nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒06‒19
twelve papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting By Gary Koop; Gary Koop; Stuart McIntyre; James Mitchell; Aubrey Poon; Ping Wu
  2. Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models By Yichi Zhang; Mihai Cucuringu; Alexander Y. Shestopaloff; Stefan Zohren
  3. Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks By Florian Huber; Gary Koop
  4. Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices By Emilija Dzuverovic; Matteo Barigozzi
  5. Detecting and dating possibly distinct structural breaks in the covariance structure of financial assets By Mugrabi, Farah Daniela
  6. Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models By Christis Katsouris
  7. A Multilevel Factor Model for Economic Activity with Observation Driven Dynamic Factors By Mariia Artemova; Francisco Blasques; Siem Jan Koopman
  8. Financial and Macroeconomic Data Through the Lens of a Nonlinear Dynamic Factor Model By Pablo Guerrón-Quintana; Alexey Khazanov; Molin Zhong
  9. Semiparametrically Optimal Cointegration Test By Bo Zhou
  10. Forecasting banknote circulation during the COVID-19 pandemic using structural time series models By Nikolaus Bartzsch; Marco Brandi; Lucas Devigne; Raymond de Pastor; Gianluca Maddaloni; Diana Posada Restrepo; Gabriele Sene
  11. Approximate Bayesian Computation for Partially Identified Models By Alvarez, Luis Antonio
  12. Predictive Power of Composite Socioeconomic Indices in Regression and Classification: Principal Components and Partial Least Squares By Stefanía D’Iorio; Liliana Forzani; Rodrigo García Arancibia; Ignacio Girela

  1. By: Gary Koop; Gary Koop; Stuart McIntyre; James Mitchell; Aubrey Poon; Ping Wu
    Abstract: Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and "short" data imply a "ragged edge" at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we develop methods to include a wide range of short data into a regional mixed-frequency VAR model. These short data include hitherto unexploited regional VAT turnover data. We address the problem of the ragged edge at both the beginning and end of our sample by estimating regional factors using different missing data algorithms that we then incorporate into our mixed-frequency VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published.
    Keywords: Regional data; Mixed-frequency data; Missing data; Nowcasting; Factors; Bayesian methods; Real-time data; Vector autoregressions
    JEL: C32 C53 E37
    Date: 2023–05–08
  2. By: Yichi Zhang; Mihai Cucuringu; Alexander Y. Shestopaloff; Stefan Zohren
    Abstract: In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series shifted in time relative to one another, and which can be leveraged for the purposes of control, forecasting or clustering. We develop a clustering-driven methodology for the robust detection of lead-lag relationships in lagged multi-factor models. Within our framework, the envisioned pipeline takes as input a set of time series, and creates an enlarged universe of extracted subsequence time series from each input time series, by using a sliding window approach. We then apply various clustering techniques (e.g, K-means++ and spectral clustering), employing a variety of pairwise similarity measures, including nonlinear ones. Once the clusters have been extracted, lead-lag estimates across clusters are aggregated to enhance the identification of the consistent relationships in the original universe. Since multivariate time series are ubiquitous in a wide range of domains, we demonstrate that our method is not only able to robustly detect lead-lag relationships in financial markets, but can also yield insightful results when applied to an environmental data set.
    Date: 2023–05
  3. By: Florian Huber; Gary Koop
    Abstract: The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichlet process mixture (DPM) to model the shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM since this would lead to computationally infeasible Bayesian inference in larger VARs and potentially a sensitivity to the way the variables are ordered in the VAR. Instead we develop a particular additive error structure inspired by Bayesian nonparametric treatments of random effects in panel data models. We show that this leads to a model which allows for computationally fast and order-invariant inference in large VARs with nonparametric shocks. Our empirical results with nonparametric VARs of various dimensions shows that nonparametric treatment of the VAR errors is particularly useful in periods such as the financial crisis and the pandemic.
    Date: 2023–05
  4. By: Emilija Dzuverovic; Matteo Barigozzi
    Abstract: We introduce a new HD DCC-HEAVY class of hierarchical-type factor models for conditional covariance matrices of high-dimensional returns, employing the corresponding realized measures built from higher-frequency data. The modelling approach features sophisticated asymmetric dynamics in covariances coupled with straightforward estimation and forecasting schemes, independent of the cross-sectional dimension of the assets under consideration. Empirical analyses suggest the HD DCC-HEAVY models have a better in-sample fit, and deliver statistically and economically significant out-of-sample gains relative to the standard benchmarks and existing hierarchical factor models. The results are robust under different market conditions.
