Evidence on Structural Instability in Macroeconomic Time Series Relations
通过实验评估了美国战后宏观经济时间序列中单变量和双变量关系的不稳定性程度，并检验了多种自适应预测技术处理这种不稳定的效果。
Abstract An experiment is performed to assess the prevalence of instability in univariate and bivariate macroeconomic time series relations and to ascertain whether various adaptive forecasting techniques successfully handle any such instability. Formal tests for instability and out-of-sample forecasts from 16 different models are computed using a sample of 76 representative U.S. monthly postwar macroeconomic time series, constituting 5,700 bivariate forecasting relations. The tests for instability and the forecast comparisons suggest that there is substantial instability in a significant fraction of the univariate and bivariate autoregressive models. KEY WORDS: Break testsForecastingRecursive least squaresStructural stabilityTime-varying parameters