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on Forecasting |
| By: | Alexander Chudik; M. Hashem Pesaran; Mahrad Sharifvaghefi |
| Abstract: | This paper is concerned with the problem of variable selection in the presence of parameter instability when both the marginal effects of signals on the target variable and the correlations of the covariates in the active set could vary over time. We pose the issue of whether one should use weighted or unweighted observations at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large. We allow parameter instability to be continuous or discrete, subject to certain regularity conditions. We discuss the pros and cons of Lasso and the One Covariate at a time Multiple Testing (OCMT) method for variable selection and argue that OCMT has important advantages under parameter instability. We establish three main theorems on selection, estimation post selection, and in-sample fit. These theorems provide justification for using unweighted observations at the selection stage of OCMT and down-weighting of observations only at the forecasting stage. It is shown that OCMT delivers better forecasts, in mean squared error sense, as compared to Lasso, Adaptive Lasso and boosting both in Monte Carlo experiments as well as in 3 sets of empirical applications: forecasting monthly returns on 28 stocks from Dow Jones , forecasting quarterly output growths across 33 countries, and forecasting euro area output growth using surveys of professional forecasters. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.15494&r=for |
| By: | Jean-Yves Pitarakis |
| Abstract: | We introduce a novel approach for comparing out-of-sample multi-step forecasts obtained from a pair of nested models that is based on the forecast encompassing principle. Our proposed approach relies on an alternative way of testing the population moment restriction implied by the forecast encompassing principle and that links the forecast errors from the two competing models in a particular way. Its key advantage is that it is able to bypass the variance degeneracy problem afflicting model based forecast comparisons across nested models. It results in a test statistic whose limiting distribution is standard normal and which is particularly simple to construct and can accommodate both single period and longer-horizon prediction comparisons. Inferences are also shown to be robust to different predictor types, including stationary, highly-persistent and purely deterministic processes. Finally, we illustrate the use of our proposed approach through an empirical application that explores the role of global inflation in enhancing individual country specific inflation forecasts. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16099&r=for |
| By: | Todd E. Clark; Matthew V. Gordon; Saeed Zaman |
| Abstract: | This paper examines the forecasting efficacy and implications of the recently popular breakdown of core inflation into three components: goods excluding food and energy, services excluding energy and housing, and housing. A comprehensive historical evaluation of the accuracy of point and density forecasts from a range of models and approaches shows that a BVAR with stochastic volatility in aggregate core inflation, its three components, and wage growth is an effective tool for forecasting inflation's components as well as aggregate core inflation. Looking ahead, the model's baseline projection puts core inflation at 2.6 percent in 2026, well below its 2023 level but still elevated relative to the Federal Reserve's 2 percent objective. The probability that core inflation will return to 2 percent or less is much higher when conditioning on goods or non-housing services inflation slowing to pre-pandemic levels than when conditioning on these components remaining above the same thresholds. Scenario analysis indicates that slower wage growth will likely be associated with reduced inflation in all three components, especially goods and non-housing services, helping to return core inflation to near the 2 percent target by 2026. |
| Keywords: | Supercore inflation; forecast aggregation; Bayesian vector autoregression; scenario analysis |
| JEL: | C32 C53 E17 E31 E37 |
| Date: | 2023–12–20 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwq:97496&r=for |