When regression coefficients change over time: A proposal
A common approach in forecasting problems is to estimate a least-squares regression (or other statistical learning models) from past data, which is then applied to predict future outcomes. An underlying assumption is that the same correlations that were observed in the past still hold for the future. We propose a model for situations when this assumption is not met: adopting methods from the state space literature, we model how regression coefficients change over time. Our approach can shed light on the large uncertainties associated with forecasting the future, and how much of this is due to changing dynamics of the past. Our simulation study shows that accurate estimates are obtained when the outcome is continuous, but the procedure fails for binary outcomes.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionSimilar Papers 제목 키워드 기반
Are Instrumental Variables Really That Instrumental? Endogeneity Resolution in Regression Models for Comparative Studies
We provide a justification for why, and when, endogeneity will not cause bias in the interpretation of the coefficients in a regression model. This technique can be a viable alternative to, or even used alongside, the in…
regressionInterpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
Linear regression is often deemed inherently interpretable; however, challenges arise for high-dimensional data. We focus on further understanding how linear regression approximates nonlinear responses from high-dimensio…
regressionForecasting foreign exchange rates with regression networks tuned by Bayesian optimization
The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates. To address this problem, we introduce a regression network termed RegPred Net. The exchange rate t…
Bayesian Optimizationglobal-optimizationregressionTime Series+2A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership
The Covid-19 pandemic drastically changed urban mobility, both during the height of the pandemic with government lockdowns, but also in the longer term with the adoption of working-from-home policies. To understand its e…
parameter estimationregressionDynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demand…
Computational Efficiency