Results 91 to 100 of about 77,552 (290)
Acknowledgement Misspecification in Macroeconomic Theory [PDF]
We explore methods for confronting model misspecification in macroeconomics. We construct dynamic equilibria in which private agents and policy makers recognize that models are approximations.
Hansen, Lars-Peter, Sargent, Thomas-J
core
Energy Science &Engineering, EarlyView.ABSTRACT This research aims to explore the effects of agricultural productivity and the use of renewable energy sources on India's CO2 emissions while taking economic expansion into concern based on data during 1985–2022. This study applies the autoregressive distributed lag (ARDL) technique, dynamic‐ordinary least square (DOLS), fully‐modified ...
Palanisamy Manigandan +4 more
wiley +1 more source
Generalized Bayes approach to inverse problems with model misspecification. [PDF]
Inverse Probl, 2023 Baek Y, Aquino W, Mukherjee S.
europepmc +1 more source
Assessing estimation uncertainty under model misspecification
Scandinavian Journal of StatisticsAbstract Model misspecification is ubiquitous in data analysis because the data‐generating process is often complex and mathematically intractable. Therefore, assessing estimation uncertainty and conducting statistical inference under a possibly misspecified working model is unavoidable.
Rong Li, Yichen Qin, Yang Li
openaire +2 more sources
Coherent Forecasting of Realized Volatility
Journal of Forecasting, EarlyView.ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
wiley +1 more source
Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach
Journal of Forecasting, EarlyView.ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch +3 more
wiley +1 more source
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
Improving Implied Volatility Forecasts for American Options Using Neural Networks
Journal of Futures Markets, EarlyView.ABSTRACT This paper explores the application of neural networks to improve pricing of American options. Focusing on both American and European options on the S&P 100 index from January 2016 to August 2023, we integrate neural networks to model the difference between market‐implied and model‐implied volatilities derived from the Black‐Scholes and Heston
Haitong Jiang, Emese Lazar, Miriam Marra
wiley +1 more source
Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity. [PDF]
Stat Methods Med Res, 2023 Zhang Y, Vock DM, Patrick ME, Murray TA.
europepmc +1 more source
Spatial Econometrics Revisited: A Case Study of Land Values in Roanoke County [PDF]
Omitting spatial characteristics such as proximity to amenities from hedonic land value models may lead to spatial autocorrelation and biased and inefficient estimators.
Bosch, Darrell J. +2 more
core +1 more source