•How the p-value is computed (here: two-sided test)
The p-value is the significance level at which one is indifferent between rejecting and not
rejecting the null hypothesis.
In the two-sided case, the p-value is thus the probability that the t-distributed variable takes on
a larger absolute value than the realized value of the test statistic, e.g.:
From this, it is clear that a null hypothesis is rejected if and only if the corresponding p-value
is smaller than the significance level.
For example, for a significance level of 5% the t-statistic would not lie in the rejection region.
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value of test statistic
These would be the critical values for a 5% significance level
Inference: The t Test

Inference: Confidence Intervals
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Critical value of
two-sided test
•Confidence intervals
•Simple manipulation of the result in Theorem 4.2 implies that
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•Interpretation of the confidence interval
•The bounds of the interval are random
•In repeated samples, the interval that is constructed in the above way will cover the population
regression coefficient in 95% of the cases
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Lower bound of the
Confidence interval
Upper bound of the
Confidence interval
Confidence level

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•Confidence intervals for typical confidence levels
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•Relationship between confidence intervals and hypotheses tests
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 reject                         in favor of
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Use rules of thumb
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Inference: Confidence Intervals

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•Example: Model of firms‘ R&D expenditures
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Spending on R&D
Annual sales
Profits as percentage of sales
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The effect of sales on R&D is relatively precisely estimated as the interval is narrow. Moreover,
the effect is significantly different from zero because zero is outside the interval.
This effect is imprecisely estimated as the in-
terval is very wide. It is not even statistically
significant because zero lies in the interval.
Inference: Confidence Intervals
(0.0128 )
0.0217
(0.0128 )

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•Example: Return to education at 2 year vs. at 4 year colleges
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Years of education at 2 year colleges
Years of education at 4 year colleges
Test                                against                              .
A possible test statistic would be:
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The difference between the estimates is normalized by the estimated standard deviation of the
difference. The null hypothesis would have to be rejected if the statistic is „too negative“ to
believe that the true difference between the parameters is equal to zero.
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Inference: Testing hypotheses about a linear combination of parameters

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Inference: Testing hypotheses about a linear combination of parameters
•Impossible to compute with standard regression output because
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•Alternative method
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Usually not available in regression output
Define                          and test                       against                     .
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a new regressor (= total years of college)
Insert into original regression
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•Estimation results
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•This method works always for single linear hypotheses
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Total years of college
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Hypothesis is rejected at 10% level but not at 5% level
Inference: Testing hypotheses about a linear combination of parameters

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•Testing multiple linear restrictions: The F-test
•Testing exclusion restrictions
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Years in
the league
Average number of
games per year
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Salary of major lea-
gue baseball player
Batting average
Home runs per year
Runs batted in per year
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against
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Test whether performance measures have no effect/can be exluded from regression.
Inference: The F Test

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•Estimation of the unrestricted model
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None of these variabels are statistically significant when tested individually
Idea: How would the model fit be if these variables were dropped from the regression?
Inference: The F Test

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•Estimation of the restricted model
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•Test statistic
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The sum of squared residuals necessarily increases, but is the increase statistically significant?
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The relative increase of the sum of squared residuals when going from
H1 to H0 follows a F-distribution (if
the null hypothesis H0 is correct)
 Number of restrictions
Inference: The F Test

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•Rejection rule
A F-distributed variable only takes on positive values. This corresponds to the fact that the sum
of squared residuals can only increase if one moves from H1 to H0.
Choose the critical value so that the null hypo-thesis is rejected in, for example, 5% of the
cases, although it is true.
Inference: The F Test

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•Test decision in example
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•Discussion
ØThe three variables are „jointly significant“
ØThey were not significant when tested individually
ØThe likely reason is multicollinearity between them
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Number of restrictions to be tested
Degrees of freedom in
the unrestricted model
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The null hypothesis is overwhel-mingly rejected (even at very small significance levels).
Inference: The F Test

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•Test of overall significance of a regression
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•The test of overall significance is reported in most regression packages; the null hypothesis is
usually overwhelmingly rejected
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The null hypothesis states that the explanatory variables are not useful at all in explaining the
dependent variable
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Restricted model
(regression on constant)
Inference: The F Test