nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒03‒04
eight papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure By Alain Hecq; Luca Margaritella; Stephan Smeekes
  2. Testing for Shifts in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances By Badi Baltagi; Chihwa Kao; Long Liu
  3. Robust Nearly-Efficient Estimation of Large Panels with Factor Structures By Marco Avarucci; Paolo Zaffaroni
  4. Testing Nonlinearity through a Logistic Smooth Transition AR Model with Logistic Smooth Transition GARCH Errors. By Mohamed Chikhi; Claude Diebolt
  5. Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings By Anne Opschoor; André Lucas; Istvan Barra; Dick van Dijk
  6. Financial series prediction using Attention LSTM By Sangyeon Kim; Myungjoo Kang
  7. The Long Run Stability of Money Demand in the Proposed West African Monetary Union By Asongu, Simplice; Folarin, Oludele; Biekpe, Nicholas
  8. Estimation of Dynamic Panel Threshold Model using Stata By Myung Hwan Seo; Sueyoul Kim; Young-Joo Kim

  1. By: Alain Hecq; Luca Margaritella; Stephan Smeekes
    Abstract: In this paper we develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test which retains the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out the effects of the variables not of interest. We conduct an extensive set of Monte-Carlo simulations to compare different ways to set up the test procedure and choose the tuning parameter. The test performs well under different data generating processes, even when the underlying model is not very sparse. Additionally, we investigate two empirical applications: the money-income causality relation using a large macroeconomic dataset and networks of realized volatilities of a set of 49 stocks. In both applications we find evidences that the causal relationship becomes much clearer if a high-dimensional VAR is considered compared to a standard low-dimensional one.
    Date: 2019–02
  2. By: Badi Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Chihwa Kao (Department of Economics, University of Connecticut); Long Liu (Department of Economics, College of Business, University of Texas at San Antonio)
    Abstract: This paper studies testing of shifts in a time trend panel data model with serially correlated error component disturbances, without any prior knowledge of whether the error term is sta- tionary or nonstationary. This is done in case the shift is known as well as unknown. Following Vogelsang (1997) in the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator derived in Baltagi, et al. (2014). The proposed test has a Chi-square limiting distribution and is valid for both J(O) and J(l) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations.
    Keywords: Non-Stationary Panels, Time Trends, Serial Correlation, Wald Type Tests
    JEL: C23 C3
    Date: 2019–02
  3. By: Marco Avarucci; Paolo Zaffaroni
    Abstract: This paper studies estimation of linear panel regression models with heterogeneous coefficients, when both the regressors and the residual contain a possibly common, latent, factor structure. Our theory is (nearly) efficient, because based on the GLS principle, and also robust to the specification of such factor structure because it does not require any information on the number of factors nor estimation of the factor structure itself. We first show how the unfeasible GLS estimator not only affords an efficiency improvement but, more importantly, provides a bias-adjusted estimator with the conventional limiting distribution, for situations where the OLS is affected by a first-order bias. The technical challenge resolved in the paper is to show how these properties are preserved for a class of feasible GLS estimators in a double-asymptotics setting. Our theory is illustrated by means of Monte Carlo exercises and, then, with an empirical application using individual asset returns and firms' characteristics data.
    Date: 2019–02
  4. By: Mohamed Chikhi; Claude Diebolt
    Abstract: This paper analyzes the cyclical behavior of CAC 40 by testing the existence of nonlinearity through a logistic smooth transition AR model with logistic smooth transition GARCH errors. We study the daily returns of CAC 40 from 1990 to 2018. We estimate several models using nonparametric maximum likelihood, where the innovation distribution is replaced by a nonparametric estimate for the density function. We find that the rate of transition and the threshold value in both the conditional mean and conditional variance are highly significant. The forecasting results show that the informational shocks have transitory effects on returns and volatility and confirm nonlinearity.
    Keywords: LSTAR model, LSTGARCH model, nonparametric maximum likelihood, nonlinearity, informational shocks, time series analysis.
    JEL: C14 C22 C58 G17
    Date: 2019
  5. By: Anne Opschoor; André Lucas; Istvan Barra; Dick van Dijk
  6. By: Sangyeon Kim; Myungjoo Kang
    Abstract: Financial time series prediction, especially with machine learning techniques, is an extensive field of study. In recent times, deep learning methods (especially time series analysis) have performed outstandingly for various industrial problems, with better prediction than machine learning methods. Moreover, many researchers have used deep learning methods to predict financial time series with various models in recent years. In this paper, we will compare various deep learning models, such as multilayer perceptron (MLP), one-dimensional convolutional neural networks (1D CNN), stacked long short-term memory (stacked LSTM), attention networks, and weighted attention networks for financial time series prediction. In particular, attention LSTM is not only used for prediction, but also for visualizing intermediate outputs to analyze the reason of prediction; therefore, we will show an example for understanding the model prediction intuitively with attention vectors. In addition, we focus on time and factors, which lead to an easy understanding of why certain trends are predicted when accessing a given time series table. We also modify the loss functions of the attention models with weighted categorical cross entropy; our proposed model produces a 0.76 hit ratio, which is superior to those of other methods for predicting the trends of the KOSPI 200.
    Date: 2019–02
  7. By: Asongu, Simplice; Folarin, Oludele; Biekpe, Nicholas
    Abstract: This study examines the stability of money demand in the proposed West African Monetary Union (WAMU). The study uses annual data for the period 1981 to 2015 from thirteen of the fifteen countries making-up the Economic Community of West African States (ECOWAS). A standard money demand function is designed and estimated using a bounds testing approach to co-integration and error-correction modeling. The findings show divergence across ECOWAS member states in the stability of money demand. This divergence is informed by differences in cointegration, stability, short run and long term determinants, and error correction in event of a shock.
    Keywords: Stable; demand for money; bounds test
    JEL: C22 E41
    Date: 2018–01
  8. By: Myung Hwan Seo; Sueyoul Kim; Young-Joo Kim
    Abstract: We develop a Stata command xthenreg to implement the first-differenced GMM estimation of the dynamic panel threshold model, which Seo and Shin (2016, Journal of Econometrics 195: 169-186) have proposed. Furthermore, We derive the asymptotic variance formula for a kink constrained GMM estimator of the dynamic threshold model and include an estimation algorithm. We also propose a fast bootstrap algorithm to implement the bootstrap for the linearity test. The use of the command is illustrated through a Monte Carlo simulation and an economic application.
    Date: 2019–02

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