使用机器学习算法预测经济衰退
Predicting Economic Recessions Using Machine Learning Algorithms
摘要：
即使在2008年初，也没有预测到2008/09年的经济衰退。未能预测衰退是经济预测中的一个持久主题。《专业预测者调查》提供了自1960年代末以来对美国未来一个、两个、三个和四个季度总产出、国内生产总值增长的预测数据。在未来三个季度的时间里，对GDP增长的平均预测从未出现过负值。未来三个季度的平均SPF预测与数据之间的相关性非常低，并且在最近25年中与零没有显着差异。在这里，我们展示了随机森林的机器学习技术有可能对经济衰退做出早期预警。我们使用了一小部分来自金融市场的解释变量，这些变量在预测者做出预测时就可以得到。我们在1970年Q2 - 1990年Q1期间对算法进行训练，并提前一个季度、三个季度和六个季度进行预测。然后，我们在1970 Q2 - 1990 Q2之间重新训练，并进行进一步的预测，等等。我们没有尝试任何预测优化，只使用我们在包R中下载的算法的默认输入参数。我们将从1990年到现在的预测与实际数据进行比较。提前四分之一，算法无法改进SPF预测。在接下来的三个季度和六个季度，实际和预测之间的相关性很低，但它们与零有非常显著的差异。尽管时间上有点错误，但2009年上半年的严重衰退本可以在2007年底提前六个季度预测。该算法从来没有预测到经济衰退时，一个没有发生。在英国的情况下，我们使用随机森林机器学习技术获得了更强的结果。
英文摘要：
Even at the beginning of 2008, the economic recession of 2008/09 was not being predicted. The failure to predict recessions is a persistent theme in economic forecasting. The Survey of Professional Forecasters (SPF) provides data on predictions made for the growth of total output, GDP, in the United States for one, two, three and four quarters ahead since the end of the 1960s. Over a three quarters ahead horizon, the mean prediction made for GDP growth has never been negative over this period. The correlation between the mean SPF three quarters ahead forecast and the data is very low, and over the most recent 25 years is not significantly different from zero. Here, we show that the machine learning technique of random forests has the potential to give early warning of recessions. We use a small set of explanatory variables from financial markets which would have been available to a forecaster at the time of making the forecast. We train the algorithm over the 1970Q2-1990Q1 period, and make predictions one, three and six quarters ahead. We then re-train over 1970Q2-1990Q2 and make a further set of predictions, and so on. We did not attempt any optimisation of predictions, using only the default input parameters to the algorithm we downloaded in the package R. We compare the predictions made from 1990 to the present with the actual data. One quarter ahead, the algorithm is not able to improve on the SPF predictions. Three and six quarters ahead, the correlations between actual and predicted are low, but they are very significantly different from zero. Although the timing is slightly wrong, a serious downturn in the first half of 2009 could have been predicted six quarters ahead in late 2007. The algorithm never predicts a recession when one did not occur. We obtain even stronger results with random forest machine learning techniques in the case of the United Kingdom.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
