Multiplier Uncertainty
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Multiplier Uncertainty
In macroeconomics, multiplier uncertainty is lack of perfect knowledge of the multiplier effect of a particular policy action, such as a monetary or fiscal policy change, upon the intended target of the policy. For example, a fiscal policy maker may have a prediction as to the value of the fiscal multiplier—the ratio of the effect of a government spending change on GDP to the size of the government spending change—but is not likely to know the exact value of this ratio. Similar uncertainty may surround the magnitude of effect of a change in the monetary base or its growth rate upon some target variable, which could be the money supply, the exchange rate, the inflation rate, or GDP. There are several policy implications of multiplier uncertainty: (1) If the multiplier uncertainty is uncorrelated with additive uncertainty, its presence causes greater cautiousness to be optimal (the policy tools should be used to a lesser extent). (2) In the presence of multiplier uncertainty, ...
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Macroeconomics
Macroeconomics (from the Greek prefix ''makro-'' meaning "large" + ''economics'') is a branch of economics dealing with performance, structure, behavior, and decision-making of an economy as a whole. For example, using interest rates, taxes, and government spending to regulate an economy's growth and stability. This includes regional, national, and global economies. According to a 2018 assessment by economists Emi Nakamura and Jón Steinsson, economic "evidence regarding the consequences of different macroeconomic policies is still highly imperfect and open to serious criticism." Macroeconomists study topics such as Gross domestic product, GDP (Gross Domestic Product), unemployment (including Unemployment#Measurement, unemployment rates), national income, price index, price indices, output (economics), output, Consumption (economics), consumption, inflation, saving, investment (macroeconomics), investment, Energy economics, energy, international trade, and international finance. ...
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Loss Function
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its opposite (in specific domains, variously called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy. In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data. The concept, as old as Laplace, was reintroduced in statistics by Abraham Wald in the middle of the 20th century. In the context of economics, for example, this i ...
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Review Of Economic Studies
''The Review of Economic Studies'' (also known as ''REStud'') is a quarterly peer-reviewed academic journal covering economics. It was established in 1933 by a group of economists based in Britain and the United States. The original editorial team consisted of Abba P. Lerner, Paul Sweezy, and Ursula Kathleen Hicks. It is published by Oxford University Press. The journal is widely considered one of the top 5 journals in economics. It is managed by the editorial board currently chaired by Nicola Fuchs-Schündeln (Goethe University Frankfurt). The current joint managing editors are Thomas Chaney (Sciences Po), Andrea Galeotti (London Business School), Nicola Gennaioli (Bocconi University), Veronica Guerrieri (University of Chicago), Kurt Mitman (Institute for International Economic Studies, Stockholm University), Francesca Molinari (Cornell University), Uta Schönberg (University College London), and Adam Szeidl (Central European University). According to the ''Journal Citation Repor ...
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Stochastic Control
Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system. The system designer assumes, in a Bayesian probability-driven fashion, that random noise with known probability distribution affects the evolution and observation of the state variables. Stochastic control aims to design the time path of the controlled variables that performs the desired control task with minimum cost, somehow defined, despite the presence of this noise. The context may be either discrete time or continuous time. Certainty equivalence An extremely well-studied formulation in stochastic control is that of linear quadratic Gaussian control. Here the model is linear, the objective function is the expected value of a quadratic form, and the disturbances are purely additive. A basic result for discrete-time centralized systems with only additive uncertainty is ...
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Mutual Fund Theorem
In portfolio theory, a mutual fund separation theorem, mutual fund theorem, or separation theorem is a theorem In mathematics, a theorem is a statement that has been proved, or can be proved. The ''proof'' of a theorem is a logical argument that uses the inference rules of a deductive system to establish that the theorem is a logical consequence of th ... stating that, under certain conditions, any investor's optimal portfolio can be constructed by holding each of certain mutual funds in appropriate ratios, where the number of mutual funds is smaller than the number of individual assets in the portfolio. Here a mutual fund refers to any specified benchmark portfolio of the available assets. There are two advantages of having a mutual fund theorem. First, if the relevant conditions are met, it may be easier (or lower in transactions costs) for an investor to purchase a smaller number of mutual funds than to purchase a larger number of assets individually. Second, from a theor ...
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