Using real‐time data from 1985:Q1 to 2017:Q3 and simple vector autoregression (VAR) models, we show that there is a substantial payoff in combining credit supply indicators with house prices for forecasting real economic activity in the USA. Consistent with the findings in the literature, we show that the forecasts from a bivariate VAR model of real activity and credit conditions dominate the forecasts from a univariate model of real activity. The most interesting finding of the paper is that once real house price growth is added to the bivariate VAR model of real activity and credit conditions, the forecasting performance improves significantly. The forecasts from the model that contains credit supply indicator and real house price growth are also competitive with the forecasts of the Survey of Professional Forecasters. These results provide further evidence in support of the recent theoretical and empirical research on the dynamic relationship between housing market and credit conditions and its role in explaining real economic activity fluctuations in the USA.

中文翻译：

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使用房价和信贷条件预测实时经济活动

使用1985：Q1到2017：Q3的实时数据和简单的矢量自回归（VAR）模型，我们表明，将信贷供应指标与房价相结合来预测美国的实际经济活动，将会获得可观的回报。与文献中的发现一致，我们表明真实活动和信用状况的双变量VAR模型的预测支配着真实活动的单变量模型的预测。该论文最有趣的发现是，一旦将实际房价增长添加到实际活动和信用状况的二元VAR模型中，预测性能就会显着提高。包含信贷供应指标和实际房价增长的模型的预测也与专业预测员调查的预测具有竞争力。