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DID BIGOTRY AFFECT THE 2016 PRIMARY election?
Chris Dombrowski [Email address]
AbstractThis paper explores whether Donald Trump’s provocative remarks during the 2016 primary campaign contributed to his success. We use a series of OLS regressions to
estimate the share of votes he received in each state, controlling for different measures of hatred. We found that on average, Donald Trump performed better during the 2016 primary elections in states with higher hate crimes and more hate groups per 100000
capita.
1. INTRODUCTION
Donald Trump won the Republican nomination for president as a rather unconventional
candidate. At his rallies and during interviews, he consistently made inflammatory remarks about
various groups, including women, Hispanics, and Muslims. Despite making these bold,
provocative claims throughout his campaign, Donald Trump still won 1543 delegates out of a
possible 2472. He beat Republican Party mainstays by wide margins along his path to the general
election. We will explore if racial prejudice affected the results of 2016 Republican primary
elections.
Whether or not racial animosity played a role in election outcomes has been examined before
(Stephens-Davidowitz 2013). However, much of the literature discusses how a candidate has
been hurt by racial animosity. We take a different approach to this issue and attempt to determine
if Donald Trump harnessed voters’ racial animosity and used it to his benefit. There are two
main reasons this research is important. First, it would reveal the extent of contemporary
prejudice in the United States. It would also help us better understand the determinants of voting.
Does racial prejudice affect the results of an election? The most recent review of the literature is
inconclusive. "Despite considerable effort by numerous researchers over several decades, there is
still no widely accepted answer as to whether or not prejudice against blacks remains a potent
factor within American politics" (Huddy and Feldman, 2009).
2. LITERATURE REVIEW
A study conducted by Huddy and Feldman (2009) focused on determining whether or not
white prejudice hurt black candidates. To determine whether prejudice influenced white voters in
the 2008 election, Huddy and Fledman (2009) used data from the May 2009 release of the
American National Election Studies (ANES) 2008 series survey. These surveys were conducted
face-to-face. For questions about stereotypes and other sensitive subjects, the respondents
entered their responses into a computer, out of view from the interviewer to reduce social
desirability bias. The authors conducted a series of multivariate logistic regression analyses of
presidential elections from 2008 dating back to 1992. The dependent variable was vote choice of
the interviewees and the independent variables was an index which included the difference
measure for “lazy” and “unintelligent” questions that were given to the interviewees. This study
found that, among white Americans, prejudice continues to present a bigger split for Democrats
than Republicans. Among Independents, the effects of racial prejudice may be the greatest. It
was a challenge for the authors to conclude that there was a racial bias affecting voting in the
Republican party because the Republicans who were interviewed that exhibited no racial bias
were still not going to vote for Obama due to differences of opinion.
Stephens-Davidowitz (2013) sought to find if racial animus can cost a black candidate
votes in contemporary America. This paper used non-survey based methods and instead
measured an area’s racial animus based on the percent of Google search queries that included
racially charged language. This method had not previously been used to study prejudice. The
author used the data from Barack Obama’s presidential votes, controlling for the previous
Democratic candidate, John Kerry’s presidential votes. In conclusion, this paper suggests that
prejudice cost Obama 4.2 percentage points of the national popular vote in 2008 and 4.0
percentage points in 2012. These numbers imply that, among white voters who would have
supported a Democratic presidential candidate in 2012, 9.1 percent did not support a black
Democratic presidential candidate (Stephens-Davidowitz 2014). This paper was effective in
demonstrating that racial bias does impact votes for a black presidential candidate.
In a similar study, Mas and Moretti (2009) demonstrated that racial prejudice was
strongly related to the state-level nonblack vote in the 2008 presidential election. The hypothesis
assumed that Barack Obama lost nonblack Democratic votes in the 2008 election. To analyze the
uncertainty of the outcome, Mas and Moretti (2009) used regression analysis with independent
variables of nonblack partisanship and ideology, which were measured using exit poll questions
and past presidential election votes for Democratic candidates. Mas and Moretti (2009) relied on
the Voter News Service (2000) and National Election Pool, which are state exit polls. The
authors conclude that there was a strong relationship between the state-level nonblack vote for
Obama in 2008 and racial attitudes. Obama received fewer votes in states with higher levels of
prejudice.
