nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒09‒26
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Center-outward Rank- and Sign-based VARMA Portmanteau Tests By Marc Hallin; Hang Liu
  2. Sectoral Uncertainty By Efrem Castelnuovo; Kerem Tuzcuoglu; Luis Uzeda
  3. Do Recessions Occur Concurrently Across Countries? A Multinomial Logistic Approach By Poon, Aubrey; Zhu, Dan
  4. Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data By Tobias Wand; Martin He{\ss}ler; Oliver Kamps
  5. Specification Choices in Quantile Regression for Empirical Macroeconomics By Andrea Carriero; Todd E. Clark; Massimiliano Marcellino

  1. By: Marc Hallin; Hang Liu
    Abstract: Revisiting the pseudo-Gaussian tests of Chitturi (1974), Hosking (1980), and Li and McLeod (1981) for VARMA models from a Le Cam perspective, we first provide a more precise and rigorous description of the asymptotic behavior of the multivariate portmanteau test statistic.Then, based on the concepts of center-outward ranks and signs recently developed in Hallin et al. (2021), we propose a class of multivariateportmanteau rank- and sign-based test statistics which, under the null hypothesis and under a broad family of innovation densities, can be approximated by an asymptotically chi-square variable. The asymptotic properties of these tests are derived; simulations demonstratetheir advantages over their classical pseudo-Gaussian counterpart.
    Keywords: Multivariate ranks and signs, Measure transportation, Distributionfreeness, Le Cam’s asymptotic theory, Multivariate time series
    Date: 2022–08
  2. By: Efrem Castelnuovo; Kerem Tuzcuoglu; Luis Uzeda
    Abstract: We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a large dataset of disaggregated industrial production series for the US economy. Our results indicate that common uncertainty and uncertainty linked to non-durable goods both recorded their pre-pandemic global peaks during the 1973-75 recession. In contrast, durable goods uncertainty recorded its pre-pandemic peak during the global financial crisis of 2008-09. Vector autoregression exercises identify unexpected changes in durable goods uncertainty as drivers of downturns that are both economically and statistically significant, while unexpected hikes in non-durable goods uncertainty are expansionary. Our findings suggest that: (i) uncertainty is heterogeneous at a sectoral level; and (ii) durable goods uncertainty may drive some business cycle effects typically attributed to aggregate uncertainty.
    Keywords: Business fluctuations and cycles; Econometric and statistical methods; Monetary policy and uncertainty
    JEL: E32 E44 C51 C55
    Date: 2022–09
  3. By: Poon, Aubrey (Örebro University School of Business); Zhu, Dan (Monash University)
    Abstract: We develop a novel multinomial logistic model to detect and forecast concurrent recessions across multi-countries. The key advantage of our proposed framework is that we can detect recessions across countries using the additional informational content from the cross-country panel feature of the data. Furthermore, in a simulation study, we show that our proposed model accurately captures the true underlying probabilities. Finally, we apply our proposed framework to a US and UK empirical application. In terms of recession forecastability, the multinomial logistic model with both countries’ interest rate spread and the weekly US NFCI as the set of exogenous predictors was the best performing model. For the counterfactual analysis, we found that a previous US recession will increase the probability of a recession occurring jointly in the US and the UK. However, a tightening of the US NFCI and a negative interest rate spread in both countries only increases the probability of a recession exclusively in the US and UK, respectively.
    Keywords: Recession prediction; multinomial logistic; cross-country; mixed frequency; Bayesian estimation
    JEL: C22 C25 E32 E37
    Date: 2022–09–07
  4. By: Tobias Wand; Martin He{\ss}ler; Oliver Kamps
    Abstract: Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies. Based on the finding that the dynamics of sector correlation matrices of the S&P 500 stock market can be described by a sequence of distinct states via a clustering algorithm, we try to identify the industrial sectors dominating the correlation structure of each state. For this purpose, we use a method from Explainable Artificial Intelligence (XAI) on daily S&P 500 stock market data from 1992 to 2012 to assign relevance scores to every feature of each data point. To compare the significance of the features for the entire data set we develop an aggregation procedure and apply a Bayesian change point analysis to identify the most significant sector correlations. We show that the correlation matrix of each state is dominated only by a few sector correlations. Especially the energy and IT sector are identified as key factors in determining the state of the economy. Additionally we show that a reduced surrogate model, using only the eight sector correlations with the highest XAI-relevance, can replicate 90% of the cluster assignments. In general our findings imply an additional dimension reduction of the dynamics of the financial market.
    Date: 2022–08
  5. By: Andrea Carriero; Todd E. Clark; Massimiliano Marcellino
    Abstract: Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks to macroeconomic indicators. In this paper we examine various choices in the specification of quantile regressions for macro applications, for example, choices related to how and to what extent to include shrinkage, and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, using for evaluation both quantile scores and quantile-weighted continuous ranked probability scores at a range of quantiles spanning from the left to right tail. We find that shrinkage is generally helpful to tail forecast accuracy, with gains that are particularly large for GDP applications featuring large sets of predictors and unemployment and inflation applications, and with gains that increase with the forecast horizon.
    Keywords: Quantile regression; tail forecasting; shrinkage; Bayesian methods; quantile scores
    JEL: C53 E17 E37 F47
    Date: 2022–08–31

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