scholarly journals PREDICTION FOR THE 2012 UNITED STATES PRESIDENTIAL ELECTION USING MULTIPLE REGRESSION MODEL

2012 ◽  
Vol 6 (2) ◽  
pp. 77-97
Author(s):  
Pankaj Sinha ◽  
Aastha Sharma Aastha Sharma ◽  
Harsh Vardhan Singh
Keyword(s):  
Regression Model ◽  
Unemployment Rate ◽  
Presidential Election ◽  
Voting Behavior ◽  
Presidential Elections ◽  
Multiple Regression Model ◽  
Economic Factors ◽  
Gdp Growth Rate ◽  
Proposed Model ◽  
Midterm Elections

This paper investigates the factors responsible for predicting 2012 U.S. Presidential election. Though contemporary discussions on Presidential election mention that unemployment rate will be a deciding factor in this election, it is found that unemployment rate is not significant for predicting the forthcoming Presidential election. Except GDP growth rate, various other economic factors like interest rate, inflation, public debt, change in oil and gold prices, budget deficit/surplus and exchange rate are also not significant for predicting the U.S. Presidential election outcome. Lewis-Beck and Rice (1982) proposed Gallup rating, obtained in June of the election year, as a significant indicator for forecasting the Presidential election. However, the present study finds that even though there exists a relationship between June Gallup rating and incumbent vote share in the Presidential election, the Gallup rating cannot be used as the sole indicator of the Presidential elections. Various other non-economic factors like scandals linked to the incumbent President and the performance of the two parties in the midterm elections are found to be significant. We study the influence of the above economic and non-economic variables on voting behavior in U.S. Presidential elections and develop a suitable regression model for predicting the 2012 U.S. Presidential election. The emergence of new non-economic factors reflects the changing dynamics of U.S. Presidential election outcomes. The proposed model forecasts that the Democrat candidate Mr. Barack Obama is likely to get a vote percentage between 51.818 % - 54.239 %, with 95% confidence interval.

scholarly journals HIERARCHICAL BAYES PREDICTION FOR THE 2008 US PRESIDENTIAL ELECTION

2012 ◽  
Vol 2 (3) ◽  
pp. 47-59
Author(s):  
Pankaj Sinha ◽  
Ashok K. Bansal
Keyword(s):  
Regression Model ◽  
Density Function ◽  
Multiple Regression ◽  
Presidential Election ◽  
Voting Behavior ◽  
Predictive Performance ◽  
Multiple Regression Model ◽  
Hierarchical Bayes ◽  
Predictive Density ◽  
Us Presidential Election

In this paper a procedure is developed to derive the predictive density function of a future observation for prediction in a multiple regression model under hierarchical priors for the vector parameter. The derived predictive density function is applied for prediction in a multiple regression model given in Fair (2002) to study the effect of fluctuations in economic variables on voting behavior in U.S. presidential election. Numerical illustrations suggest that the predictive performance of Fair’s model is good under hierarchical Bayes setup, except for the 1992 election. Fair’s model under hierarchical Bayes setup indicates that the forthcoming 2008 US presidential election is likely to be a very close election slightly tilted towards Republicans. It is likely that republicans will get 50.90% vote with probability for win 0.550 in the 2008 US presidential election

scholarly journals Analysis of decreasing natality trend in the Czech Republic

2008 ◽  
Vol 56 (3) ◽  
pp. 147-156
Author(s):  
Jitka Poměnková ◽  
Lenka Němcová
Keyword(s):  
Czech Republic ◽  
Regression Model ◽  
Multiple Regression Model ◽  
Living Standard ◽  
Second Step ◽  
Economic Factors ◽  
The Czech Republic ◽  
Financial Situation ◽  
Evaluation Influence ◽  
Family State

The aim of this paper is factors identification of the decreasing natality trend in the Czech Republic between years 1991–2005. This identification is done with respect to the financial situation and living standard of families.The first step, analysis of natality factor – animation natality, is performed. Animation natality is divi­ded according to the mother family state in the time of the birth. Trend of born in marriage and trend out of marriage are described. Following analysis is focused on decreasing component of natality – number of born in marriage.The second step is time series correlation analysis used for identification and evaluation influence of demographic and economic factors on decreasing component of natality. Based on this analysis, in­fluen­cing factors for regression model describing natality are selected.The last step is formulation and estimation of multiple regression model describing causality between natality in marriage and selected factors.

scholarly journals INNOVATION EFFECT THROUGH RESEARCH AND DEVELOPMENT ON ECONOMIC GROWTH IN 35 OECD COUNTRIES

2021 ◽  
Vol 11 (4) ◽  
pp. 159-166
Author(s):  
Maman Ali M. Moustapha ◽  
Qian Yu
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Research And Development ◽  
Regression Model ◽  
Oecd Countries ◽  
Multiple Regression Model ◽  
Gdp Growth ◽  
Real Gdp ◽  
Gdp Growth Rate ◽  
Development Expenditure

This paper analyzes the effect of research and development (R&D) expenditures on economic growth in the Organization of Economic Cooperation and Development (OECD) countries over the period 2000-2016. This study conducts an empirical analysis using a multiple regression model. The main findings confirm that an increase in research and development expenditure by 1% would generate an increase of real GDP growth rate to 2.83 %. The implication emerging from this study is that government and institutions need to increase investment in R&D expenditures to fulfill inclusive economic growth perspective.

