Results 161 to 170 of about 18,244,857 (290)
Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors [PDF]
We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The information contained in large datasets is captured by few dynamic common factors, which we assume being conditionally heteroskedastic ...
Capasso, Marco +2 more
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Agribusiness, EarlyView.ABSTRACT Market‐based solutions are increasingly tested to address aflatoxin issues in peanuts in developing countries. Although previous studies have found that Haitian grocery store shoppers are willing to pay a 21% premium for peanut butter with levels of aflatoxin that meet international standards, no information is available for the much larger ...
Phendy Jacques +2 more
wiley +1 more source
Alternative methods for forecasting GDP [PDF]
An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the
Patrick Rakotomarolahy, Dominique Guegan
core +2 more sources
Using wavelets for time series forecasting: Does it pay off? [PDF]
By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the ...
Schlüter, Stephan, Deuschle, Carola
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Return and Volatility Spillovers Among Major Cotton Markets
Agribusiness, EarlyView.ABSTRACT This study explores return and volatility transmission among major cotton markets. Several events have disrupted cotton supply and demand in recent years, leading to heightened price volatility and significant shifts in market interconnections.
Susmitha Kalli +3 more
wiley +1 more source
Bootstrap forecasts of multivariate time series
, 2014 En esta tesis se estudia el desempeño de procedimientos que tienen por objetivo la aproximación de densidades de predicción y sus respectivos intervalos y regiones de confianza en series de tiempos multivariantes. En concreto, desarrollamos procedimientos bootstrap para predecir los modelos VAR y DCC, utilizados a menudo en la modelización y predicción
openaire +2 more sources
Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison [PDF]
In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series.
Saarinen, Erkka +2 more
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Agribusiness, EarlyView.ABSTRACT This study examines food price inflation rate convergence among EU27 Member States from 2005 to 2024, focusing on structural breaks, external shocks, and regional disparities. Using panel unit root tests and club convergence analysis, the findings reveal no overall convergence but identify multiple convergence clubs.
Tibor Bareith, Imre Fertő
wiley +1 more source
Multivariate time-series forecasting of liver biomarkers from longitudinal lifestyle data for nonalcoholic steatohepatitis detection. [PDF]
JAMIA OpenMila SA, Ray S.
europepmc +1 more source
Empirical Information Criteria for Time Series Forecasting Model Selection [PDF]
In this paper, we propose a new Empirical Information Criterion (EIC) for model selection which penalizes the likelihood of the data by a function of the number of parameters in the model.
R.J. Hyndman, Md B. Billah, A.B. Koehler
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