Redlawsk (2012) examined how emotions play an important role in American politics
and claims that racial resentment towards African American candidates has an effect on the
outcomes of elections. To test their argument, they used a national pre- and post-election
telephone survey carried out at the Eagleton Center for Public Interest Polling at Rutgers
University. This survey had a total of 863 respondents. In the survey, they asked the respondents
to evaluate the 2012 candidates before and after the election on a four-point scale, where 1 is
very well and 4 is not well at all. The two primary independent variables were symbolic racism,
and the second primary explanatory variable is the measure responses about the candidates. The
questions in the survey helped them measure these variables. Questions such as agreeing or
disagreeing with statements like “many other minority groups have overcome prejudice and
worked their way up, African Americans should do the same without special favors.”
Respondents also received the following question, “I’d like you to rate each question using
something called the feeling thermometer”, for which they could choose any number between 0-
100. The higher the number, the more favorable you feel towards the candidate. Using the OLS
model, they were able to predict the 2012 pre- and post-election feeling thermometer ratings of
President Obama. The author’s findings showed that racism did in fact have a strong negative
effect on Obama’s evaluation. Results also revealed that high levels of racial resentment do lead
to lower evaluations of Obama, all else equal, as they expected from the voluminous literature on
attitudes toward minority candidates.
Lee and Roemer (2006) argue that racism among American voters has reduced the
income tax rate by 11-18%. They attribute this phenomenon to two mechanisms. The first is the
“anti-solidarity effect” in which some voters oppose government transfer payments to minorities.
The second mechanism is “that some voters who desire redistribution nevertheless vote for the
anti-redistributive (Republican) party because its position on the race issue is more consonant
with their own” (2006). This effect was dubbed the “policy bundle effect.” The authors retrieved
survey data from the National Election Studies and analyzed 1,905 responses from 1976 to 1992.
The dependent variable was Republican vote share, and the independent variables were the
participants’ responses to the survey. From the data, they were able to identify participants’
attitudes towards a variety of racial and political issues. Lee and Roemer (2006) found that when
comparing racism, libertarianism, compassion for the poor, and feminism, racism was the best
predictor of whether or not a participant voted for a Republican president.
Geys (2006) explored the effect that socio-economic, political, and institutional variables
have on voter turnout. The paper is a meta-analysis of the empirical work that has been done to
study the factors that motivate people to vote. The data used were not raw data, but the results
from 83 aggregate-level analyses. Geys (2006) points out how different techniques used for the
same measure may change results, and which explanatory variables were consistently significant
across the studies. The study suggests a standard model be used when determining voter-turnout.
3. DATA
This study analyzes cross-sectional state-level data to determine whether measures of
racism can be correlated with the percentage of votes Donald Trump received in the 2016
Republican primary elections and caucuses. After compiling political, economic, education, and
demographic variables, as well as, measures of racism, we were left with 46 states to analyze.
Neither Colorado nor North Dakota held Republican primaries or caucuses. Washington D.C.,
Alaska, and Hawaii were omitted because they were major outliers across numerous independent
variables. (See Table 1. and Table 2. for independent variable and dependent variable descriptive
statistics.)
3.1. Variables and Hypothesis
For this study, the dependent variable is the percentage of votes Donald Trump received
in each state’s primary election (TRUMPVOTES). Stephens-Davidowitz (2014) used the
difference between the percentage of votes John Kerry received in 2004 and the percentage of
votes Barack Obama received in 2008. This technique currently does not work for our purposes.
However, future studies, after the 2016 General Election, will be able to compare Mitt Romney’s
share of votes in 2012 to Donald Trump’s share in 2016. We also used (CLINTONVOTES) as a
dependent variable in our other model. Like (TRUMPVOTES), (CLINTONVOTES) is the
percentage of votes Hillary Clinton received in each state’s primary election.
We chose not to use survey data for our study. We agree with Stephens-Davidowitz’s
(2014) concerns that surveys may not always provide accurate data due to participants’
adherence to social norms. To measure racism by state, we use three different independent
variables, including Stephens-Davidowitz’s (2014) state-level Racially Charged Search Rate data
(STEPHENS_RCSR). Our second measure is the hate crime rate in each state (HCPER100000).
