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摘要翻译：
本文采用ARIMA(X)、VARX、(G)ARCH等计量经济方法和机器学习算法人工神经网络、岭回归、K-最近邻和支持向量回归，对2019年的月度房屋开工量进行了时间序列分析和预测，并建立了集成模型。集合模型将来自各个单独模型的预测叠加起来，并给出所有预测的加权平均值。分析表明，集成模型的预测误差最小，表现最好，而计量模型的预测误差较大。
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英文标题：
《Time Series Analysis and Forecasting of the US Housing Starts using
Econometric and Machine Learning Model》
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作者：
Sudiksha Joshi
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
In this research paper, I have performed time series analysis and forecasted the monthly value of housing starts for the year 2019 using several econometric methods - ARIMA(X), VARX, (G)ARCH and machine learning algorithms - artificial neural networks, ridge regression, K-Nearest Neighbors, and support vector regression, and created an ensemble model. The ensemble model stacks the predictions from various individual models, and gives a weighted average of all predictions. The analyses suggest that the ensemble model has performed the best among all the models as the prediction errors are the lowest, while the econometric models have higher error rates.
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PDF链接：
https://arxiv.org/pdf/1905.07848
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