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on Econometric Time Series |
| By: | Gianluca Cubadda (CEIS & DEF, University of Rome "Tor Vergata"); Francesco Giancaterini (CEIS, University of Rome "Tor Vergata"); Alain Hecq (Maastricht University); Joann Jasiak (York University, Canada) |
| Abstract: | This paper investigates the performance of routinely used optimization algorithms in application to the Generalized Covariance estimator (GCov) for univariate and multivariate mixed causal and noncausal models. The GCov is a semi-parametric estimator with an objective function based on nonlinear autocovariances to identify causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function are insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well in its application to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical study of a bivariate series of commodity prices. |
| Keywords: | Mixed causal and noncausal models, Generalized covariance estimator, Simulated Annealing, Optimization, Commodity prices |
| Date: | 2024–04–23 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:574&r=ets |
| By: | Paulo Parente; Richard J. Smith |
| Abstract: | This article generalizes and extends the kernel block bootstrap (KBB) method of Parente and Smith (2018, 2021) to provide a comprehensive treatment of its use for GMM estimation and inference in time-series models formulated in terms of moment conditions. KBB procedures that employ bootstrap distributions with generalised empirical likelihood implied probabilities as probability mass points are also considered. The first-order asymptotic validity of new KBB estimators and test statistics for over-identifying moments, additional moment constraints and parametric restrictions is established. Their empirical distributions may serve as practical alternative approximations to those of GMM estimators and statistics and to other bootstrap distributions in the extant literature. Simulation experiments reveal that critical values arising from the empirical distributions of some KBB test statistics are more accurate than those from standard first-order asymptotic theory. |
| Date: | 2024–04–25 |
| URL: | http://d.repec.org/n?u=RePEc:azt:cemmap:08/24&r=ets |
| By: | Helmut L\"utkepohl (Freie Universit\"at Berlin and DIW Berlin); Fei Shang (South China University of Technology and Yuexiu Capital Holdings Group); Luis Uzeda (Bank of Canada); Tomasz Wo\'zniak (University of Melbourne) |
| Abstract: | We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions under which the matrix containing structural parameters is partially or globally unique; (ii) a statistical procedure to assess the validity of the conditions mentioned above; and (iii) a shrinkage prior distribution for conditional variances centred on a hypothesis of homoskedasticity. Such a prior ensures that the evidence for identifying a structural shock comes only from the data and is not favoured by the prior. We illustrate our new methods using a U.S. fiscal structural model. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.11057&r=ets |
| By: | Abdulnasser Hatemi-J |
| Abstract: | Connectedness measures the degree at which a time-series variable spills over volatility to other variables compared to the rate that it is receiving. The idea is based on the percentage of variance decomposition from one variable to the others, which is estimated by making use of a VAR model. Diebold and Yilmaz (2012, 2014) suggested estimating this simple and useful measure of percentage risk spillover impact. Their method is symmetric by nature, however. The current paper offers an alternative asymmetric approach for measuring the volatility spillover direction, which is based on estimating the asymmetric variance decompositions introduced by Hatemi-J (2011, 2014). This approach accounts explicitly for the asymmetric property in the estimations, which accords better with reality. An application is provided to capture the potential asymmetric volatility spillover impacts between the three largest financial markets in the world. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.12997&r=ets |
| By: | Mustafa R. K{\i}l{\i}n\c{c}; Michael Massmann |
| Abstract: | In this paper, we analyse the influence of estimating a constant term on the bias of the conditional sum-of-squares (CSS) estimator in a stationary or non-stationary type-II ARFIMA ($p_1$, $d$, $p_2$) model. We derive expressions for the estimator's bias and show that the leading term can be easily removed by a simple modification of the CSS objective function. We call this new estimator the modified conditional sum-of-squares (MCSS) estimator. We show theoretically and by means of Monte Carlo simulations that its performance relative to that of the CSS estimator is markedly improved even for small sample sizes. Finally, we revisit three classical short datasets that have in the past been described by ARFIMA($p_1$, $d$, $p_2$) models with constant term, namely the post-second World War real GNP data, the extended Nelson-Plosser data, and the Nile data. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.12882&r=ets |