The impact of integrated measurement errors on modeling long-run macroeconomic time series

Duffy, James A.; Hendry, David F.

doi:10.1080/07474938.2017.1307177

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…In recent work on linear dynamic systems with nonstationary regressors,Duffy and Hendry (2017) analyzed the effects of measurement error and were able to sign these effects in certain special cases.…”

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confidence: 99%

Latent Variable Nonparametric Cointegrating Regression

2020

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This article studies the asymptotic properties of empirical nonparametric regressions that partially misspecify the relationships between nonstationary variables. In particular, we analyze nonparametric kernel regressions in which a potential nonlinear cointegrating regression is misspecified through the use of a proxy regressor in place of the true regressor. Such models occur in linear and nonlinear regressions where the regressor suffers from measurement error or where the true regressor is a latent or filtered variable as in mixed-data-sampling. The treatment allows for endogenous regressors as the latent variable and proxy variables that cointegrate asymptotically with the true latent variable, including correctly specified as well as misspecified systems, and is therefore intermediate between nonlinear nonparametric cointegrating regression and completely spurious nonparametric nonstationary regression. The results relate to recent work on dynamic misspecification in nonparametric nonstationary systems and the limit theory accommodates regressor variables with autoregressive roots that are local to unity and whose errors are driven by long memory and short memory innovations, thereby encompassing applications with a wide range of economic and financial time series. Some implications for forecasting under misspecification are also examined.

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confidence: 99%

Latent Variable Nonparametric Cointegrating Regression

2020

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“…GDP and its deflator, but monthly data precludes this so we use GVA and CPI inflation instead. As Duffy and Hendry (2017) show, measurement errors in I(1) time series need not substantively affect subsequent cointegration analysis. The unemployment rate exhibits substantial movements over the sample period, with a more than 3 percentage point increase in unemployment following the financial crisis, which persisted for several years before gradually falling back to the lowest levels over the sample period.…”

Section: Modelling the Uk Unemployment Ratementioning

confidence: 94%

“…It is unclear if these will matter for large N due to the law of large numbers. The wide-sense non-stationary analysis in Duffy and Hendry (2017) also suggests measurement errors are not crucial, considering the T N case. When shifts or trends in the data are large, cointegration analysis is not much affected by such measurement errors.…”

Section: Wide-sense Non-stationary Big Datamentioning

confidence: 99%

Modelling non-stationary ‘Big Data’

Castle

Doornik

Hendry

2021

International Journal of Forecasting

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Seeking substantive relationships among vast numbers of spurious connections when modelling Big Data requires an appropriate approach. Big Data are useful if they can increase the probability that the data generation process is nested in the postulated model, increase the power of specification and mis-specification tests, and yet do not raise the chances of adventitious significance. Simply choosing the best-fitting equation or trying hundreds of empirical fits and selecting a preferred one-perhaps contradicted by others that go unreported-is not going to lead to a useful outcome. Wide-sense non-stationarity (including both distributional shifts and integrated data) must be taken into account. The paper discusses the use of principal components analysis to identify cointegrating relations as a route to handling that aspect of non-stationary big data, along with saturation to handle distributional shifts, and models the monthly UK unemployment rate, using both macroeconomic and Google Trends data, searching over 3000 explanatory variables and yet identifying a parsimonious, well-specified and theoretically interpretable model specification.

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“…DFH: Location shifts are not always bad news. For near-integrated measurement errors, Duffy and Hendry (2015) show that location shifts that co-break across variables can reveal the underlying relationships. The specifics of the particular data measurement system should be considered carefully.…”

Section: Nrementioning

confidence: 98%

Economic forecasting in theory and practice: An interview with David F. Hendry

Ericsson

2017

International Journal of Forecasting

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David Hendry has made major contributions to many areas of economic forecasting. He has developed a taxonomy of forecast errors and a theory of unpredictability that have yielded valuable insights into the nature of forecasting. He has also provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. In addition, David has developed new forecast tools, such as forecast encompassing; and he has improved existing ones, such as nowcasting and robustification to breaks. This interview for the International Journal of Forecasting explores David Hendry's research on forecasting. an anonymous referee for helpful comments and discussion, and to Aaron Markiewitz for research assistance. Empirical results and graphics were obtained using 64-bit Ox-Metrics 7.1; see Doornik and Hendry (2013). policy's implementation, or consumers' responses to both. Consequently, my model's forecasts failed badly.NRE: Your UK model was subsequently published as Hendry (1974), which included a new test for predictive failure. It generalized Gregory Chow's (1960) single-equation predictive failure test to systems, albeit in a  2 version rather than the  version that Jan Kiviet (1986) later developed. How did that experience with your small macromodel influence your work on forecasting?DFH: It motivated me to investigate the nature of predictive failure. Why did models built from the best available economics using the latest econometrics and fairly good data not produce useful forecasts? In Hendry (1979b), I linked predictive failure to poor model formulation, but that explanation subsequently turned out to be unhelpful, or at least incomplete. NRE:Other economists were also evaluating forecasts from macro-models. In particular, Charles Nelson wrote two influential papers on ex ante forecasts: Nelson (1972) and Cooper and Nelson (1975).DFH: Charles showed that forecasts from univariate time-series models could beat forecasts from large empirical economic models such as the FRB-MIT-PENN model. From an LSE perspective, such large models treated dynamics inadequately, often simply as autocorrelated errors in static equations. Because of that dynamic misspecification, we suspected that models that included only dynamics could forecast better. I found that simple dynamic models did indeed forecast better than static economic models, even though the latter embedded economic theory whereas the former did not. However, I had misinterpreted the implications of Nelson and Cooper's results. I had not realized that models in differences-such as those in Nelson (1972)almost invariably forecast better than models in levels if the means of the variables being forecast altered. We now refer to such changes as location shifts.

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The impact of integrated measurement errors on modeling long-run macroeconomic time series

Cited by 12 publications

References 11 publications

Latent Variable Nonparametric Cointegrating Regression

Latent Variable Nonparametric Cointegrating Regression

Modelling non-stationary ‘Big Data’

Economic forecasting in theory and practice: An interview with David F. Hendry

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