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Nowcasting (economics)
Nowcasting in economics is the prediction of the present, the very near future, and the very recent past state of an economic indicator. The term is a portmanteau of "now" and "forecasting" and originates in meteorology. It has recently become popular in economics as typical measures used to assess the state of an economy (e.g., gross domestic product (GDP)), are only determined after a long delay and are subject to revision. Nowcasting models have been applied most notably in Central Banks, who use the estimates to monitor the state of the economy in real-time as a proxy for official measures. Principle While weather forecasters know weather conditions today and only have to predict future weather, economists have to forecast the present and even the recent past. Many official measures are not timely due to the difficulty in collecting information. Historically, nowcasting techniques have been based on simplified heuristic approaches but now rely on complex econometric techniqu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nowcasting (meteorology)
Nowcasting is weather forecasting on a very short term mesoscale period of up to 2 hours according to the World Meteorological Organization and up to six hours according to other authors in the field. This forecast is an extrapolation in time of known weather parameters, including those obtained by means of remote sensing, using techniques that take into account a possible evolution of the air mass. This type of forecast therefore includes details that cannot be solved by numerical weather prediction (NWP) models running over longer forecast periods. Principle Nowcasting in meteorology uses surface weather station data, wind profiler data, and any other weather data available to initialize the current weather situation and forecast by extrapolation for a period of 0 to 6 hours. In this time range it is possible to forecast small features such as individual storms with reasonable accuracy. Weather radar echoes and satellite data, giving cloud coverage, are particularly important ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lucrezia Reichlin
Lucrezia Reichlin (born 14 August 1954) is an Italian economist who has been a professor at London Business School since 2008. Reichlin's research focuses on forecasting, business cycle analysis and monetary policy. She pioneered now-casting in economics by developing econometrics methods capable of reading the real time data flow through the lenses of a formal econometric model. These methods are now widely used by central banks and private investors around the world. Early life and education Reichlin was born in 1954, she is the daughter of Alfredo Reichlin, former deputy of the Italian Communist Party and its heir the Democratic Party of the Left, and of Luciana Castellina, co-founder of the newspaper '' Il manifesto'' and also a one-time deputy; her brother Pietro Reichlin is a well-known economist. After completing high school at the Liceo Tasso in Rome, Reichlin graduated in economics at the University of Modena and Reggio Emilia in 1980, and then got a PhD at New York Uni ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Monetary Policy
Monetary policy is the policy adopted by the monetary authority of a nation to control either the interest rate payable for very short-term borrowing (borrowing by banks from each other to meet their short-term needs) or the money supply, often as an attempt to reduce inflation or the interest rate, to ensure price stability and general trust of the value and stability of the nation's currency. Monetary policy is a modification of the supply of money, i.e. "printing" more money, or decreasing the money supply by changing interest rates or removing excess reserves. This is in contrast to fiscal policy, which relies on taxation, government spending, and government borrowing as methods for a government to manage business cycle phenomena such as recessions. Further purposes of a monetary policy are usually to contribute to the stability of gross domestic product, to achieve and maintain low unemployment, and to maintain predictable exchange rates with other currencies. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dynamic Factor
In econometrics, a dynamic factor (also known as a diffusion index) is a series which measures the co-movement of many time series. It is used in certain macroeconomic models. A diffusion index is intended to indicate * the changes of the fraction of economic data time series which increase or decrease over the selected time interval, * an increase or decrease in future economic activity, * provide some correlation to the business sentiment of companies. Formally : X_=\Lambda_F_+e_, where F_=(f^_,\dots,f^_) is the vector of lagged factors of the variables in the T \times N matrix X_ (T is the number of observations and N is the number of variables), \Lambda_ are the factor loadings, and e_ is the factor error. History Diffusion indexes were originally designed to help identify business cycle turning points. Example A diffusion index of monthly employment levels across industries measures the degree to which a growth in employment levels in a population is made up of grow ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Vector Autoregression
In statistics and econometrics, Bayesian vector autoregression (BVAR) uses Bayesian methods to estimate a vector autoregression (VAR) model. BVAR differs with standard VAR models in that the model parameters are treated as random variables, with prior probabilities, rather than fixed values. Vector autoregressions are flexible statistical models that typically include many free parameters. Given the limited length of standarmacroeconomic datasetsrelative to the vast number of parameters available, Bayesian methods have become an increasingly popular way of dealing with the problem of over-parameterization. As the ratio of variables to observations increases, the role of prior probabilities becomes increasingly important. The general idea is to use informative priors to shrink the unrestricted model towards a parsimonious naïve benchmark, thereby reducing parameter uncertainty and improving forecast accuracy. A typical example is the shrinkage prior, proposed by Robert Litte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