Aggregate Economic Variables and Votes for Congress: A Rejoinder

1975 ◽  
Vol 69 (4) ◽  
pp. 1266-1269 ◽  
Author(s):  
Francisco Arcelus ◽  
Allan H. Meltzer
Keyword(s):  
Unemployment Rate ◽  
Voting Behavior ◽  
Presidential Elections ◽  
Inflation Rate ◽  
Real Income ◽  
Implicit Theory ◽  
Economic Variables ◽  
Econometric Evidence ◽  
Election Results

Our interest in the effect of aggregate economic variables on election results began in 1970 following a conversation with an administration official that we have reported elsewhere. We doubted both the implicit theory of voting behavior and the ability of the administration to achieve rates of inflation and unemployment even close to the ranges mentioned. We take this opportunity to note that the unemployment rate was higher and the inflation rate substantially higher than the adviser's estimate, but President Nixon was reelected.At the time, the principal econometric evidence of the effects of aggregate economic variables was a study by Kramer. Kramer found evidence of an effect of real income, but despite (or perhaps because of) the flaws in his procedure, he found no evidence of an effect of inflation or unemployment. Furthermore, then and now, most of the reported evidence pertains to congressional not presidential elections and to votes for congressmen, not seats in the Congress.

scholarly journals Charismatic and Affective Rhetoric in a Presidential Campaign

2014 ◽  
pp. 120-137
Author(s):  
Sheilesha R. Willis ◽  
Gloria L. Sweida ◽  
Stephanie Glassburn ◽  
Cynthia L. Sherman ◽  
Michelle C. Bligh
Keyword(s):  
Content Analysis ◽  
Presidential Election ◽  
Voting Behavior ◽  
Presidential Elections ◽  
Presidential Campaign ◽  
2008 Presidential Election ◽  
Presidential Candidates ◽  
Emotional Language ◽  
Democratic Parties

Although prior research demonstrates that charisma and rhetoric are two determinants of voting behavior, few studies have examined the effects of charismatic rhetoric and affect as they pertain to the outcomes of presidential elections. Using DICTION software for content analysis, 432 pre-convention speeches from the 2008 presidential election were analyzed to explore the effects that charismatic rhetoric and affect have on presidential candidates’ success. Results indicate that there were more similarities than differences in the charismatic and affect-laden rhetoric of successful and unsuccessful presidential candidates in both the Republican and Democratic parties. Overall, the results demonstrate that both successful and unsuccessful presidential candidates used charismatic rhetoric and emotional language to motivate their followers in the 2008 presidential election.

scholarly journals Support for populist candidates in the 2016 presidential elections predicted by declining social capital and an increase in suicides.

2019 ◽  
Author(s):  
Robert Lynch ◽  
Emily Lynch ◽  
Michael Briga ◽  
Samuli Helle ◽  
Simon Chapman ◽  
...  
Keyword(s):  
Social Capital ◽  
Social Relationships ◽  
Presidential Election ◽  
Presidential Elections ◽  
The United States ◽  
Mortality Rates ◽  
Economic Factors ◽  
Donald Trump ◽  
Per Capita ◽  
Mental And Physical Health

Social relationships have far-reaching effects on both the mental and physical health of individuals and, consequently, the larger communities in which they live. Deteriorating social networks reflect rising alienation and social isolation, and in extreme cases, can result in death. Using changes in mortality rates, measures of social capital, and key demographic and economic factors we show that per capita deaths from alcohol or suicide, as well as an overall decline in social capital, strongly predict support for populist candidates Donald Trump and Bernie Sanders in the United States 2016 presidential election and primaries. These results suggest that healthy social relationships and networks underpin trust in politicians and government, and that their deterioration may result in popular outrage against ‘elites’.

scholarly journals Estimating Time Spent at the Waste Collection Point by a Garbage Truck with a Multiple Regression Model

2021 ◽  
Vol 13 (8) ◽  
pp. 4272
Author(s):  
Robert Giel ◽  
Alicja Dąbrowska
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Waste Collection ◽  
Proposed Model ◽  
Planning Approach ◽  
Planning Processes ◽  
Inaccurate Estimation ◽  
The Impact ◽  
Collection Point

The planning of the garbage trucks’ routes is an essential process in waste collection companies. The main issues in garbage truck routing are determining the optimal routes, minimizing time, decreasing the costs, and reducing the pollution’s emission. In the literature, the time spent at a waste collection point (WCP) is considered as the average time, or it is not included at all. Time spent at a WCP is determined by the processes of picking up, emptying, and putting down the waste containers and the factors specific for different WCPs. Those factors impact the time spent at WCP significantly. Excluding time spent at a WCP or taking the average of that in the planning approach may lead to the inaccurate estimation of total collection time. The aim of this article is to present the multiple regression model for estimating time spent at a WCP. We analyzed the impact of the WCP factors (i.e., building type and number of containers) on the time that a garbage truck spends at it. We initially considered seven chosen factors, five categorical and two numerical. Based on this, we developed the multiple regression model based on linear regression use. Later, the proposed model was validated based on data obtained from the municipal company operating in Wroclaw city, Poland. The study confirmed that the defined factors significantly affect garbage truck’s time spent at a WCP and should be taken into account during waste collection planning processes’ performance.