While only the most extreme racists commit hate crimes, we believe that the hate crime rate in
each state is a good barometer for the level of intolerance in a state. For our final measure of
racism, we used the Southern Poverty Law Center’s Hate Map to determine the number of active
hate groups in each state. We then divided the number of active hate groups by the state’s
population, and multiplied by 100,000. This gave us the number of hate groups per 100,000
people (HGPER100000).
Our education variables include each state’s high school dropout rate
(DROPOUT_RATE) and the percentage of the population with an advanced degree. For our
purposes, we defined an advanced degree as a master’s degree and above (PERCENT_W_AD).
Our final education variable is the state’s spending per pupil in 2014 (SPP_2014).
Lee and Roemer (2006) focused on which party voters with racist motivations vote for.
Our intent, however, is to determine whether a single candidate harnessed voters’ attitudes
toward race. In some states, as many as six Republicans received at least 5% of the vote during
the primaries. When more candidates are drawing votes, it skews the percentage of votes
received by each candidate. To control for this, we counted the number of Republicans in each
state that received at least 5% of the vote (NUMBER_REPUBS_W5). For other political
variables, we used a dummy variable for states with closed primaries (CLOSED_DUMMY) and
a dummy variable to distinguish between a primary and a caucus (PRIMARY_DUMMY).
The economic variables we use include the change in average unemployment rate from
2014 to 2015. We calculated this by finding the average unemployment rate in 2014 and
subtracted it from the average unemployment rate in 2015 (CHANGE_IN_UR). We also used
the 2013 Gini Coefficient from each state to measure income inequality (GINI_2013).
For demographic variables, we calculated a diversity index based on the United States
Census estimates for the 2015 population. Our index works like a Herfindahl-Hirschman Index.
We squared the percentage of the population of each race and summed the results
(DIVERSITY_INDEX). Another demographic variable is the percentage of the population in
each state that lives in rural areas (POPPCT_RURAL). (For a list of variables and expected
signs, see Table 3.)
4. MODELS
We tested our regressions for heteroskedasticity with the White test. The results from the
White test showed that none of our models suffered from heteroskedasticity.
4.1. Inclusive Models
Our inclusive models use all data gathered for each state for independent variables, and
use (TRUMPVOTES) and (CLINTONVOTES) as the dependent variables. (See Regressions 1
and 2.)
TRUMPVOTES= β0+β1 [POLITICAL ]+β2[DEMOGRAPHIC ]+β3 [HATEMETRICS ]+β4[EDUCATIONAL ]+ β5 [ECONOMIC ]+∈
CLINTONVOTES=β0+β1 [POLITICAL ]+β2 [DEMOGRAPHIC ]+β3 [HATEMETRICS ]+β4 [EDUCATIONAL ]+β5 [ECONOMIC ]+∈
4.2. Semi-log Models
The estimated models in this section use the semi-log form. This is based on the
assumption that the effect of each independent variable on the dependent variable
(TRUMPVOTES) increases at a decreasing rate. (See regressions 3 and 4.)
TRUMPVOTES=β0+β1 [ log (NUMBERREPUBSW 5 ) ]+ β2 [ log (HCPER100000 ) ]+β3 [ log (HGPER100000 ) ]+β4 [DIVERSITYINDEX ]+β5 [ log (PERCENTWAD ) ]+β6 [ log (GINI 2013 ) ]+∈
Our Clinton model used the same variables as our Trump model, excluding
(NUMBERREPUBSW5).
CLINTONVOTES=β0+β2 [ log (HCPER 100000 ) ]+β3 [ log (HGPER 100000 ) ]+β4 [DIVERSITYINDEX ]+ β5 [ log (PERCENTWAD ) ]+β6 [ log (GINI 2013 ) ]+∈
4.3. Simple Regressions
Since [log(HCPER100000)] proved to be significant in each of our semi-log models, we
ran simple regressions with each candidate’s share of votes from each state’s primary election as
the dependent variable, and the hate crime rate for each state as the sole independent variable.