2005
File:2005 Events Collage V2.png, From top left, clockwise: Hurricane Katrina in the Gulf of Mexico; the Funeral of Pope John Paul II is held in Vatican City; "Me at the zoo", the first video ever to be uploaded to YouTube; Eris (dwarf planet), Eris was discovered in January 2005 by a Palomar Observatory–based team; Saddam Hussein sits before an Iraqi judge at a courthouse in Baghdad and is executed the 2006, next year; the shrine and resting place for Rafic Hariri in September; the Mars Reconnaissance Orbiter is launched from Cape Canaveral, Kennedy Space Center, designed to explore Mars; The Live 8 concert in the Tiergarten, Berlin., 300x300px, thumb rect 0 0 200 200 Hurricane Katrina rect 200 0 400 200 Funeral of Pope John Paul II rect 400 0 600 200 Me at the zoo rect 0 200 300 400 Live 8 rect 300 200 600 400 Eris (dwarf planet) rect 0 400 200 600 Mars Reconnaissance Orbiter rect 200 400 400 600 Rafic Hariri rect 400 400 600 600 Saddam Hussein 2005 was designated as the Int ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making pred ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mixed-data Sampling
Econometric models involving data sampled at different frequencies are of general interest. Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), Ghysels, Sinko and Valkanov, Andreou, Ghysels and Kourtellos (2010) and Andreou, Ghysels and Kourtellos (2013). MIDAS Regressions A MIDAS regression is a direct forecasting tool which can relate future low-frequency data with current and lagged high-frequency indicators, and yield different forecasting models for each forecast horizon. It can flexibly deal with data sampled at different frequencies and provide a direct forecast of the low-frequency variable. It incorporates each individual high-frequency data in the regression, which solves the problems of losing potentially useful information and including mis-specification. A simple regression exampl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sentiment Analysis
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.Hamborg, Felix; Donnay, Karsten (2021)"NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles" "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume" Examples The objective a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Elsevier
Elsevier () is a Dutch academic publishing company specializing in scientific, technical, and medical content. Its products include journals such as '' The Lancet'', '' Cell'', the ScienceDirect collection of electronic journals, '' Trends'', the '' Current Opinion'' series, the online citation database Scopus, the SciVal tool for measuring research performance, the ClinicalKey search engine for clinicians, and the ClinicalPath evidence-based cancer care service. Elsevier's products and services also include digital tools for data management, instruction, research analytics and assessment. Elsevier is part of the RELX Group (known until 2015 as Reed Elsevier), a publicly traded company. According to RELX reports, in 2021 Elsevier published more than 600,000 articles annually in over 2,700 journals; as of 2018 its archives contained over 17 million documents and 40,000 e-books, with over one billion annual downloads. Researchers have criticized Elsevier for its high profit ma ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
International Journal Of Forecasting
The ''International Journal of Forecasting'' is a quarterly peer-reviewed scientific journal on forecasting. It is published by Elsevier on behalf of the International Institute of Forecasters. Its objective is to "unify the field of forecasting and to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers". The journal was established in 1985. According to the ''Journal Citation Reports'', the journal has a 2021 impact factor of 7.022. Editors-in-chief The editors-in-chief of the journal have been: * Pierre Pinson (2019–) * Esther Ruiz (2019) * Rob J. Hyndman (2005–2018) * Jan G. de Gooijer (1998–2004) * Robert Fildes (1988–1998) * J. Scott Armstrong (1988–1989) * Spyros Makridakis (1985–1987) References External links * Statistics journals Quarterly journals Elsevier academic journals Publications established in 1985 English-language journals {{Academic-journal-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gross Domestic Product
Gross domestic product (GDP) is a money, monetary Measurement in economics, measure of the market value of all the final goods and services produced and sold (not resold) in a specific time period by countries. Due to its complex and subjective nature this measure is often revised before being considered a reliable indicator. List of countries by GDP (nominal) per capita, GDP (nominal) per capita does not, however, reflect differences in the cost of living and the inflation, inflation rates of the countries; therefore, using a basis of List of countries by GDP (PPP) per capita, GDP per capita at purchasing power parity (PPP) may be more useful when comparing standard of living, living standards between nations, while nominal GDP is more useful comparing national economies on the international market. Total GDP can also be broken down into the contribution of each industry or sector of the economy. The ratio of GDP to the total population of the region is the GDP per capita, p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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