Results 221 to 230 of about 103,352 (315)

DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning

Journal of Forecasting, EarlyView.
ABSTRACT This paper compares within‐sample and out‐of‐sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets, and Wouters model is the chosen laboratory using quarterly real‐time euro area data vintages, covering 2001Q1–2019Q4.
Anders Warne
wiley   +1 more source

Forecasting With Dynamic Factor Models Estimated by Partial Least Squares

Journal of Forecasting, EarlyView.
ABSTRACT Dynamic factor models (DFMs) have found great success in nowcasting and short‐term macroeconomic forecasting when incorporating large sets of predictive information. The factor loadings are typically estimated cross‐sectionally with principal component analysis (PCA) or maximum likelihood (ML), which ignore whether the factors have predictive ...
Samuel Rauhala
wiley   +1 more source

Measuring the effects of monetary policy in Pakistan: A factor augmented vector autoregressive approach

This paper examines the effects of monetary policy in Pakistan economy using a data rich environment. We used the Factor Augmented Vector Autoregressive (FAVAR) methodology, which contains 115 monthly variables for the period 1992:01 to 2010:12.
Munir, Kashif, Qayyum, Abdul
core  

Systematic evaluation of integration between China's digital economy and sports industry: Two-stage grey relational analysis and vector autoregressive model. [PDF]

PLoS One
Sun X, Su L, Zhou B, Bu T, Zhang Y.
europepmc   +1 more source

Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now

Journal of Forecasting, EarlyView.
ABSTRACT We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante.
Beomseok Seo, Hyungbae Cho, Dongjae Lee
wiley   +1 more source

Corrigendum: Degenerate Beta autoregressive model for proportion time-series with zeros or ones: an application to antimicrobial resistance rate using R shiny app. [PDF]

Front Public Health
Lobo J, Kamath A, Kalwaje Eshwara V.
europepmc   +1 more source

Nowcasting World Trade With Machine Learning: A Three‐Step Approach

Journal of Forecasting, EarlyView.
ABSTRACT We nowcast world trade using machine learning, distinguishing between tree‐based methods (random forest and gradient boosting) and their linear‐regression‐based counterparts (macroeconomic random forest and gradient boosting—linear). While much less used in the literature, the latter are found to outperform not only the tree‐based techniques ...
Menzie Chinn, Baptiste Meunier, Sebastian Stumpner   +2 more
wiley   +1 more source

Testing for vector autoregressive dynamics under heteroskedasticity

In this paper we introduce a bootstrap procedure to test parameterrestrictions in vector autoregressive models which is robust incases of conditionally heteroskedastic error terms.
Hafner, C.M., Herwartz, H.
core  

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 more
wiley   +1 more source

OLS Estimator for a Mixed Regressive, Spatial Autoregressive Model: Extended Version

We find the asymptotic distribution of the OLS estimator of the parameters $% \beta$ and $\rho$ in the mixed spatial model with exogenous regressors $% Y_n=X_n\beta+\rho W_nY_n+V_n$.
Mynbaev, Kairat
core