Real-Time Forecasting US GDP from Small-Scale Factor Models
نویسندگان
چکیده
منابع مشابه
Forecasting New Zealand’s Real GDP
Recent time series methods are applied to the problem of forecasting New Zealand’s real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-deter...
متن کاملModeling and forecasting China's GDP data with time series models
The gross domestic product (GDP), a basic measure of an economy's economic performance, is the market value of all final goods and services produced within the borders of a nation in a year. In this paper, the features of quarterly data of China’s GDP obtained from aggregated annual data of the National Bureau of Statistics of China starting from 1962 to 2008 are studied. Testing for existing a...
متن کاملReal-Time Forecasting by Bio-Inspired Models
In recent years, bio-inspired methods for problem solving, such as Artificial Neural Networks (ANNs) or Genetic and Evolutionary Algorithms (GEAs), have gained an increasing acceptance as alternative approaches for forecasting, due to advantages such as nonlinear learning and adaptive search. The present work reports the use of these techniques for Real-Time Forecasting (RTF), where there is a ...
متن کاملPredicting Recessions: Forecasting US GDP Growth through Supervised Learning
Machine learning algorithms have gained much popularity in finance, where the abundance of training examples and high-frequency sampling rates produce datasets that are amenable to successful regression. In macroeconomics, however, where data is scarce and sampling rates are far lower, learning algorithms have not been extensively explored, and even within the sparse literature success has been...
متن کاملModeling and Forecasting Effects of Crude Oil Price Changes on the US and UK GDP
       This paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and GDP of the US, as the largest oil consumer, and the UK, as the oil producer. GMDH neural network and MLFF neural network approaches, which are both non-linear models, are employed to forecast GDP responses to the oil price changes. The resul...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2014
ISSN: 1556-5068
DOI: 10.2139/ssrn.2508179