Companies must keep track of the changes with moving averages and forecast accuracies to find errors.
A behavioral bias that drove an economic decision was when I was choosing to stop at a gas station that doesn’t have the cheapest gas, just because I really enjoy the people who work there. I am biased of the best gas station choice because I would rather pay 10 cents more a gallon and get to talk to pleasant people. Now I do not have this dilemma because the workers I enjoyed so much all got fired. I now go to the gas station with the cheapest gas now that I am not biased. Given my understanding of behavioral economics, I know I was biased to favor the more expensive gas station.A stream of experimental evidence has documented the human penchant for error. This research is best known for its offshoot, behavioral economics (Fox, 2015). The endowment effect is described when you give somebody a mug and ask how much they will sell it for, and they will name a much higher price than if you tried to sell them the mug in the first place, as Daniel Kahneman, Jack Knetsch, and Richard Thaler discovered in 1990 (Fox, 2015). I also learned that people attach a higher value to things they own than to things they don’t, even when the market values are even (Fox, 2015).
A personal economic decision that was driven by a behavioral bias rather than by pure rational behavior was when I decided to purchase a Jeep wrangler a year ago. I decided to due to this purchase because it was a vehicle that was trending in my hometown. Purchasing this car for me was an impulse buy instead of a need. From the behavioral economics perspective, my decision to buy this car was irrational because I did not take into consideration how much gas it would waste and therefore cause me to have more expenses. I should have maximized my utility and made a rational decision such as buying a car that was more economic. This vehicle consumed quite a lot of fuel than its competitors. The gas prices have been going up so driving this jeep has come out more expensive than I thought. In pure rational behavior, it would be more beneficial to get a car that wastes less gas and turn my jeep into the dealership to avoid extra costs due to the gas increase.
From the articles I’ve found that there is a correlation between market concentration and price levels. The evidence suggests that firms have been increasing their prices to maximize profits. The reason for this is so the firms can reinvest the profits in infrastructure to produce more and therefore more profits are generated. These are known as markups and have grown rapidly since the 90s. There is also evidence that markups are related to market concentration, which has surged in recent decades. Since the mid-1990s the standard measure of concentration used in antitrust analysis, the Herfindahl-Hirschman Index, has risen by 50%. (Galston, 2018) This is because the lack of competition has driven the pricing markups since there is little competition, so they, therefore, dictate the price in the market. Since markups have increased in such a way it has made entry to the market more difficult since the investment of is behind from what the established majority.
A forecast is a prediction of what will happen in the future using either qualitative or quantitative method Forecasts can improve organizational performance through the establishment of a plan, prediction of where the business may be headed, what the customers may want and how to best guide the business in the right direction. It’s a crucial tool that when utilized properly can be a useful tool. In addition to that a time series plot should be one of the first analytic tools used (Anderson et al., 2016). In my organization forecasting is used effectively by seeing reports from last month and forecasting what areas we need to focus on to bring in the most revenue, customer satisfaction and overall company success. Forecasting helped us double our numbers and increase our overall customer satisfaction and trust in our business.
The importance of forecasting in my organization allows it to make strategic decisions regarding healthcare plans and goods that we provide. This strategy enables our customers and clients to utilize our goods and services in ways they see fit to better manage their health. In addition, it helps to prove and support our fiduciary behaviors toward our shareholders and stakeholders. Staying current with trends, initiatives, best practices, laws, and regulations, strategically position us to be a highly reputable healthcare leader that many people can trust and look forward to conducting business with. As noted by Anderson et al., (2016), a forecast is generally a prediction of what will happen in the future. With this being said, a beneficial example that forecasting can bring to its user and audience in the business world if used properly can include the following: increased sales, better pricing, employee retention, etc. Generally, these benefits translate into enhanced overall organizational success, and the more accurate the forecast, the more beneficial the results and expectations will be. If the user is able to successfully incorporate all aspects of what they are trying to accomplish through forecasting, then they will most likely yield the results to match. I believe that forecasting is another term for strategic thinking, as future steps and expectations are trying to be turned into reality.
Due to their ease of use, optimal moving averages, weighted moving averages, and exponential smoothing are commonly employed when attempting to obtain high levels of accuracy for short-term predictions, such as a forecast for the next time period. When a new observation for a time series becomes available, it replaces the oldest observation in the equation, and a new moving average is produced (Anderson et al., 2016). As a result, the periods over which the average is calculated shift with each succeeding period. Exponential smoothing forecasts using a weighted average of prior time series data; it is a subset of the weighted moving averages approach in which only one weight is used (Anderson et al., 2016). Therefore, error measures can be observed and assist in differentiating between actual and measured values. The primary distinction between simple moving average, weighted moving average, and exponential moving average is their sensitivity to changes in the data.
Moving averages are essential when developing a forecast because they measure the momentum that possible forecasts can produce in the near future. The differences between each type of moving average are their formulas (Anderson et al., 2015). However, these calculations do not always provide the most accurate forecast; therefore, three measures of forecast accuracy are used to determine the reliability of the forecast. For example, it can help businesses determine the budget they will have for hiring more employees and giving out bonuses and pay raises. Companies must keep track of the changes with moving averages and forecast accuracies to find errors. These errors will help companies become better at developing more accurate forecasts and identifying changes in business trends (Indeed Editorial Team, 2021).
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