Hi buddy, my classmate told me that the presentation is going to presenting the rough draft of term paper……
Account and password attached. Need a ppt draft in about 2 hours.
plan for about one slide per minute.
Include: a title slide, a slide on the literature, a slide on your regression model, and three slides for tables of variable descriptions, summary statistics and regression results. That's it in a nutshell. Info from Page 8 of the syllabus on guidance for presentations is pasted below:
Presentations:
This page contains more detailed guidelines for how to plan your presentation. We have a maximum of 10 minutes per speaker. After this amount of time passes, we absolutely have to move on to the next speaker. We want to allow a minute or two of discussion after each one. So, plan for a seven minute presentation. Please practice your presentations to make sure you don't exceed seven minutes by more than a minute at most.
I've found a good rule of thumb is to plan one slide for each minute of presentation, certainly not more. So say you shoot for seven slides: 1.) Title page, 2.) Literature Review, 3.) the regression model / empirical approach 4.) Table of Variable Descriptions 5.) Table of Summary Statistics 6.) Table of Regression Results 7.) Plan for future research.
Avoid distracting colors and themes, don’t put too much text on a slide, don’t make the numbers you want people to see in your tables tiny, don’t read off your slides (the audience can read them). Do aim to teach the audience something about the econometric methods and data used in the literature you have reviewed, and explain your research findings clearly. In terms of what concepts you present, see the following rubric:
Criteria
Description of Criteria for Presentations
Title slide
5%
Slide 1: Reveal the research question to the audience clearly and at the outset. Motivate interest.
Literature Review and Economic Theory
35%
Slide 2: In this part, briefly discuss the relevant theory. A brief but clear qualitative description of the theory is adequate. On this slide, list up to three references, and discuss in some detail the one that provides the single best discussion of the estimation of the causal effect of interest. Explain why (i.e. discuss ways the state-of-the art study handles a well-defined econometric problem such as Omitted Variable Bias.) The literature review must be integrated and not merely an annotated bibliography.
Data and Methods
30%
Slides 3, 4 and 5: Present the equation you will be estimating here. Describe the data; even though I am grading it and I know where the data comes from, write it for a more general audience. Describe the estimation subsample, and present the tables of Variable Descriptions and Summary Statistics.
Results
25%
Slide 6: Present regression results in a table with several models in different columns. Indicate which of the models is your best attempt at replicating the previously published model, or indicate which is the best attempt to estimate a causal effect Interpret the results for the audience in terms of the sign, magnitude, economic and statistical significance, as well as the implications for the theory discussed on slide 2.
Conclusion
5%
Slide 7: Acknowledge any limitations. Suggest directions for improvement in future research. Discuss policy implications and how your results can be used in a cost-benefit analysis, if applicable.
2
2
RHETORICS IN ECONOMICS
Name
Class
Date
Abstract
The author uses rhetoric as an art of argument. The whole place and narrative put forth aims at driving home the critical inquiry concept. The point driven back here points to the fact that the entire study aims at convincing economists to accept the economy as a rhetorical opportunity and analogy and even practice the same with this in mind. Despite the extensive use of various models in the study, it is significant that we understand that the whole aim of the research is to allow us to look into economic matters rhetorically.
Also, the other vital aspect looked at points to the use of linear regression in getting to the bottom of the whole data management aspects of the study. The reader will notice that this is the single most used data gathering approach that leaves everybody sorted and in good hands. All in all, we conclude by advising the world to dig deeper into the unforeseen events’ operational efficiencies and analysis in economics.
The untapped sector provides a tricky path worth evaluating. Even though we have briefly touched on it, an in-depth look must get put in coming up with the results of the study for that matter. It is always the pleasure of the writer to put things to perspective and ensure that the world is a better place with different ideas and opportunities for growth in this sector.
