Results 141 to 150 of about 23,659 (294)

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

Time-Varying Connectedness Between Global Uncertainties and Economic Activity in a Developing Economy Using a Dynamic Conditional Correlation — GARCH Model

Review of Business and Economics Studies
As economies become increasingly interconnected, individual economies are at risk of shocks from external uncertainties ranging from fluctuations in climate regulations to geopolitical conflicts and international economic policies.
Mohammed Gbanja Abdulai, Andrews Salakpi, Ishaque Mahama   +2 more
doaj   +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  

Bayesian Vector Autoregressive Models and their Applications

, 2013
This master's thesis studies the Bayesian estimation of the vector autoregressive models and their applications. In particular, we begin with the autoregressive models, since they can be treated as one-dimensional vector autoregressive models.
Bao, Min
core  

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

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

Econometric Analysis with Vector Autoregressive Models [PDF]

, 2007
Vector autoregressive (VAR) models for stationary and integrated variables are reviewed. Model specification and parameter estimation are discussed and various uses of these models for forecasting and economic analysis are considered.
LUETKEPOHL, Helmut
core  

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

State Space Methods in Stata

Journal of Statistical Software, 2011
We illustrate how to estimate parameters of linear state-space models using the Stata program sspace. We provide examples of how to use sspace to estimate the parameters of unobserved-component models, vector autoregressive moving-average models, and ...
David M. Drukker, Richard B. Gates
doaj  

Parameter estimation in nonlinear AR–GARCH models [PDF]

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general ...
Mika Meitz, Pentti Saikkonen
core