nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒06‒28
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Transformed Regression-based Long-Horizon Predictability Tests By Demetrescu, Matei; Rodrigues, Paulo MM; Taylor, AM Robert
  2. Dynamic Factor Copula Models with Estimated Cluster Assignments By Dong Hwan Oh; Andrew J. Patton
  3. Which Output Gap Estimates Are Stable in Real Time and Why? By Alessandro Barbarino; Travis J. Berge; Han Chen; Andrea Stella
  4. A Note on the Accuracy of Variational Bayes in State Space Models: Inference and Prediction By David T. Frazier; Ruben Loaiza-Maya; Gael M. Martin
  5. Dynamic Asymmetric Causality Tests with an Application By Abdulnasser Hatemi-J
  6. A new measure to study erratic financial behaviors and time-varying dynamics of equity markets By Nick James; Max Menzies

  1. By: Demetrescu, Matei; Rodrigues, Paulo MM; Taylor, AM Robert
    Abstract: We propose new tests for long-horizon predictability based on IVX estimation (see Kostakis et al., 2015) of transformed regressions. These explicitly account for the over-lapping nature of the dependent variable which features in a long-horizon predictive regression arising from temporal aggregation. Because we use IVX estimation we can also incorporate the residual augmentation approach recently used in the context of short-horizon predictability testing by Demetrescu and Rodrigues (2020) to improve efficiency. Our proposed tests have a number of advantages for practical use. First, they are simple to compute making them more appealing for empirical work than, in particular, the Bonferroni-based methods developed in, among others, Valkanov (2003) and Hjalmarsson (2011), which require the computation of confidence intervals for the autoregressive parameter characterising the predictor. Second, unlike some of the available tests, they allow the practitioner to remain ambivalent as to whether the predictor is strongly or weakly persistent. Third, the tests are valid under considerably weaker assumptions on the innovations than extant long-horizon predictability tests. In particular, we allow for quite general forms of conditional and unconditional heteroskedasticity in the innovations, neither of which are tied to a parametric model. Fourth, our proposed tests can be easily implemented as either one or two-sided hypotheses tests, unlike the Bonferroni-based methods which require the computation of different confidence intervals for the autoregressive parameter depending on whether left or right tailed tests are to be conducted (see Hjalmarsson, 2011). Finally our approach is straightforwardly generalisable to a multi-predictor context. Monte Carlo analysis suggests that our preferred test displays improved finite properties compared to the leading tests available in the literature. We also report an empirical application of the methods we develop to investigate the potential predictive power of real exchange rates for predicting nominal exchange rates and inflation.
    Keywords: long-horizon predictive regression; IVX estimation; (un)conditional heteroskedasticity; unknown regressor persistence; endogeneity; residual augmentation
    Date: 2021–06–18
  2. By: Dong Hwan Oh; Andrew J. Patton
    Abstract: This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.
    Keywords: Correlation; Tail risk; Multivariate density forecast
    JEL: C32 C58 C38
    Date: 2021–04–30
  3. By: Alessandro Barbarino; Travis J. Berge; Han Chen; Andrea Stella
    Abstract: Output gaps that are estimated in real time can differ substantially from those estimated after the fact. We aim to understand the real-time instability of output gap estimates by comparing a suite of reduced-form models. We propose a new statistical decomposition and find that including a Okun’s law relationship improves real-time stability by alleviating the end-point problem. Models that include the unemployment rate also produce output gaps with relevant economic content. However, we find that no model of the output gap is clearly superior to the others along each metric we consider.
    Keywords: Output gap; Real-time data; Trend-cycle decomposition; Unobserved components model
    JEL: E24 E32 E52
    Date: 2020–12–18
  4. By: David T. Frazier; Ruben Loaiza-Maya; Gael M. Martin
    Abstract: Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a broad range of state space models. The results demonstrate that, in terms of accuracy on fixed parameters, there is a clear hierarchy in terms of the methods, with approaches that do not approximate the states yielding superior accuracy over methods that do. We also document numerically that the inferential discrepancies between the various methods often yield only small discrepancies in predictive accuracy over small out-of-sample evaluation periods. Nevertheless, in certain settings, these predictive discrepancies can become marked over longer out-of-sample periods. This finding indicates that the invariance of predictive results to inferential inaccuracy, which has been an oft-touted point made by practitioners seeking to justify the use of variational inference, is not ubiquitous and must be assessed on a case-by-case basis.
    Date: 2021–06
  5. By: Abdulnasser Hatemi-J
    Abstract: Testing for causation, defined as the preceding impact of the past values of one variable on the current value of another one when all other pertinent information is accounted for, is increasingly utilized in empirical research of the time-series data in different scientific disciplines. A relatively recent extension of this approach has been allowing for potential asymmetric impacts since it is harmonious with the way reality operates in many cases according to Hatemi-J (2012). The current paper maintains that it is also important to account for the potential change in the parameters when asymmetric causation tests are conducted, as there exists a number of reasons for changing the potential causal connection between variables across time. The current paper extends therefore the static asymmetric causality tests by making them dynamic via the usage of subsamples. An application is also provided consistent with measurable definitions of economic or financial bad as well as good news and their potential interaction across time.
    Date: 2021–06
  6. By: Nick James; Max Menzies
    Abstract: This paper introduces a new framework to quantify distance between finite sets with uncertainty present, where probability distributions determine the locations of individual elements. Combining this with a Bayesian change point detection algorithm, we produce a new measure of similarity between time series with respect to their structural breaks. Next, we apply this to financial data to study the erratic behavior profiles of 19 countries and 11 sectors over the past 20 years. Then, we take a closer examination of individual equities and their behavior surrounding market crises, times when change points are consistently observed. Combining new and existing methods, we study the dynamics of our collection of equities and highlight an increase in equity similarity in recent years, particularly during such crises. Finally, we show that our methodology may provide a new outlook on diversification and risk-reduction during times of extraordinary correlation between assets, where traditional portfolio optimization algorithms encounter difficulties.
    Date: 2021–06

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