Low-Frequency Econometrics

47 Pages Posted: 21 Sep 2015 Last revised: 4 Jun 2022

See all articles by Ulrich K. Müller

Ulrich K. Müller

Princeton University - Department of Economics

Mark W. Watson

Princeton University - Princeton School of Public and International Affairs; National Bureau of Economic Research (NBER)

Date Written: September 2015

Abstract

Many questions in economics involve long-run or trend variation and covariation in time series. Yet, time series of typical lengths contain only limited information about this long-run variation. This paper suggests that long-run sample information can be isolated using a small number of low-frequency trigonometric weighted averages, which in turn can be used to conduct inference about long-run variability and covariability. Because the low-frequency weighted averages have large sample normal distributions, large sample valid inference can often be conducted using familiar small sample normal inference procedures. Moreover, the general approach is applicable for a wide range of persistent stochastic processes that go beyond the familiar I(0) and I(1) models.

Suggested Citation

Müller, Ulrich K. and Watson, Mark W., Low-Frequency Econometrics (September 2015). NBER Working Paper No. w21564, Available at SSRN: https://ssrn.com/abstract=2663202

Ulrich K. Müller (Contact Author)

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States
609-258-3216 (Phone)
609-258-4026 (Fax)

HOME PAGE: http://www.princeton.edu/~umueller

Mark W. Watson

Princeton University - Princeton School of Public and International Affairs ( email )

Princeton University
Princeton, NJ 08544-1021
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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