Pub Date : 2019-02-28DOI: 10.1093/OSO/9780190900663.003.0002
J. Racine
This chapter outlines pitfalls of using standard inference procedures common in cross- sectional settings in time series settings and presents alternative procedures. It also addresses the issue of spurious regression and cautions the reader against the unquestioning use of cross section tools in time series settings.
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Pub Date : 2019-02-28DOI: 10.1093/oso/9780190900663.003.0003
J. Racine
This chapter looks at a range of popular univariate time series models and their use for forecasting.
本章着眼于一系列流行的单变量时间序列模型及其在预测中的应用。
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Pub Date : 2019-02-28DOI: 10.1093/OSO/9780190900663.003.0004
J. Racine
This chapter looks at alternatives to the use of asymptotic theory and finite-sample theory for the purpose of inference. It considers numerical approaches that include the bootstrap and the Jackknife and considers procedures for dependent processes as well as heteroskedastic and independent identically distributed instances.
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Pub Date : 2019-02-28DOI: 10.1093/OSO/9780190900663.003.0005
J. Racine
This chapter looks at issues surrounding outliers in data and methods for addressing their presence.
本章着眼于数据异常值周围的问题和解决异常值存在的方法。
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Pub Date : 2019-02-28DOI: 10.1093/OSO/9780190900663.003.0001
J. Racine
This chapter introduces time series data and outlines how it differs from cross sectional data. It also highlights how the object of interest when modelling time series data is the forecast, which differs from the object of interest in cross-sectional modelling, which is typically some parameter of interest that has an economic interpretation.
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