Structural Breaks in Carbon Emissions: A Machine Learning Analysis
نویسندگان
چکیده
منابع مشابه
Learning, Forecasting and Structural Breaks
The literature on structural breaks focuses on ex post identification of break points that may have occurred in the past. While this question is important, a more challenging problem facing econometricians is to provide forecasts when the data generating process is unstable. The purpose of this paper is to provide a general methodology for forecasting in the presence of model instability. We ma...
متن کاملUsing machine learning to identify structural breaks in single-group interrupted time series designs.
RATIONALE, AIMS AND OBJECTIVES Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the ...
متن کاملLearning, Structural Breaks, and Asset-Return Dynamics
This paper studies a representative-agent asset-pricing model of an endowment economy in which the agent has incomplete knowledge about exogenous stochastic endowment process and has incentive to learn about the process with adaptive learning rules. There is the well documented fact that when underlying economic environment is known and is common knowledge to investors, asset-pricing models und...
متن کاملPrediction of carbon dioxide emissions based on principal component analysis with regularized extreme learning machine: The case of China
Nowadays, with the burgeoning development of economy, CO2 emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast CO2 emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make CO2 emissions prediction bas...
متن کاملA New Nonlinear Specification of Structural Breaks for Money Demand in Iran
In a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. The use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. The simple and basic idea in this paper i...
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: IMF working paper
سال: 2022
ISSN: ['1018-5941', '2227-8885']
DOI: https://doi.org/10.5089/9798400200267.001