    Date: 2023–05
  5. By: Mugrabi, Farah Daniela (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: This paper aims to identify and date contagion by accounting for possibly distinct structural breaks among the covariance structure of financial assets. We propose an efficient three-steps procedure that applies the Lagrange Multiplier test, in particular the SupLM statistic, among the DCC-GARCH model parameters. Monte Carlo experiments show that our procedure possess good power and accurately detects the location of the true breaking points. We explore contagion between the government bond and stock markets of advanced and emerging economies. Evidence of common shifts in the covariance structure coincides with the European Sovereign Debt Crisis, the Taper Tantrum originated in United States in mid-2013 and the Covid-19 pandemic.
    Keywords: Contagion, emerging markets, unknown structural breaks, Lagrange Multiplier test, DCC-GARCH model
    JEL: C32 C15 G15
    Date: 2023–03–01
  6. By: Christis Katsouris
    Abstract: This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than assets in a portfolio using quantile predictive regression models without assuming the presence of nonstationary regressors and; (ii) the construction of a novel variable selection algorithm, so-called, Feature Ordering by Centrality Exclusion (FOCE), which is based on an assumption-lean regression framework, has no tuning parameters and is proved to be consistent under general sparsity assumptions. We illustrate the usefulness of our proposed methodology with numerical studies of real and simulated datasets when modelling systemic risk in a network.
    Date: 2023–05
  7. By: Mariia Artemova (Vrije Universiteit Amsterdam); Francisco Blasques (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam)
    Abstract: We analyze the role of industrial and non-industrial production sectors in the US economy by adopting a novel multilevel factor model. The proposed model is suitable for high-dimensional panels of economic time series and allows for interdependence structures across multiple sectors. The estimation procedure is based on a multistep least squares method which is simple and fast in its implementation. By analyzing the shock propagation process throughout the network of interconnections, we corroborate some of the key findings about the role of industrial production in the US economy, quantify the importance of propagation effects and shed new light on dynamic sectoral linkages.
    Keywords: Dynamic factor model, Interconnectedness, Output growth.
    JEL: C22 C32 C38 C51
    Date: 2023–04–23
  8. By: Pablo Guerrón-Quintana; Alexey Khazanov; Molin Zhong
    Abstract: Through the lens of a nonlinear dynamic factor model, we study the role of exogenous shocks and internal propagation forces in driving the fluctuations of macroeconomic and financial data. The proposed model 1) allows for nonlinear dynamics in the state and measurement equations; 2) can generate asymmetric, state-dependent, and size-dependent responses of observables to shocks; and 3) can produce time-varying volatility and asymmetric tail risks in predictive distributions. We find evidence in favor of nonlinear dynamics in two important U.S. applications. The first uses interest rate data to extract a factor allowing for an effective lower bound and nonlinear dynamics. Our estimated factor coheres well with the historical narrative of monetary policy. We find that allowing for an effective lower bound constraint is crucial. The second recovers a credit cycle. The nonlinear component of the factor boosts credit growth in boom times while hinders its recovery post-crisis. Shocks in a credit crunch period are more amplified and persist for longer compared with shocks during a credit boom.
    Keywords: Interest rates; Effective lower bound; Credit cycle; Asymmetric dynamics; Predictive distributions; Tail risk
    JEL: E51 C51 E43
    Date: 2023–05–05
  9. By: Bo Zhou
    Abstract: This paper aims to address the issue of semiparametric efficiency for cointegration rank testing in finite-order vector autoregressive models, where the innovation distribution is considered an infinite-dimensional nuisance parameter. Our asymptotic analysis relies on Le Cam's theory of limit experiment, which in this context takes the form of Locally Asymptotically Brownian Functional (LABF). By leveraging the structural version of LABF, an Ornstein-Uhlenbeck experiment, we develop the asymptotic power envelopes of asymptotically invariant tests for both cases with and without a time trend. We propose feasible tests based on a nonparametrically estimated density and demonstrate that their power can achieve the semiparametric power envelopes, making them semiparametrically optimal. We validate the theoretical results through large-sample simulations and illustrate satisfactory size control and excellent power performance of our tests under small samples. In both cases with and without time trend, we show that a remarkable amount of additional power can be obtained from non-Gaussian distributions.