Introduction
The economic world is riddled with a plethora of ideas. It is thus vital to narrow down based on various financial models. In the commercial world, we have micro and macroeconomic concepts. People must get to the bottom of operation and economic study. The purpose of the work goes around discussion on the Rhetoric of economics as put across by previous authors. The study above seeks to delve into a draft of a research project. It will entail the whole concepts revolving around the topical area of discussion. For a reason, it is crucial to look into factors such as the primary objective of the stud/research question.
Additionally, the research analysis evaluates the various literature review concepts revolving around different economic models. Then also, the data analysis and gathering stage will form a significant part of the study at any given point in time. Interpretation and even the review of the gathered results will summarize the data management topic for this economic theory evaluation and research. Then, we will have the concluding remarks that will seek to summarize all factors related to the investigation and analysis of the whole operations and activities at any given point in time.
The research questions
For this investigation, the paper will examine the place of rhetoric's in economics as brought out by McCloskey. It is vital to begin by the first chapters of the book, which introduces the various phenomena as espoused by economists. The author examines an overview of primary commodities. Additionally, the author comes up with significant aspects regarding the biggest importing nations and also the largest exporters for his phenomena. Then, there is also the study of a typical commodity market. In the end, the work validates the concepts around economic modeling. Both theoretical and conceptual models play a significant role in getting the best out of the whole study and operational efficiency allocations for that matter.
The literature review and the economic theory
There are several economic modeling techniques. The society and the community may elect a particular modeling alternative based on their tastes and preferences. Every expert has a rationale behind the choice of a specific model that they prefer to implement various programs at any given point in time. For this study, we must look into the econometric models.
However, it is also essential that we highlight the other strategies used in models. They include the dynamic system models, spatial equilibrium models, input, output models, and eventually, the linear programming models. As stated earlier, it is crucial to narrow down to the econometric models. And for this reason, we are going to look at about seven examples of the same.
The classical economic model.
In the model, economists present both demand and supply on one model. The classical model opines that when all factors are constant, the quantity of a good or service demanded will increase with the decreased price of the goods and or service under investigation. On the other hand, the law of supply states that, should we not interfere with any factor, an increased price will lead to a consequent increase in the quantity of the commodity supplied to the market at any given point in time. It is this safe to conclude that these laws present a starting point to the introduction and investigation of any activity revolving around the models looked into at any given point in time.
The production possibility frontier model
The primary assumption derived and applied to this model points to the fact that all inputs get utilized with the utmost efficiency. Additionally, the framework talks about the possibility of a maximum productivity of two products given various levels of production. [footnoteRef:1][footnoteRef:2]Productivity is measured using the different production channels, such as technology, land, and even capital, for that matter.[footnoteRef:3][footnoteRef:4][footnoteRef:5] [1: ] [2: Maydeu-Olivares, Alberto, Dexin Shi, and Amanda J. Fairchild. "Estimating causal effects in linear regression models with observational data: The instrumental variables regression model." Psychological methods (2019). ] [3: Petukhova, Tatiana, Davor Ojkic, Beverly McEwen, Rob Deardon, and Zvonimir Poljak. "Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada." PloS one 13, no. 6 (2018): e0198313. ] [4: Tsionas, Efthymios G., and Panayotis G. Michaelides. "A spatial stochastic frontier model with spillovers: Evidence for Italian regions." Scottish Journal of Political Economy 63, no. 3 (2016): 243-257. ] [5: ]
The linear regression models
It points to a statistical tool used to distinguish the relationship between the dependent and the independent variables. There are two types of regression. In the case of a single explanatory variable, the researcher calls this a simple linear regression model. However, for multiple explanatory models, researchers will call this numerous direct regression activity. It is essential to see how the economics world works to look into the relationship between the two variables—one significant application as espoused by economic scholars’ points to the demand activity. A regression model will tell the organization the number of items that consumers will buy based on a series of factors at any given point in time.
The Probit model
The above statistical feature points to a probabilistic approach used to analyze particular data. It's a function-quantile in nature used to reference the normal distribution at any given point in time. The history of this model can be traced back to Chester Bliss, who came up with a descriptive approach used to record the number of pests killed with a particular pesticide. Upon plotting the graph, the author expected a straight line for the same and has subsequently been used by several data gathering researchers to plot graphs and come up with sophisticated results for the same.
