Daniele Bianchi, Kenichiro McAlinn
arXiv 18 Mar 2018 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:1803.06738 · PDF · DOI · OpenAlex · Extracted main text
We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within an implied time-varying latent factor model. As a result, the latent inter-dependencies across predictive densities and biases are sequentially learned and corrected. Unlike sparse modeling and variable selection procedures, we do not assume a priori that there is a given subset of active predictors, which characterize the predictive density of a quantity of interest. We test our procedure by investigating the predictive content of a large set of financial ratios and macroeconomic variables on both the equity premium across different industries and the inflation rate in the U.S., two contexts of topical interest in finance and macroeconomics. We find that our predictive synthesis framework generates both statistically and economically significant out-of-sample benefits while maintaining interpretability of the forecasting variables. In addition, the main empirical results highlight that our proposed framework outperforms both LASSO-type shrinkage regressions, factor based dimension reduction, sequential variable selection, and equal-weighted linear pooling methodologies.
appendix boundary found by appendix_command · 77% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Goyal, A., and I. Welch (2008) A comprehensive look at the empirical performance of equity premium prediction | 1.000 | 12 | 3 | 100% |
| 2 | Rapach, D., J. Strauss, and G. Zhou (2010) Out-of-Sample Equity Prediction: Combination Forecasts and Links to the Real Economy | 1.000 | 11 | 3 | 100% |
| 3 | Avramov, D (2004) Stock Return Predictability and Asset Pricing Models | 1.000 | 6 | 3 | 100% |
| 4 | Lewellen, J (2004) Predicting returns with financial ratios | 1.000 | 6 | 3 | 100% |
| 5 | McAlinn, K., and M. West (2017) Dynamic Bayesian predictive synthesis in time series forecasting self | 0.961 | 9 | 3 | 89% |
| 6 | West, M., and P. J. Harrison (1997) Bayesian Forecasting & Dynamic Models | 0.928 | 10 | 4 | 80% |
| 7 | Prado, R., and M. West (2010) Time Series: Modelling, Computation & Inference | 0.874 | 6 | 3 | 67% |
| 8 | Dangl, T., and M. Halling (2012) Predictive Regressions with Time-Varying Coefficients | 0.874 | 6 | 2 | 100% |
| 9 | McCracken, M. W., and S. Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.874 | 5 | 2 | 100% |
| 10 | Stock, J. H., and M. W. Watson (2002) Forecasting using principal components from a large number of predictors | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 68 scored citations.