Chapter 21 primary question: Fully develop the AD/AS model with inflation and output as its axis labels in both the short run and long run. Be sure to include all of the elements that go into its development.
Chapter 22 primary question: Describe the Great Moderation, when and how it came about and why it ended as badly as it did.
Chapter 23 primary question: Summarize all of the channels by which the monetary policy transmission mechanism can work.
example of a really good answer:
Distinguish between idiosyncratic and systemic risk.
Idiosyncratic risk is the risk that results from owning a specific security in the form of a stock, bond, CD, etc. It is the risk you think of when you think about losing money investing in a specific thing. For example, if you bought a share of AMZN, and then earnings came out and they were disappointing and the stock price falls resulting in you losing money, you just suffered the consequences of idiosyncratic risk. You can diversify against idiosyncratic risk by purchasing many different securities so that when one does poorly, it doesn’t affect you as much because the others are likely to not be suffering the same problems.
Systemic risk, on the other hand, is risk that affects many securities at once. These are often geopolitical events that result in the market as a whole being affected. For example, a highly infectious virus might originate somewhere in the world and start to spread aggressively causing economic slowdowns/lockdowns that in turn affect the performance of many securities at once. These sorts of things are generally out of the hands of individual investors, and cannot be so easily diversified against.
ter on, one of the most known methods will be discussed in a detailed way. The facial recognition methods that can be used, all have a different approach. Some are more frequently used for facial recognition algorithms than others. The use of a method also depends on the needed applications. For instance, surveillance applications may best be served by capturing face images by means of a video camera while image database investigations may require static intensity images taken by a standard camera. Some other applications, such as access to top security domains, may even necessitate the forgoing of the nonintrusive quality of face recognition by requiring the user to stand in front of a 3D scanner or an infrared sensor. Consequently, there can be concluded that there can be made a division of three groups of face recognition techniques, depending on the wanted type of data results, i.e. methods that compare images, methods that look at data from video cameras and methods that deal with other sensory data, like 3D pictures or infrared imagery. All of them can be used in different ways, to prevent crime from happening or recurring. ii. How do these technologies work? As listed above, there exists a long list of methods and algorithms that can be used for facial recognition. Four of them are used frequently and are most known in the literature, i.e. Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method. But how do these facial recognition work? Because of word limitations, only one of those four facial recognition techniques, i.e The Eigenface Method, will be discussed. Hopefully this will give an general idea of how facial recognition works and can be used. One of the major difficulties of facial recognition, is that you have to cope with the fact that a person’s appearance may change, such that the two images that are being compared differentiate too much from each other. Also environmental changes in pictures, like lightning, have to be taken into account, in order to have successful facial recognition. Thus from a picture of a face, as well as from a live face, some yet more abstract visual representation must be established which can mediate recognition despite the fact that in real life the same face will hardl>GET ANSWER