Forecasting Us Inflation Using Dynamic General‐to‐specific Model Selection

Abstract

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out-of-sample forecasts are generated from models identified from a multipath general-to-specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well-established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods. JEL: C22, C52, E31, E37.

Energy Security and Economic Development Based on Complex Computer System Dynamic Model

“…An energy security perspective has generally focused on oil supplies, but it has broadened to include the influence of numerous alternative energy sources, price volatility, supply chain instability, and political stability of oil-producing countries. It is becoming increasingly difficult to define energy security, as new concerns such as global climate change are thrown into the equation [20]. The availability and cost of alternate energy sources should be taken into account when defining energy security and geopolitical considerations.…”