nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒03‒13
seven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. High-Dimensional Causality for Climatic Attribution By Marina Friedrich; Luca Margaritella; Stephan Smeekes
  2. Sparse High-Dimensional Vector Autoregressive Bootstrap By Robert Adamek; Stephan Smeekes; Ines Wilms
  3. Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates By Christis Katsouris
  4. Cauchy Robust Principal Component Analysis with Applications to High-Dimensional Data Sets By Aisha Fayomi; Yannis Pantazis; Michail Tsagris; Andrew Wood
  5. On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market By Saker Sabkha; Christian de Peretti
  6. Instrument Strength in IV Estimation and Inference: A Guide to Theory and Practice By Michael Keane; Timothy Neal
  7. A two sample size estimator for large data sets By O’Connell, Martin; Smith, Howard; Thomassen, Øyvind

  1. By: Marina Friedrich; Luca Margaritella; Stephan Smeekes
    Abstract: In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global temperatures. By allowing for high dimensionality in the model we can enrich the information set with all relevant natural and anthropogenic forcing variables to obtain reliable causal relations. These variables have mostly been investigated in an aggregated form or in separate models in the previous literature. Additionally, our framework allows to ignore the order of integration of the variables and to directly estimate the VAR in levels, thus avoiding accumulating biases coming from unit-root and cointegration tests. This is of particular appeal for climate time series which are well known to contain stochastic trends as well as yielding long memory. We are thus able to display the causal networks linking radiative forcings to global temperatures but also to causally connect radiative forcings among themselves, therefore allowing for a careful reconstruction of a timeline of causal effects among forcings. The robustness of our proposed procedure makes it an important tool for policy evaluation in tackling global climate change.
    Date: 2023–02
  2. By: Robert Adamek; Stephan Smeekes; Ines Wilms
    Abstract: We introduce a high-dimensional multiplier bootstrap for time series data based capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest.
    Date: 2023–02
  3. By: Christis Katsouris
    Abstract: We propose an econometric environment for structural break detection in nonstationary quantile predictive regressions. We establish the limit distributions for a class of Wald and fluctuation type statistics based on both the ordinary least squares estimator and the endogenous instrumental regression estimator proposed by Phillips and Magdalinos (2009a, Econometric Inference in the Vicinity of Unity. Working paper, Singapore Management University). Although the asymptotic distribution of these test statistics appears to depend on the chosen estimator, the IVX based tests are shown to be asymptotically nuisance parameter-free regardless of the degree of persistence and consistent under local alternatives. The finite-sample performance of both tests is evaluated via simulation experiments. An empirical application to house pricing index returns demonstrates the practicality of the proposed break tests for regression quantiles of nonstationary time series data.
    Date: 2023–02
  4. By: Aisha Fayomi; Yannis Pantazis; Michail Tsagris; Andrew Wood
    Abstract: In this paper, we propose a modified formulation of the principal components analysis, based on the use of a multivariate Cauchy likelihood instead of the Gaussian likelihood, which has the effect of robustifying the principal components. We present an algorithm to compute these robustified principal components. We additionally derive the relevant influence function of the first component and examine its theoretical properties.
    Keywords: Principal component analysis, robust, Cauchy log-likelihood, high-dimensional data
    JEL: C13
    Date: 2023–02–08
  5. By: Saker Sabkha (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Christian de Peretti (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)
    Abstract: The study of an efficient financial assets' modeling method is still an open hot issue especially during recent crises. Using credit risk data from 33 worldwide countries, this paper investigates the performance of 9 Dynamic Conditional Correlation models taking into account different properties of financial markets (long memory behavior, asymmetry and/or leverage effects...). This comparative study is based on the results of several multivariate diagnostic tests. Findings show that no model outperforms the others in all situations, though, the straightforward DCC-GARCH model seems to provide the most relevant estimator parameters. Yet, the innovations distributions assumption significantly impacts the statistical fit of the model. Our work is useful for financial markets' participants so as to making decision in terms of arbitrage, hedging or speculation. JEL Classification G11, G12, F02, C58
    Keywords: DCC-class models, Multivariate diagnostic tests, Time-varying correlation, Sovereign credit market
    Date: 2022–01–11
  6. By: Michael Keane (School of Economics); Timothy Neal (UNSW School of Economics)
    Abstract: 2SLS has poor properties if instruments are exogenous but weak. But how strong must instruments be for 2SLS estimates and test statistics to exhibit acceptable properties? A common standard is a first-stage F ≥ 10. This is adequate to ensure two- tailed t-tests have small size distortions. But other problems persist: In particular, we show 2SLS standard errors tend to be artificially small in samples where the estimate is most contaminated by the OLS bias. Hence, if the bias is positive, the t-test has little power to detect true negative effects, and inflated power to find positive effects. This phenomenon, which we call a “power asymmetry, †persists even if first-stage F is in the thousands. Robust tests like Anderson-Rubin perform better, and should be used in lieu of the t-test even with strong instruments. We also show how 2SLS test statistics typically suffer from very low power when first-stage F is near 10, leading us to suggest a higher standard of instrument strength in empirical practice.
    Keywords: Instrumental variables, weak instruments, 2SLS, endogeneity, F-test, size distortion, Anderson-Rubin test, likelihood ratio test, LIML, GMM, Fuller, JIVE
    JEL: C12 C26 C36
    Date: 2022–11
  7. By: O’Connell, Martin (Dept. of Economics, University of Wisconsin-Madison); Smith, Howard (Dept. of Economics, Oxford University); Thomassen, Øyvind (Dept. of Business and Management Science, Norwegian School of Economics)
    Abstract: In GMM estimators moment conditions with additive error terms involve an observed component and a predicted component. If the predicted component is computationally costly to evaluate, it may not be feasible to estimate the model with all the available data. We propose an estimator that uses the full data set for the computationally cheap observed component, but a reduced sample size for the predicted component. We show consistency, asymptotic normality, and derive standard errors and a practical criterion for when our estimator is variance-reducing. We demonstrate the estimator’s properties on a range of models through Monte Carlo studies and an empirical application to alcohol demand.
    Keywords: GMM; estimation; micro data
    JEL: C20 C51 C55
    Date: 2023–02–17

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