Voting behavior in the South Korean presidential election – exploring the regional voting pattern

2019 ◽  
Vol 8 (3) ◽  
pp. 268-281
Author(s):  
Tae Wan Kim
Keyword(s):  
South Korea ◽  
Presidential Election ◽  
Voting Behavior ◽  
Design Methodology ◽  
Presidential Elections ◽  
Correlation Coefficients ◽  
Constant Level ◽  
Local Identity ◽  
Content Type ◽  
South Korean

Purpose The purpose of this paper is to examine regional voting patterns in South Korea using the results from six presidential elections since the 1990s. Design/methodology/approach A χ2 test was used to determine the municipalities where a regional voting pattern emerged, and λ correlation coefficients were calculated to examine changes in the regional voting patterns. Findings The analyses lead to three key findings. First, voting patterns differ in Yeongnam and Honam: regional voting in Yeongnam is getting weaker, it remains strong in Honam. Second, the tendency to vote along regional lines decreased significantly in the election in which the Honam party fielded a candidate with a Yeongnam appeared identity. Third, regional voting patterns declined but then stabilized at a constant level, regardless of the candidates’ local identity, which was confirmed in “Bu-Ul-Gyeong.” Originality/value This paper can empirically verify the manifestation of regional voting pattern and confirm the trend. It is possible to derive a condition for suppressing the regional voting pattern.

scholarly journals Life Expectancy and Voting Patterns in 2018 US House of Representative Elections

2020 ◽  
Author(s):  
Lesley H. Curtis ◽  
Molly N Hoffman ◽  
Robert M Califf ◽  
Bradley G. Hammill
Keyword(s):  
Life Expectancy ◽  
House Of Representatives ◽  
Presidential Election ◽  
Presidential Elections ◽  
Parameter Estimate ◽  
Economic Factors ◽  
County Level ◽  
Significant Parameter ◽  
Republican Candidate ◽  
House Election

AbstractIntroductionIn the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in 2018 U.S. House of Representative elections.MethodsWith data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from Harvard Dataverse, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate in the 2018 House election.ResultsAmong 3,107 U.S counties, change in life expectancy at the county level was negatively associated with Republican share of the vote in 2018 House of Representative elections (parameter estimate −7.3, 95% confidence interval, −8.1 to −6.5). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated and no longer statistically significant (parameter estimate −0.9; 95% CI, −2.2 to 0.4).ConclusionCounties with a less positive trajectory in life expectancy were more likely to vote for Republican candidates in 2018 U.S. House of Representatives elections, but the association was mediated by demographic, social and economic factors.

scholarly journals Brazilian Presidential Elections: Analysing Voting Patterns in Time and Space Using a Simple Data Science Pipeline

2020 ◽  
Author(s):  
Lucas Henrique Mantovani Jacintho ◽  
Tiago Pinho Da Silva ◽  
Antonio Rafael Sabino Parmezan ◽  
Gustavo Enrique de Almeida Prado Alves Batista
Keyword(s):  
Presidential Election ◽  
Voting Behavior ◽  
Presidential Elections ◽  
Data Science ◽  
First Year ◽  
Behavior Understanding ◽  
Analysis Process ◽  
Short And Long Term ◽  
Election Results ◽  
Political Economic

Since 1989, the first year of the democratic presidential election after a long period of a dictatorship regime, Brazil conducted eight presidential elections. This period was marked by short and long-term shifts of power and two impeachment processes. Such instability is a case of study in electoral studies, e.g., the study of the population voting behavior. Understanding patterns in the population behavior can give us insight into factors and influences that affect the quality of democratic political decisions. In light of this, our paper focuses on analyzing the Brazilian presidential election voting behavior across the years and the Brazilian territory. Following a data science pipeline, we divided the analysis process into five steps: (i) data selection; (ii) data preprocessing; (iii) identification of spatial patterns, in which we seek to understand the role of space in the election results using spatial autocorrelation techniques; (iv) identification of temporal patterns, where we investigate similar trends of votes over the years using a hierarchical clustering method; and (v) evaluation of the results. It is noteworthy that the data in this work represents the election results at the municipal level, from 1994 to 2018, of the two most relevant parties of this period: the Brazilian Social Democracy Party (PSDB) and the Workers’ Party (PT). Through the results obtained, we found the existence of spatial dependence in every electoral year investigated. Moreover, despite the changes in the political-economic context over the years, neighboring cities seem to present similar voting behavior trends.

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