    Date: 2023–05
  10. By: Nikolaus Bartzsch (Deutsche Bundesbank); Marco Brandi (Banca d'Italia); Lucas Devigne (Banque de France); Raymond de Pastor (Banque de France); Gianluca Maddaloni (Banca d'Italia); Diana Posada Restrepo (Banco de España); Gabriele Sene (Banca d'Italia)
    Abstract: As part of the Eurosystem’s annual banknote production planning, the national central banks draw up forecasts estimating the volumes of national-issued banknotes in circulation for the three years ahead. As at the end of 2021, more than 80 per cent of euro banknotes in circulation (cumulated net issuance) had been issued by the national central banks of France, Germany, Italy and Spain (‘4 NCBs’). To date, the 4 NCBs have been using ARIMAX models to forecast the banknotes issued nationally in circulation by denomination (‘benchmark models’). This paper presents the structural time series models developed by the 4 NCBs as an additional forecasting tool. The forecast accuracy measures used in this study show that the structural time series models outperform the benchmark models currently in use at each of the 4 NCBs for most of the denominations. However, it should be borne in mind that the statistical informative value of this comparison is limited by the fact the projection period is only twelve months.
    Keywords: euro, demand for banknotes, forecast of banknotes in circulation, structural time series models, ARIMA models, intervention variables
    JEL: C22 E41 E47 E51
    Date: 2023–05
  11. By: Alvarez, Luis Antonio
    Abstract: Partial identification is a prominent feature of several economic models. Such prevalence has spurred a large literature on valid set estimation under partial identification from a frequentist viewpoint. From the Bayesian perspective, it is well known that, under partial identification, the asymptotic validity of Bayesian credible sets in conducting frequentist inference, which is ensured by several Bernstein von-Mises theorems available in the literature, breaks down. Existing solutions to this problem require either knowledge of the map between the distribution of the data and the identified set -- which is generally unavailable in more complex models --, or modifications to the methodology that difficult the Bayesian interpretability of the proposed solution. In this paper, I show how one can leverage Approximate Bayesian Computation, a Bayesian methodology designed for settings where evaluation of the model likelihood is unfeasible, to reestablish the asymptotic validity of Bayesian credible sets in conducting frequentist inference, whilst preserving the core interpretation of the Bayesian approach and dispensing with knowledge of the map between data and identified set. Specifically, I show in a simple, yet encompassing, setting how, by calibrating the main tuning parameter of the ABC methodology, one could hope to achieve asymptotic frequentist coverage. Based on my findings, I then propose a semiautomatic algorithm for selecting this parameter and constructing valid confidence sets. This is a work in progress. In future versions, I intend to present further theoretical results, Monte Carlo simulations and an empirical application on the Economics of Networks.
    Keywords: Approximate Bayesian Computation; Partial Identification; Tuning parameter selection
    JEL: C11
    Date: 2023–03–20
  12. By: Stefanía D’Iorio (Universidad Nacional de Entre Ríos); Liliana Forzani (Universidad Nacional del Litoral/ CONICET); Rodrigo García Arancibia (Universidad Nacional del Litoral/ CONICET); Ignacio Girela (Universidad Nacional de Córdoba/ CONICET)
    Abstract: Principal Components Analysis (PCA) and Partial Least Squares (PLS) have been used for the construction of socioeconomic status (SES) indices to use as a predictor of the well-being status in targeted programs. Generally, these indicators are constructed as a linear combination of the first component. Due to the characteristics of the socioeconomic data, different extensions of PCA and PLS for non-metric variables have been proposed for these applications. In this paper we compare the predictive performance of SES indices constructed using more than one component. Additionally, for the inclusion of non-metric variables, a variant of the normal mean coding is proposed that takes into account the multivariate nature of the variables, that we call multivariate normal mean coding (MNMC). Using simulations and real data, we found that PLS using MNMC as well as the classical dummy encoding method give the best predictive results with a more parsimonious SES index.
    Keywords: Dimension Reduction, Categorical Predictors, SES, Proxy Mean Test
    Date: 2023–05

This nep-ets issue is ©2023 by Jaqueson K. Galimberti. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.