(See regressions 5 and 6.) TRUMPVOTES=β0+β2 [ (HCPER 100000 ) ]+∈
CLINTONVOTES=β0+β2 [ (HCPER 100000 ) ]+∈
5. RESULTS
5.1. Inclusive Models
For the inclusive Trump model, the adjusted R2 was .28. Only three of the independent
variables proved to be significant. The number of Republicans with at least 5% of the vote, as
expected, was negative and significant at the 1% level. The hate crime rate and the number of
hate groups per 100000 population were both significant at the 10% level and positive. No other
independent variables proved to be significant.
The inclusive Clinton model had an adjusted R2 of .59. The only significant independent
variable was the diversity index. The diversity index was positive and significant at the 1% level.
5.2. Semi-log Models
We used the semi-log models above to analyze 46 states, excluding Alaska, Colorado,
Hawaii, North Dakota, and Washington D.C. The adjusted R2 for the Trump model is .35. As
expected, the number of Republicans that received at least 5% of the votes was the biggest
driving factor of the percentage of votes Donald Trump received.
Interestingly, the [log(DIVERSITY_INDEX)] was negative and significant at the 10%
level. This means that the more diverse a state is, the more votes Donald Trump received from
that state on average.
Both metrics used to quantify hate in a state have positive and significant coefficients.
[log(HCPER100000)] is significant at the 5% level, and [log(HGPER100000)] is significant at
the 10% level. A simple regression, using TRUMPVOTES as the dependent variable and
HCPER100000 as the independent variable, also produces a positive coefficient significant at the
10% level. In our semi-log model, [log(PERCENT_W_AD)] and [log(GINI_2013)] were not
statistically significant.
For our Clinton model, our adjusted R2 is .66. The [log(DIVERSITY_INDEX)] is
significant and positive at the 1% level, meaning the more diverse a state is, the higher
percentage of votes Clinton received, on average. Only one measure of hate in the Clinton model
proved to be significant at the 5% level. [log(HCPER100000)] was significant at the 5% level
and negative. The higher the hate crime rate in each state, the less votes Clinton received, on
average.
5.3. Simple Regressions
The results of our simple regressions jibed with the results from our semi-log and
inclusive models. On average, Donald Trump received a greater share of primary election votes
in states with a higher hate crime rate. These results are significant at a 10% level.
For the simple regression comparing hate crimes and the share of votes Hillary Clinton
received, the sign for (HCPER100000) remained negative and significant at a 5% level.
6. CONCLUSION
Our findings are similar to Lee and Roemer’s (2006) in that we believe some voters are
indeed motivated by racist ideologies. Provocative remarks by Donald Trump regarding groups
of people different than himself (and, admittedly, similar to himself) may have motivated voters
with negative racial attitudes to vote for him. Stephens-Davidowitz (2014) found that a black
candidate, Barack Obama, was hurt by racial animosity among voters. It follows that a candidate
could be helped by that same racial animosity by tailoring his rhetoric toward it.
Whether Donald Trump intentionally manipulated voters should be left to other studies.
However, it is likely that his rhetoric—building a wall, banning Muslims, that the black
population in the United States is living in Hell—motivated some voters to vote for him during
the 2016 primary election.
Further research could compare the 2016 General Election Results with the 2012 General
Election Results, following Stephens-Davidowitz (2014). Mitt Romney was a more traditional
Republican candidate for President, and could serve as a baseline, much like John Kerry did in
Stephens-Davidowitz (2014). When comparing our Trump models with our Clinton models, it
seems as though Donald Trump benefited from voters’ bigoted attitudes, while Hillary Clinton
did not, and was possibly even hurt by them.
Stephens-Davidowitz (2014), Lee and Roemer (2006), and Mas and Moretti (2009) have
all shown that racial animosity can affect elections. Stephens-Davidowitz (2014) and Mas and
Moretti (2009) focused on how candidates can be hurt by racial animosity. It should follow, then,
that racial animosity can also help a candidate.
REFERENCES
Huddy, Leonie and Stanley Feldman, “On Assessing the Political Effects of Racial Prejudice,”
Annual Review of Political Science, June 2009, 12 (1), 423–447.
Geys, Benny. "Explaining voter turnout: A review of aggregate-level research." Electoral Studies
25.4 (2006): 637-663.
Lee, Woojin, and John E. Roemer. "Racism and redistribution in the United States: A solution to
the problem of American exceptionalism." Journal of Public Economics 90.6 (2006):
1027-1052.
