Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models |
| |
Authors: | Norman R Swanson Halbert White |
| |
Institution: | aThe Pennsylvania State University, 521 Kern Graduate Bldg., Department of Economics, University Park, PA 16802, USA;bResearch Group for Econometric Analysis, Department of Economics, University of California, San Diego, La Jolla, CA 92093, USA |
| |
Abstract: | Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings. |
| |
Keywords: | Cointegration Confusion rate Linearity Model selection Nonlinearity Parameter evolution Real-time forecasting |
本文献已被 ScienceDirect 等数据库收录! |
|