nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒11‒13
seven papers chosen by
Jaqueson K. Galimberti, Asian Development Bank

  1. MIDAS regression: a new horse in the race of filtering macroeconomic time series By Michal BenÄ ík
  2. Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity By Anastasiou, Andreas; Cribben, Ivor; Fryzlewicz, Piotr
  3. Realized Covariance Models with Time-varying Parameters and Spillover Effects By Bauwens, Luc; Otranto, Edoardo
  4. Estimation of market efficiency process within time-varying autoregressive models by extended Kalman filtering approach By Maria Kulikova; Gennady Kulikov
  5. Instability of Factor Strength in Asset Returns By Daniele Massacci
  6. Structural Vector Autoregressions and Higher Moments: Challenges and Solutions in Small Samples By Sascha A. Keweloh
  7. On changepoint detection in functional data using empirical energy distance By B. Cooper Boniece; Lajos Horv\'ath; Lorenzo Trapani

  1. By: Michal BenÄ ík (National Bank of Slovakia)
    Abstract: We propose a new method of dealing with the end point problem when filtering economic time series. The main idea is to replace filtered quarterly observations at the end of the sample with static forecasts from a MIDAS regression using higher frequency time series. This method is capable to improve stability of output gap estimates or other cyclical series, as we confirm by empirical analysis on selected CEE countries and the United States. We find that stability may still be violated due to structural breaks in business cycles, or by an excessive amount of short-term noise. While MIDAS regressions have the potential to improve output gap estimates compared to the HP filter approach, the country-specific circumstances play a considerable role and need to be considered.
    JEL: C22 E32
    Date: 2023–10
  2. By: Anastasiou, Andreas; Cribben, Ivor; Fryzlewicz, Piotr
    Abstract: Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID may also be applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from CRAN.
    Keywords: fMRI; dynamic functional connectivity; change-point analysis; networks; time varying connectivity
    JEL: C1
    Date: 2022–01–01
  3. By: Bauwens, Luc (Université catholique de Louvain, LIDAM/CORE, Belgium); Otranto, Edoardo (Universita di Messina)
    Abstract: A realized covariance model specifies a dynamic process for a conditional covariance matrix of daily asset returns as a function of past realized variances and covariances. We propose parsimonious parameterizations enabling a spillover effect in the conditional variance equations, and a specific nonlinear, time-varying, impact of the lagged realized covariance between each asset pair on the corresponding conditional covariance. We introduce these parameterizations in BEKK, DCC and HAR type scalar models. In an application relative to the components of the Dow Jones index, we find that the extended models improve the fit and the out-of-sample forecast performances of their less flexible scalar versions.
    Keywords: Realized volatility ; spillover effect ; attenuation effect ; time-varying parameters
    JEL: G11 G17 C32 C58
    Date: 2023–07–21
  4. By: Maria Kulikova; Gennady Kulikov
    Abstract: This paper explores a time-varying version of weak-form market efficiency that is a key component of the so-called Adaptive Market Hypothesis (AMH). One of the most common methodologies used for modeling and estimating a degree of market efficiency lies in an analysis of the serial autocorrelation in observed return series. Under the AMH, a time-varying market efficiency level is modeled by time-varying autoregressive (AR) process and traditionally estimated by the Kalman filter (KF). Being a linear estimator, the KF is hardly capable to track the hidden nonlinear dynamics that is an essential feature of the models under investigation. The contribution of this paper is threefold. We first provide a brief overview of time-varying AR models and estimation methods utilized for testing a weak-form market efficiency in econometrics literature. Secondly, we propose novel accurate estimation approach for recovering the hidden process of evolving market efficiency level by the extended Kalman filter (EKF). Thirdly, our empirical study concerns an examination of the Standard and Poor's 500 Composite stock index and the Dow Jones Industrial Average index. Monthly data covers the period from November 1927 to June 2020, which includes the U.S. Great Depression, the 2008-2009 global financial crisis and the first wave of recent COVID-19 recession. The results reveal that the U.S. market was affected during all these periods, but generally remained weak-form efficient since the mid of 1946 as detected by the estimator.
    Date: 2023–10
  5. By: Daniele Massacci (University of Naples Federico II, King’s Business School, and CSEF)
    Abstract: We study the problem of detecting structural instability of factor strength in asset pricing models for financial returns with observable factors. We allow for strong and weaker factors, in which the sum of squared betas grows at a rate equal to and slower than the number of test assets, respectively: this growth rate determines the strength of the corresponding factor. We propose LM and Wald statistics for the null hypothesis of stability and derive their asymptotic distribution when the break fraction is known, as well as when it is unknown and has to be estimated. We corroborate our theoretical results through a comprehensive series of Monte Carlo experiments. An extensive empirical analysis uncovers the dynamics of instability of factor strength in financial returns from equity portfolios.
    Keywords: Factor strength, structural break, hypothesis testing, stock portfolios.
    JEL: C12 C33 C58 G10 G12
    Date: 2023–10–13
  6. By: Sascha A. Keweloh
    Abstract: Generalized method of moments estimators based on higher-order moment conditions derived from independent shocks can be used to identify and estimate the simultaneous interaction in structural vector autoregressions. This study highlights two problems that arise when using these estimators in small samples. First, imprecise estimates of the asymptotically efficient weighting matrix and the asymptotic variance lead to volatile estimates and inaccurate inference. Second, many moment conditions lead to a small sample scaling bias towards innovations with a variance smaller than the normalizing unit variance assumption. To address the first problem, I propose utilizing the assumption of independent structural shocks to estimate the efficient weighting matrix and the variance of the estimator. For the second issue, I propose incorporating a continuously updated scaling term into the weighting matrix, eliminating the scaling bias. To demonstrate the effectiveness of these measures, I conducted a Monte Carlo simulation which shows a significant improvement in the performance of the estimator.
    Date: 2023–10
  7. By: B. Cooper Boniece; Lajos Horv\'ath; Lorenzo Trapani
    Abstract: We propose a novel family of test statistics to detect the presence of changepoints in a sequence of dependent, possibly multivariate, functional-valued observations. Our approach allows to test for a very general class of changepoints, including the "classical" case of changes in the mean, and even changes in the whole distribution. Our statistics are based on a generalisation of the empirical energy distance; we propose weighted functionals of the energy distance process, which are designed in order to enhance the ability to detect breaks occurring at sample endpoints. The limiting distribution of the maximally selected version of our statistics requires only the computation of the eigenvalues of the covariance function, thus being readily implementable in the most commonly employed packages, e.g. R. We show that, under the alternative, our statistics are able to detect changepoints occurring even very close to the beginning/end of the sample. In the presence of multiple changepoints, we propose a binary segmentation algorithm to estimate the number of breaks and the locations thereof. Simulations show that our procedures work very well in finite samples. We complement our theory with applications to financial and temperature data.
    Date: 2023–10

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