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Our economics problem solvers provide excellent economics homework help that covers all major economics assignment topics such as given below (in alphabetical order). Click on any of the topcis for more information:
Computational economics is an area of economic studies that combines the concepts and methods of economics, mathematics and computer science. The primary objectives of computational economics are to integrate data and information technology into economics and to automate the formerly manual, error-prone statistical and analytical processes. As an advanced field of research, it allows economists to use a range of computational tools to solve complex analytical problems.
It also helps them model or predict the ways in which the many agents within economic systems and financial marketplaces interact and the outcome of those interactions. For new-age economists, especially those working in the financial domain, the ability to conceptualize and verbally articulate an economic problem; to develop and formulate the problem as a mathematical model; and then to represent the model in computer software to solve the problem is becoming an increasing crucial skill. Computational economics helps them address all the three at once effectively-both time and cost-wise.
Computational economics uses advanced computer programs to conceptualize and solve complex analytical problems from all branches of economics. It can be applied in the study of both theoretical and empirical economics. The main areas of study that use computational economics include:
All branches of economics can use computer-based modeling for analytical problems and thus enter the field of computational economics. As a relatively new field of study though, the potential and usage of computational economics is still being explored by researchers. They believe that as this field continues to mature, its application will expand significantly and reintegrated into the larger discipline of economics.
An inherent characteristic of an adaptive and complex economic system is the presence of a nonlinear interaction and feedback mechanism among the actors. This makes it very difficult and time consuming to analyze them with manual or conventional analytical methods. Agent-based Computational Economics (ACE) uses advanced computer simulations to represent such complex systems and easily analyze nonlinear processes within them. It is a specialized field of computational economics used to construct models of different economic processes. It constructs the process models initially using user-defined fixed conditions but later develops them on the basis of the interactions among the factors or agents. Agents used to create the models can be individuals such as customers and respondents; institutions such as financial markets and central banks; biological entities such as cattle and livestock; and physical entities such as geographical area and weather. The type and number of agents used by the researcher to create a model depends on the given formulation and the specific problem being solved.
The use of agent-based modeling helps researchers achieve many critical goals of computational economics. The method is especially useful for researchers who want to virtually evaluate the performance of a given system of processes, policies or organizations before they are formally implemented to ensure that the outcomes are beneficial for all stakeholders. To generate unbiased outcomes, researchers introduce different types of agents and other relevant factors all of which have their own motivations and learning capabilities, and let them freely interact and develop over a period of time in a virtual environment. The main question researchers try to answer using ACE is whether a particularly motivated behavior by any of the agents results in inefficiency, disorder or unfair advantage in the system. Answering this question also allows them to identify potential drawbacks and deficiencies in a proposed plan, policy or system before it is implemented.
The ACE method also helps researchers identify patterns within an economic system which may be missed or appear to be completely random in the first glance. The observed patterns can be explained by tracking recurrent patterns in which the agents interact over time. Researchers have used computerized simulations effectively identify recurring patterns in trade networks, business cycles, and market procedures. Recent advances in statistical analysis, programming methodology, and visualization techniques combined with ACE have expanded the capability of computational economists and allowed them to generate newer perspectives and theories on economic phenomena and what leads to them.
Machine learning: Machine learning models developed using computational economics provide an effective method to clean and organize large, unstructured and complex datasets. Machine learning methods such as random forest and the kernel method are used extensively in statistical analysis and data mining. Compared to conventional statistical methods such as the STAR method, models developed using machine learning methods provide much better flexibility, classification and predictive capabilities. Others such as the causal tree method in machine learning are known to allow inference testing which many conventional methods lack. The tuning function built into the machine learning models makes statistical analysis very efficient for researchers by automating several tasks. These models can conduct the empirical analysis, cross-validate the results, and estimate and compare against different models concurrently, thereby saving significant amount of time and effort of the researcher.
