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Keynes–Tinbergen Debate
John Maynard Keynes and Jan Tinbergen engaged in an exchange of letters in which Keynes initially commented and Tinbergen responded. This conversation was subsequently expanded upon in a book review by Keynes in 1939, which Tinbergen replied to in 1940, followed by a final remark from Keynes in the same year. This discourse is commonly referred to as the Keynes–Tinbergen debate.Boumans, Marcel (2019). "Econometrics: the Keynes–Tinbergen controversy", in ''The Elgar Companion to John Maynard Keynes'' edited by Robert W. Dimand and Harald Hagemann. It was in the field of econometrics. Tinbergen's work Tinbergen's work, titled "Statistical Testing of Business-Cycle Theories, vol. I, A Method and its Application to Investment Activity," centered on using the multiple correlation technique to explain fluctuations in total investment, investment in residential building, and investment in railway rolling stock. He sought to statistically test various economists' theories of business ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
John Maynard Keynes
John Maynard Keynes, 1st Baron Keynes ( ; 5 June 1883 – 21 April 1946), was an English economist and philosopher whose ideas fundamentally changed the theory and practice of macroeconomics and the economic policies of governments. Originally trained in mathematics, he built on and greatly refined earlier work on the causes of business cycles. One of the most influential economists of the 20th century, he produced writings that are the basis for the schools of economic thought, school of thought known as Keynesian economics, and its various offshoots. His ideas, reformulated as New Keynesianism, are fundamental to mainstream economics, mainstream macroeconomics. He is known as the "father of macroeconomics". During the Great Depression of the 1930s, Keynes spearheaded Keynesian Revolution, a revolution in economic thinking, challenging the ideas of neoclassical economics that held that free markets would, in the short to medium term, automatically provide full employment, as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Roy Harrod
Sir Henry Roy Forbes Harrod (13 February 1900 – 8 March 1978) was an English economist. He is best known for writing '' The Life of John Maynard Keynes'' (1951) and for the development of the Harrod–Domar model, which he and Evsey Domar developed independently. He is also known for his ''International Economics'', a former standard textbook of international economics, the first edition of which contained some observations and ruminations (wanting in subsequent editions) that would foreshadow theories developed independently by later scholars (such as the Balassa–Samuelson effect). Biography Harrod was born in London to businessman Henry Dawes Harrod and novelist Frances Forbes-Robertson. He attended St Paul's School and then Westminster School. Harrod attended New College in Oxford on a history scholarship. After a brief period in the Artillery, he gained a first in literae humaniores in 1921, and a first in modern history the following year. Afterwards he spent some ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
False Precision
False precision (also called overprecision, fake precision, misplaced precision, excess precision, and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias. Overview Madsen Pirie defines the term "false precision" in a more general way: when exact numbers are used for notions that cannot be expressed in exact terms. For example, "We know that 90% of the difficulty in writing is getting started." Often false precision is abused to produce an unwarranted confidence in the claim: "our mouthwash is twice as good as our competitor's". In science and engineering, convention dictates that unless a margin of error is explicitly stated, the number of significant figures used in the presentation of data should be limited to what is warranted by the precision of those data ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Inductive Reasoning
Inductive reasoning refers to a variety of method of reasoning, methods of reasoning in which the conclusion of an argument is supported not with deductive certainty, but with some degree of probability. Unlike Deductive reasoning, ''deductive'' reasoning (such as mathematical induction), where the conclusion is ''certain'', given the premises are correct, inductive reasoning produces conclusions that are at best ''probable'', given the evidence provided. Types The types of inductive reasoning include generalization, prediction, statistical syllogism, argument from analogy, and causal inference. There are also differences in how their results are regarded. Inductive generalization A generalization (more accurately, an ''inductive generalization'') proceeds from premises about a Sample (statistics), sample to a conclusion about the statistical population, population. The observation obtained from this sample is projected onto the broader population. : The proportion Q of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linearity
In mathematics, the term ''linear'' is used in two distinct senses for two different properties: * linearity of a '' function'' (or '' mapping''); * linearity of a '' polynomial''. An example of a linear function is the function defined by f(x)=(ax,bx) that maps the real line to a line in the Euclidean plane R2 that passes through the origin. An example of a linear polynomial in the variables X, Y and Z is aX+bY+cZ+d. Linearity of a mapping is closely related to '' proportionality''. Examples in physics include the linear relationship of voltage and current in an electrical conductor ( Ohm's law), and the relationship of mass and weight. By contrast, more complicated relationships, such as between velocity and kinetic energy, are '' nonlinear''. Generalized for functions in more than one dimension, linearity means the property of a function of being compatible with addition and scaling, also known as the superposition principle. Linearity of a polynomial means that it ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Collinearity
In geometry, collinearity of a set of points is the property of their lying on a single line. A set of points with this property is said to be collinear (sometimes spelled as colinear). In greater generality, the term has been used for aligned objects, that is, things being "in a line" or "in a row". Points on a line In any geometry, the set of points on a line are said to be collinear. In Euclidean geometry this relation is intuitively visualized by points lying in a row on a "straight line". However, in most geometries (including Euclidean) a line is typically a primitive (undefined) object type, so such visualizations will not necessarily be appropriate. A model for the geometry offers an interpretation of how the points, lines and other object types relate to one another and a notion such as collinearity must be interpreted within the context of that model. For instance, in spherical geometry, where lines are represented in the standard model by great circles of a spher ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spurious Correlations
In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are associated but '' not'' causally related, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "common response variable", "confounding factor", or "lurking variable"). Examples An example of a spurious relationship can be found in the time-series literature, where a spurious regression is one that provides misleading statistical evidence of a linear relationship between independent non-stationary variables. In fact, the non-stationarity may be due to the presence of a unit root in both variables. In particular, any two nominal economic variables are likely to be correlated with each other, even when neither has a causal effect on the other, because each equals a real variable times the price level, and the common presence of the price level in the two data series imparts correlation to them. (See a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