Redlawsk, David P., Tolbert Caroline J., and McNeely Natasha Altema. "Symbolic Racism and
Emotional Responses to the 2012 Presidential Candidates." Political Research Quarterly
67.3 (2014): 680-94. Web.
Stephens-Davidowitz, Seth Isaac. 2013. Essays Using Google Data. Doctoral dissertation,
Harvard University.
Mas, Alexandre, and Enrico Moretti. 2009. “Racial Bias in the 2008 Presidential Election.”
American Economic Review 99 (2): 323–29.
Table 1.
Variable Mean Median Standard Deviation Minimum MaximumDROPOUT_RATE 0.173 0.151 0.056 0.095 0.315GINI_COEFFICIENT_2013 0.606 0.596 0.037 0.541 0.699HCPER100000 2.190 1.950 1.531 0.130 6.000HGPER100000 0.316 0.287 0.164 0.056 0.739MEANIMM_RATE 2.364 1.370 3.446 -3.820 12.540NUMBERREPUBS 3.717 4.000 0.807 3.000 6.000ONEMINUSDI 0.315 0.310 0.124 0.098 0.547PERCENT_W_AD 0.098 0.092 0.025 0.061 0.164POPPCT_RURAL 26.628 26.265 14.646 5.050 61.340SPP_2014 11232.957 10435.000 3144.091 6500.000 20610.000STEPHENS_RCSR 63.391 64.500 13.924 30.000 100.000
Table 2.
Candidate Mean Median Standard Deviation Minimum MaximumTrump Votes 0.4609 0.4440 0.1753 0.0720 0.8060
Clinton Votes 0.5142 0.5129 0.1489 0.1362 0.8263
Table 3.
Variable Expected SignDropout Rate PositivePop Pct in Rural Communities PositiveStephens RCSR PositiveHC Per 100000 PositiveHG Per 100000 PositiveDiversity Index NegativeNumber of Republicans NegativePct With Advanced Degree NegativeSpending Per Pupil NegativeGini Coefficient ?Mean Immigration Rate ?
Regression 1.
Dependent Variable: TRUMPVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:14Sample: 1 46Included observations: 46
Variable Coefficient Std. Error t-Statistic Prob.
C 1.093971 0.556165 1.966990 0.0574ONEMINUSDI 0.097001 0.303290 0.319831 0.7511
DROPOUT_RATE 0.798192 0.477145 1.672848 0.1035GINI_COEFFICIENT__2013... -1.344724 0.865225 -1.554190 0.1294
MEANIMM_RATE 0.003571 0.008200 0.435512 0.6659NUMBERREPUBS -0.111947 0.031447 -3.559861 0.0011PERCENT_W_AD 1.552671 1.874362 0.828374 0.4132POPPCT_RURAL -0.001850 0.002791 -0.662842 0.5119
SPP_2014 5.10E-06 1.39E-05 0.367137 0.7158STEPHENS_RCSR 0.001354 0.002308 0.586734 0.5613
HCPER100000 0.030141 0.016933 1.779975 0.0840HGPER100000 0.343565 0.198442 1.731306 0.0925
R-squared 0.454657 Mean dependent var 0.460935Adjusted R-squared 0.278223 S.D. dependent var 0.175324S.E. of regression 0.148951 Akaike info criterion -0.750945Sum squared resid 0.754334 Schwarz criterion -0.273908Log likelihood 29.27174 Hannan-Quinn criter. -0.572244F-statistic 2.576918 Durbin-Watson stat 1.316855Prob(F-statistic) 0.016945
Regression 2.Dependent Variable: CLINTONVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:18Sample: 1 46Included observations: 46
Variable Coefficient Std. Error t-Statistic Prob.