Autoregressive integrated moving averages
It is also a statistical tool that is used by the authorities to observe and investigate the time series analysis, particularly. Additionally, the above approach has been used over the years to look into the moving averages and compute the relevant. The objective of this analysis points us to the fact that we can accurately forecast the operations of an economy and get to the bottom of any unforeseen challenge at any given point in time.
A hazard
The hazard model can be defined as a set of human and natural occurrences that cause disruptions to the general and the normal functioning of an activity. An example of such an event may point to the floods. Floods here points to the fact that the organization concerned suffers destruction of property and ends up regretting its very operational existence. A hazard can either be potential or dormant. Then, there is a theoretical possibility of it becoming harmful. Events caused by constant interactions with the dangers are known as incidences. The likely severity of the undesirable consequences of an incident associated with a hazard, combined with the probability of this occurring, constitute the associated risk. If there is no possibility of a danger contributing to an incident, there are no several ways. One of these ways is by specifying the origin of the hazard.
TOBIT Variable
The last bit and model looked at points to the Tobit model. The model points to a class of regression variables where the quality of dependent variables gets looked into, and this censored in a given direction. It is for the same reason that Mr. Tobin developed the model. He wanted to mitigate the zero-inflated data while evaluating and observing household expenditure on durable goods at any given point in time. This model is also useful when looking at truncated and even non-randomly sampled operations and activities for that matter. Therefore, any economists may select to get to the bottom of the same and allow operational efficiencies at any given point in time.
The model selected and the rationale behind the selection.
The discussion section provides several models worth applying to our case study. Based on various factors, we must pick the Tobit Model. The framework gets priority since it modifies the likelihood function to cater for the unequal sampling probability concepts at any given point in time. Additionally, it is also significant that we take a look into the fact that this model mitigates the zero-inflated data. Let’s take note of the fact that our study revolves around the structures of a commodity market and the nitty-gritty surrounding the said applications and investigations at any given point in time. So, the Tobit model stands out since we are not interested in results that will inflate the value of zero inactivity and mess up everything at this particular point in time.
Data description
The data and information gathered on the said subject and study would revolve around the factors examined in the research question. For instance, we will be looking at the number of countries against their import and export volumes and thus use the same as a starting point for the whole investigation. The data description will involve a bar graph with an essential providing accurate applications and meaning of the stand taken based on the descriptive analogy highlighted herein. In addition to the figures, it is also vital to examine various factors outside the topical area of discussion, and this will include the subsample concept as discussed by various scholars and at any given point in time. Therefore, I would like to put it that the demand and supply concept applies to this scenario. Here, we can conclude that with increased demand, the prices go down, thus assuming that all factors remain constant. The reverse is valid at any given point in time. [footnoteRef:6] [6: Müller, Matthias A., and Lars Grüne. "Economic model predictive control without terminal constraints for optimal periodic behavior." Automatica 70 (2016): 128-139. ]
The estimation subsamples
For this study, it would become prudent that we rely on the bootstrap sampling technique. The purpose of this technique points to the fact that the individual will be in a position to provide an extra bit of accuracy. The extra bit of skill comes about because these individuals go over the data again and again. In the end, the operations enter out accurate and to the point at any given point in time. Eventually, the objective of the estimation subsamples points to operational and statistical significance, thus ensuring the audience of a reliable source of information gathered at any given point in time.
The tables of the variable description
The description/statistical model |
Summarized statistical data |
Linear regression model |
24 |
Probit Model |
10 |
Tobit model |
11 |
Autoregressive integrated moving averages |
36 |
The linear equation applicable to my study area points to the one:
Y=MX+C
Where Y is the dependent variable
X is the independent variable.
And the then C points to the constant and can also refer to the level of error investigated at any given point in time.
