We analyze the in-sample fit and the out-of-sample forecast performance of the investment equation using different proxies for Tobin’s Q, controlling for cash flow, and using their frequency-decomposed components. We show that different frequencies of bond Q and cash flow significantly improve the empirical performance (both in-sample and out-of-sample) of the traditional investment equation. The key step is to filter out the noisy frequencies of the predictors and only retain those that have the greatest individual predictive power.

中文翻译：

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投资动态和预测：注意频率

我们使用托宾Q的不同代理，控制现金流，并使用它们的频率分解分量来分析投资方程的样本内拟合和样本外预测性能。我们表明，债券Q和现金流的不同频率显着改善了传统投资方程的经验表现（样本内和样本外）。关键步骤是过滤掉预测变量的噪声频率，只保留那些具有最大个体预测能力的频率。