C 0.282970 0.356112 0.794610 0.4322ONEMINUSDI 0.930379 0.194804 4.775980 0.0000
DROPOUT_RATE -0.190737 0.306915 -0.621466 0.5383GINI_COEFFICIENT__2013... -0.012104 0.556498 -0.021751 0.9828
MEANIMM_RATE 0.002666 0.005258 0.506994 0.6153PERCENT_W_AD -1.626025 1.166216 -1.394274 0.1720POPPCT_RURAL 4.02E-05 0.001715 0.023451 0.9814
SPP_2014 7.61E-06 8.68E-06 0.876982 0.3865STEPHENS_RCSR 0.000844 0.001483 0.569470 0.5727
HCPER100000 -0.009977 0.010882 -0.916832 0.3655HGPER100000 0.041663 0.127355 0.327145 0.7455
R-squared 0.677643 Mean dependent var 0.514202Adjusted R-squared 0.585541 S.D. dependent var 0.148863S.E. of regression 0.095836 Akaike info criterion -1.647388Sum squared resid 0.321459 Schwarz criterion -1.210104Log likelihood 48.88991 Hannan-Quinn criter. -1.483578F-statistic 7.357522 Durbin-Watson stat 2.165626Prob(F-statistic) 0.000004
Regression 3.
Regression 4.
Dependent Variable: TRUMPVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:12Sample: 1 46Included observations: 46
Variable Coefficient Std. Error t-Statistic Prob.
C 1.476150 0.346776 4.256782 0.0001LOGREPUBS -0.491933 0.109880 -4.476991 0.0001
LOGHC 0.053828 0.024775 2.172670 0.0359LOGHGPER100000 0.089563 0.044711 2.003134 0.0521LOG(ONEMINUSDI) 0.089838 0.050532 1.777819 0.0832
LOGAD 0.147760 0.104381 1.415578 0.1648LOGGINI -0.340982 0.395402 -0.862366 0.3938
R-squared 0.435816 Mean dependent var 0.460935Adjusted R-squared 0.349019 S.D. dependent var 0.175324S.E. of regression 0.141457 Akaike info criterion -0.934372Sum squared resid 0.780395 Schwarz criterion -0.656100Log likelihood 28.49055 Hannan-Quinn criter. -0.830130F-statistic 5.021074 Durbin-Watson stat 1.286964Prob(F-statistic) 0.000675
Dependent Variable: CLINTONVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:11Sample: 1 46Included observations: 46
Variable Coefficient Std. Error t-Statistic Prob.
C 0.722439 0.192912 3.744919 0.0006LOGHC -0.036507 0.015195 -2.402620 0.0210
LOGHGPER100000 0.033212 0.026545 1.251140 0.2182LOG(ONEMINUSDI) 0.235681 0.030974 7.608948 0.0000
LOGAD -0.048793 0.063866 -0.763978 0.4494LOGGINI -0.061235 0.239815 -0.255342 0.7998
R-squared 0.698065 Mean dependent var 0.514202Adjusted R-squared 0.660323 S.D. dependent var 0.148863S.E. of regression 0.086760 Akaike info criterion -1.930226Sum squared resid 0.301094 Schwarz criterion -1.691707Log likelihood 50.39520 Hannan-Quinn criter. -1.840875F-statistic 18.49573 Durbin-Watson stat 2.294437Prob(F-statistic) 0.000000
Regression 5.
Regression 6.
Dependent Variable: TRUMPVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:04Sample: 1 46Included observations: 46
Variable Coefficient Std. Error t-Statistic Prob.
C 0.393219 0.044261 8.884129 0.0000HCPER100000 0.030924 0.016626 1.860013 0.0696
R-squared 0.072897 Mean dependent var 0.460935Adjusted R-squared 0.051826 S.D. dependent var 0.175324S.E. of regression 0.170720 Akaike info criterion -0.655078Sum squared resid 1.282396 Schwarz criterion -0.575571Log likelihood 17.06678 Hannan-Quinn criter. -0.625294F-statistic 3.459649 Durbin-Watson stat 1.420841Prob(F-statistic) 0.069577
Dependent Variable: CLINTONVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:06Sample: 1 46Included observations: 46
Variable Coefficient Std. Error t-Statistic Prob.
C 0.576507 0.037322 15.44666 0.0000HCPER100000 -0.028453 0.014019 -2.029534 0.0485
R-squared 0.085600 Mean dependent var 0.514202Adjusted R-squared 0.064819 S.D. dependent var 0.148863S.E. of regression 0.143958 Akaike info criterion -0.996084Sum squared resid 0.911853 Schwarz criterion -0.916578Log likelihood 24.90994 Hannan-Quinn criter. -0.966301F-statistic 4.119006 Durbin-Watson stat 1.795799Prob(F-statistic) 0.048479
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