nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒08‒23
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Multivariate decompositions and seasonal gender employment By Jing Tian; Jan P.A.M. Jacobs; Denise R. Osborn
  2. Estimating high-dimensional Markov-switching VARs By Kenwin Maung
  3. Modeling Macroeconomic Variations after Covid-19 By Serena Ng
  4. Fast Computation and Bandwidth Selection Algorithms for Smoothing Functional Time Series* By Bastian Schäfer; Yuanhua Feng
  5. Culling the herd of moments with penalized empirical likelihood By Jinyuan Chang; Zhentao Shi; Jia Zhang

  1. By: Jing Tian; Jan P.A.M. Jacobs; Denise R. Osborn
    Abstract: Multivariate analysis can help to focus on economic phenomena, including trend and cyclical movements. To allow for potential correlation with seasonality, the present paper studies a three component multivariate unobserved component model, focusing on the case of quarterly data and showing that economic restrictions, including common trends and common cycles, can ensure identification. Applied to seasonal aggregate gender employment in Australia, a bivariate male/female model with a common cycle is preferred to both univariate correlated component and bivariate uncorrelated component specifications. This model evidences distinct gender-based seasonal patterns with seasonality declining over time for females and increasing for males.
    Keywords: trend-cycle-seasonal decomposition, multivariate unobserved components models, correlated component models, identification, gender employment, Australia
    JEL: C22 E24 E32 E37 F01
    Date: 2021–08
  2. By: Kenwin Maung
    Abstract: Maximum likelihood estimation of large Markov-switching vector autoregressions (MS-VARs) can be challenging or infeasible due to parameter proliferation. To accommodate situations where dimensionality may be of comparable order to or exceeds the sample size, we adopt a sparse framework and propose two penalized maximum likelihood estimators with either the Lasso or the smoothly clipped absolute deviation (SCAD) penalty. We show that both estimators are estimation consistent, while the SCAD estimator also selects relevant parameters with probability approaching one. A modified EM-algorithm is developed for the case of Gaussian errors and simulations show that the algorithm exhibits desirable finite sample performance. In an application to short-horizon return predictability in the US, we estimate a 15 variable 2-state MS-VAR(1) and obtain the often reported counter-cyclicality in predictability. The variable selection property of our estimators helps to identify predictors that contribute strongly to predictability during economic contractions but are otherwise irrelevant in expansions. Furthermore, out-of-sample analyses indicate that large MS-VARs can significantly outperform "hard-to-beat" predictors like the historical average.
    Date: 2021–07
  3. By: Serena Ng
    Abstract: The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-covid forecasting models inadequate. It also creates a new problem for estimation of economic factors and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use covid indicators as controls to 'de-covid' the data prior to estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and covid uncertainty has receded. Dynamic responses of variables to shocks in a VAR similar in magnitude and shape to the ones identified before 2020 can be recovered by directly or indirectly modeling covid and treating it as exogenous. These responses to economic shocks are distinctly different from those to a covid shock, and distinguishing between the two types of shocks can be important in macroeconomic modeling post-covid.
    JEL: C18 E0 E32
    Date: 2021–07
  4. By: Bastian Schäfer (Paderborn University); Yuanhua Feng (Paderborn University)
    Abstract: This paper examines data-driven estimation of the mean surface in nonparamet- ric regression for huge functional time series. In this framework, we consider the use of the double conditional smoothing (DCS), an equivalent but much faster translation of the 2D-kernel regression. An even faster, but again equivalent func- tional DCS (FCDS) scheme and a boundary correction method for the DCS/FCDS is proposed. The asymptotically optimal bandwidths are obtained and selected by an IPI (iterative plug-in) algorithm. We show that the IPI algorithm works well in practice in a simulation study and apply the proposals to estimate the spot-volatility and trading volume surface in high-frequency nancial data under a functional representation. Our proposals also apply to large lattice spatial or spatial-temporal data from any research area.
    Keywords: Spatial nonparametric regression, boundary correction, functional double conditional smoothing, bandwidth selection, spot volatility surface
    JEL: C14 C51
    Date: 2021–08
  5. By: Jinyuan Chang; Zhentao Shi; Jia Zhang
    Abstract: Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly silent about moment selection. A large pool of valid moments can potentially improve estimation efficiency, whereas a few invalid ones may undermine consistency. This paper investigates the empirical likelihood estimation of these moment-defined models in high-dimensional settings. We propose a penalized empirical likelihood (PEL) estimation and show that it achieves the oracle property under which the invalid moments can be consistently detected. While the PEL estimator is asymptotically normally distributed, a projected PEL procedure can further eliminate its asymptotic bias and provide more accurate normal approximation to the finite sample distribution. Simulation exercises are carried out to demonstrate excellent numerical performance of these methods in estimation and inference.
    Date: 2021–08

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