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Econometrics, Volume 8, Issue 4 (December 2020) – 6 articles

Cover Story (view full-size image): We study the influence of revisions of global mean temperature and global mean sea level data on the estimated statistical relation between the two series. We find that four alternative models proposed in the literature are sensitive to these data revisions, with substantial changes in the coefficient estimate that relates sea level to temperature (differences of up to 50%). These changes in the parameter estimates translate to substantial changes in long-term sea level projections obtained from temperature scenarios (differences of up to 40 cm). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to work with the data vintages that were available at the time. View this paper

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25 pages, 480 KiB

Open AccessArticle

Direct and Indirect Effects under Sample Selection and Outcome Attrition

by Martin Huber and Anna Solovyeva

Econometrics 2020, 8(4), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics8040044 - 07 Dec 2020

Cited by 2 | Viewed by 3449

Abstract

This paper extends the evaluation of direct and indirect treatment effects, i.e., mediation analysis, to the case that outcomes are only partially observed due to sample selection or outcome attrition. We assume sequential conditional independence of the treatment and the mediator, i.e., the variable through which the indirect effect operates. We also impose missing at random or instrumental variable assumptions on the outcome attrition process. Under these conditions, we derive identification results for the effects of interest that are based on inverse probability weighting by specific treatment, mediator, and/or selection propensity scores. We also provide a simulation study and an empirical application to the U.S. Project STAR data in which we assess the direct impact and indirect effect (via absenteeism) of smaller kindergarten classes on math test scores. The estimators considered are available in the ‘causalweight’ package for the statistical software ‘R’. Full article

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26 pages, 350 KiB

Open AccessArticle

Forward Rate Bias in Developed and Developing Countries: More Risky Not Less Rational

by Michael D. Goldberg, Olesia Kozlova and Deniz Ozabaci

Econometrics 2020, 8(4), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics8040043 - 02 Dec 2020

Cited by 1 | Viewed by 3143

Abstract

This paper examines the stability of the Bilson–Fama regression for a panel of 55 developed and developing countries. We find multiple break points for nearly every country in our panel. Subperiod estimates of the slope coefficient show a negative bias during some time periods and a positive bias during other time periods in nearly every country. The subperiod biases display two key patterns that shed light on the literature’s linear regression findings. The results point toward the importance of risk in currency markets. We find that risk is greater for developed country markets. The evidence undercuts the widespread view that currency returns are predictable or that developed country markets are less rational. Full article

(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)

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54 pages, 640 KiB

Open AccessArticle

A Parameterization of Models for Unit Root Processes: Structure Theory and Hypothesis Testing

by Dietmar Bauer, Lukas Matuschek, Patrick de Matos Ribeiro and Martin Wagner

Econometrics 2020, 8(4), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics8040042 - 10 Nov 2020

Cited by 3 | Viewed by 2777

Abstract

We develop and discuss a parameterization of vector autoregressive moving average processes with arbitrary unit roots and (co)integration orders. The detailed analysis of the topological properties of the parameterization—based on the state space canonical form of Bauer and Wagner (2012)—is an essential input for establishing statistical and numerical properties of pseudo maximum likelihood estimators as well as, e.g., pseudo likelihood ratio tests based on them. The general results are exemplified in detail for the empirically most relevant cases, the (multiple frequency or seasonal) I(1) and the I(2) case. For these two cases we also discuss the modeling of deterministic components in detail. Full article

(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)

19 pages, 413 KiB

Open AccessFeature PaperArticle

Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature

by Eric Hillebrand, Søren Johansen and Torben Schmith

Econometrics 2020, 8(4), 41; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics8040041 - 02 Nov 2020

Cited by 2 | Viewed by 3948

Abstract

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880–2001 and 1880–2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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15 pages, 510 KiB

Open AccessArticle

Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data

by Erhard Reschenhofer and Manveer K. Mangat

Econometrics 2020, 8(4), 40; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics8040040 - 10 Oct 2020

Cited by 1 | Viewed by 2970

Abstract

For typical sample sizes occurring in economic and financial applications, the squared bias of estimators for the memory parameter is small relative to the variance. Smoothing is therefore a suitable way to improve the performance in terms of the mean squared error. However, in an analysis of financial high-frequency data, where the estimates are obtained separately for each day and then combined by averaging, the variance decreases with the sample size but the bias remains fixed. This paper proposes a method of smoothing that does not entail an increase in the bias. This method is based on the simultaneous examination of different partitions of the data. An extensive simulation study is carried out to compare it with conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance and its smoothed competitors with respect to the bias. Using the results of the simulation study for the proper interpretation of the empirical results obtained from a financial high-frequency dataset, we conclude that significant long-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings against daily and weekly periodic patterns is established. Full article

(This article belongs to the Special Issue Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data)

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25 pages, 586 KiB

Open AccessArticle

On the Asymptotic Distribution of Ridge Regression Estimators Using Training and Test Samples

by Nandana Sengupta and Fallaw Sowell

Econometrics 2020, 8(4), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics8040039 - 01 Oct 2020

Cited by 2 | Viewed by 4489

Abstract

The asymptotic distribution of the linear instrumental variables (IV) estimator with empirically selected ridge regression penalty is characterized. The regularization tuning parameter is selected by splitting the observed data into training and test samples and becomes an estimated parameter that jointly converges with the parameters of interest. The asymptotic distribution is a nonstandard mixture distribution. Monte Carlo simulations show the asymptotic distribution captures the characteristics of the sampling distributions and when this ridge estimator performs better than two-stage least squares. An empirical application on returns to education data is presented. Full article

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Econometrics, Volume 8, Issue 4 (December 2020) – 6 articles

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