Using the machine learning model is also seen to generate more precise and robust estimates than the conventional methods. Development of other machine learning methods such as deep learning and reinforced learning is projected to simplify heterogeneous analysis to a great extent and create models that reflect the behaviors of different agents in the economy more realistically. Moreover, the relatively recent adoption of deep learning and neural networks in computational economics significantly reduces the amount of manual effort required for redundant tasks of data cleaning and analytics. This again substantially reduces the time and cost required for large scale data analytics; allows researchers to collect, organize and analyze vast amounts of data more efficiently; and encourages them to explore new methods of modeling economic problems. Additionally, lesser time spent on data analysis enables researchers to emphasize more on the actual subject matter such as realism of the model, confounding variables, and causal inference. When provided with proper guidance, machine learning models have the capability to quickly develop accurate and applicable solutions through computation and large-scale empirical analysis.
In a nonlinear and dynamic economic system such as the financial market, computational economics is used extensively to solve multifaceted problems and provide numerical solutions in the fields of macroeconomics and public finance. It allows researchers to broaden their understanding of observed norms, regularities, and potential behaviors and patterns within such systems in the real world by constructing virtual or simulated systems. This also helps eliminate the problems related to using algorithms and formulas derived only through economic reasoning which rarely correlate with real world scenarios. Most computational economics software packages come preprogrammed with routine optimization functions such as CONOPT and Gauss and allow economists to automate tasks and generate solutions quickly while harnessing the immense capabilities of computer science.
Advanced software and programs are used to formulate analytical and statistical economic problems and then model their solutions. Computational economics allows researchers to simulate precise models that involve extremely complex and numerous calculations that are difficult to be performed manually in a timely manner. For example, agent-based computer modeling often uses a number of agents which are computer-defined models of objects to represent and study groups, demographics, and even separate individuals in an entire economy in a social simulation environment.
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You are not alone if you wished an economics problem solver would solve your computational economics homework problems or just do my economics assignment. As economics problem solvers, we solve basic and advanced economics homework questions for college and university students who study in different countries and different universities. Some students approach us for help with economics problem calculators while others just tell us something simpler-'Do my economics homework'. Either way, we are happy to help. Our experts are well-qualified and experienced to write some of the best economics homework assignments, but what really makes us stand apart is our commitment and passion to deliver only the best homework papers so that you get high marks in your college or university assessments. There could be situations when you might need some help after you receive the completed assignment from us-in such cases, you are free to ask for any number of clarifications. We will only be happy to help.
In order to appreciate Managerial economics homework help, we should first get our concept of managerial economics clear. What exactly is managerial economics? There are a number of definitions of managerial economics, lets look at a couple of them. According to Wikipedia, managerial economics bridges economics theory and practice, drawing heavily from qualtitative techniques like regression analysis, correlation and calculus. It further defines managerial economics as the application of economic concepts and analysis to help in rational managerial decisions.
There is one more definition of managerial economics which defines it as "is the science of directing scarce resources to manage cost-effectively. It consists of three branches: competitive markets, market power, and imperfect markets. A market consists of buyers and sellers that communicate with each other for voluntary exchange. Whether a market is local or global, the same managerial economics apply."
Now that we have the fundamentals clear, we can now dive into the more intricate components of managerial economics. All you need to do is to email us your economics homework details to [email protected]. We generally respond to all our emails in less than 10 minutes. If you need instance help with your managerial economics homework, chat with our customer success team on our website - we are available 24x7, including weekends and holidays.
Microeconomics deals with economics at an individual or company level whereas macro economics deals with economics at a national or global level. While microeconomics studies the decisions of individuals of people and organizations with respect to allocation of resources and prices of goods and services, it focuses on supply and demand, among other factors that influence price levels.
Macroeconomics, on the other hand, studies the entire economy of a country. It deals with entire economies and studies concepts like GDP (Gross Domestic Product) and the impact of national income, rate of growth, price levels and unemployment on GDP. John Maynard Keynes started the use of the term monetary aggregates and is credited with founding macroeconomics.
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