R Script
1 # Read data
2. Observation <-read.table
3.
4.
5. Descpritive statistics
6.summary(observation & volumes sold
7.Summary (Observation & volumes sxported)
8. Hypothesis Testing
9. HI: is there a significant difference in the volmues sold by and indual country compared to its export levels
> > >
Min. lst square. Median Mean Max.
Presenting the results
The description/statistical model |
Results using the regression analysis |
Linear regression model |
1450 |
Probit Model |
1380 |
Tobit model |
2220 |
Autoregressive integrated moving averages |
2050 |
So, we will use the market share of a country’s goods against its trade volumes for similar products and use that to get to the bottom of the whole operations. Let’s take a hypothetical situation of the fact that we have the following data:
Country Market share Volumes exported.
Poland 20 1600
United States of America 35 2500
The United Kingdom 25 2300
Russia 20 1850
So, we will have the independent variable to point to the market share while the dependent variable to point to the volumes exported. The results calculated will take us to the ones indicated above using the linear regression models at any given point in time. The best model best at replicating my previously set one aspect to the regression model since it accurately provides for the various activities required in getting to the bottom of the whole regression analysis.
Interpretation of the results
There are four aspects to the integration of the results. We have the sign, the magnitude, economic and statistical significance approaches. However, for this study, we shall pick on economic and statistical significance. The statistical significance of the data points us to the fact that the independent and the dependent variables are just enough, and there is no need for any chance that may interfere with the outcome. We relied upon the relationship Y=MX+C. It shows how precise this is. On the other hand, the economic significance of the results points to the fact that there is a relationship between the export goods visive the imported goods given the nations under study. It is thus crucial that all is investigated and highlighted as appropriately possible.
Conclusion
In conclusion, the paper looked into the general topic of rhetoric in economics. The very aim of the research went ahead to examine the very fabric surrounding the economic markets both in the domestic and foreign markets. Majorly the concepts looked at mirrored on the exportation of goods and services. There is, however, one weakness of these traditional models.
They genuinely fail to identify specific risks and ways of coming out of the said risks at any given point in time. In the end, it is crucial to appreciate the place of the society in getting to the bottom of the whole matter. Also, it is vital to understand the stabilization issues critically addressed by the study. In summary, the research seeks to get to fair competition approaches acceptable to all parties at any given point in time and with the right technique.
R scripts of the paper
https://psycnet.apa.org/record/2019-38867-001.R
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198313.R
https://www.sciencedirect.com/science/article/pii/S0005109816301042.R
https://onlinelibrary.wiley.com/doi/abs/10.1111/sjpe.12081.R
https://link.springer.com/chapter/10.1007/978-3-319-42595-5_11.R
Bibliography
1. Maydeu-Olivares, Alberto, Dexin Shi, and Amanda J. Fairchild. "Estimating causal effects in linear regression models with observational data: The instrumental variables regression model." Psychological methods (2019).
2. Petukhova, Tatiana, Davor Ojkic, Beverly McEwen, Rob Deardon, and Zvonimir Poljak. "Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada." PloS one 13, no. 6 (2018): e0198313.
3. Kates, Steven. Classical Economic Theory and the Modern Economy. Edward Elgar Publishing, 2020.
4. Müller, Matthias A., and Lars Grüne. "Economic model predictive control without terminal constraints for optimal periodic behavior." Automatica 70 (2016): 128-139.
5. Mert, Merter. "Measuring economic growth and its relation with production possibility frontier and returns to scale." International Journal of Economics and Research 7, no. 6 (2016): 75-86.
6. Tsionas, Efthymios G., and Panayotis G. Michaelides. "A spatial stochastic frontier model with spillovers: Evidence for Italian regions." Scottish Journal of Political Economy 63, no. 3 (2016): 243-257.
7. McCloskey, Deirdre N. "Rhetoric, Economics, and Nature." In Aesthetics of Universal Knowledge, pp. 249-261. Palgrave Macmillan, Cham, 2017.
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