nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒02‒10
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis By Chainarong Amornbunchornvej; Elena Zheleva; Tanya Berger-Wolf
  2. Low Frequency Robust Cointegrated Regression in the Presence of a Near-Unity Regressor By Jungbin Hwang; Gonzalo Valdés
  3. Exchange Rate Pass through to Stock Prices: A Multi GARCH Approach By Ilu, Ahmad Ibraheem
  4. Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects By Paolo Gorgi; Siem Jan Koopman
  5. Finite-sample Corrected Inference for Two-step GMM in Time Series By Jungbin Hwang; Gonzalo Valdés
  6. Boosting Non-linear Predictabilityof Macroeconomic Time SeriesComplexity and benefit take-up: Empirical evidence from the Finnish homecare allowance By Heikki Kauppi; Timo Virtanen
  7. A Mixed Frequency Approach for Stock Returns and Valuation Ratios. By Theologos Dergiades; Costas Milas; Theodore Panagiotidis
  8. Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions By Karsten Schweikert

  1. By: Chainarong Amornbunchornvej; Elena Zheleva; Tanya Berger-Wolf
    Abstract: Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variable-lag Granger causality and Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring both variable-lag Granger causality and Transfer Entropy relations. We demonstrate our approach on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approach can be applied in any domain of time series analysis. The software of this work is available in the R package: VLTimeSeriesCausality.
    Date: 2020–02
  2. By: Jungbin Hwang (University of Connecticut); Gonzalo Valdés (Universidad de Tarapacá)
    Abstract: This paper develops a robust t and F inferences on a triangular cointegrated system when one may not be sure the economic variables are exact unit root processes. We show that the low frequency transformed augmented (TA) OLS method possesses an asymptotic bias term in the limiting distribution, and corresponding t and F inferences in Hwang and Sun (2017) are asymptotically invalid. As a result, the size of the cointegration vector can be extremely large for even very small deviations from the unit root regressors. We develop a method to correct the asymptotic bias of the TA-OLS test statistics for the cointegration vector. Our modi ed statistics not only adjusts the locational bias but also reects the estimation uncertainty of the long-run endogenity parameter in the bias correction term and has asymptotic t and F limits. Based on the modi ed TA-OLS test statistics, the paper provides a simple Bonferroni method to test for the cointegration parameter.
    Keywords: Cointegration, Local to Unity, t and F tests, Alternative Asymptotics, Low Fre-quency Econometrics, Transformed and Augmented OLS
    JEL: C12 C13 C32
    Date: 2020–01
  3. By: Ilu, Ahmad Ibraheem
    Abstract: This paper analytically examines the impact of exchange rate volatility on stock prices in Nigeria via both symmetric and asymmetric GARCH models. At the onset the descriptive statistics reveals that both series are non-normally distributed as indicated by the Jacque-Bera statistic, also the standard deviation implied that the stock price series is more volatile than the exchange rate. Furthermore both series are reported to be negatively skewed also reference to the kurtosis statistics presented it is observed that both series are leptokurtic distribution. Further the result obtained from the estimated model GARCH models reveals that the PGARCH gives the better fit of the stock prices volatility model given its minimum AIC value. In the symmetric models {GARCH (1, 1) and GARCH-in-Mean} the shocks on stock returns volatility are found to be mean reverting whilst in the asymmetric GARCH models {TGARCH, EGARCH and PGARCH} only EGARCH was found to be non-mean reverting. Further, the asymmetric term in all the 3 models indicates that bad news exerts more shocks on the stock returns volatility than good news of the same magnitude. The post estimation diagnostic test of ARCH effect demonstrate that all the models completely captured the ARCH effect. Immensely the findings of this study shall be of utmost relevance to investors, stock brokers, members of the academia, regulators and monetary authorities.
    Keywords: Exchange rate, Stock Prices, All Share Index (ASI), TGARCH, EGARCH and PGARCH
    JEL: E0 E4 E44 F31 G0
    Date: 2020–02–01
  4. By: Paolo Gorgi (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam)
    Abstract: We consider a general class of observation-driven models with exogenous regressors for double bounded data that are based on the beta distribution. We obtain a stationary and ergodic beta observation-driven process subject to a contraction condition on the stochastic dynamic model equation. We derive conditions for strong consistency and asymptotic normality of the maximum likelihood estimator. The general results are used to study the properties of a beta autoregressive process with threshold effects and to establish the asymptotic properties of the maximum likelihood estimator. We employ the threshold autoregressive model with leverage effects to analyze realized correlations for several sets of stock returns. We find that the impact of past values of realized correlation on future values is at least 10% higher when stock returns are negative rather than positive. This finding supports the conjecture that correlation between stock returns tends to be higher when stock prices are falling, and lower when there is a surge in stock prices. Finally, we conduct an out-of-sample study that shows that our model with leverage effects can enhance the accuracy of point and density forecasts of realized correlations.
    Keywords: Double bounded time series, financial econometrics, leverage effects, observation- driven models, realized correlation
    JEL: C32 C52 C58
    Date: 2020–01–27
  5. By: Jungbin Hwang (University of Connecticut); Gonzalo Valdés (Universidad de Tarapacá)
    Abstract: This paper develops a nite-sample corrected and robust inference for e¢ cient two-step generalized method of moments (GMM). One of the main challenges in e¢ cient GMM is that we do not observe the moment process and have to use the estimated moment process to construct a GMM weighting matrix. We use a non-parametric long run variance estimator as the optimal GMM weighting matrix. To capture the estimation uncertainty embodied in the weight matrix, we extend the nite-sample corrected formula of Windmeijer (2005) to a heteroskedasticity autocorrelated robust (HAR) inference in time series setting. Using xed-smoothing asymptotics, we show that our new test statistics lead to standard asymptotic F or t critical values and improve the nite sample performance of existing HAR robust GMM tests.
    Keywords: Generalized Method of Moments, Heteroskedasticity Autocorrelated Robust, Finite-sample Correction, Fixed-smoothing Asymptotics, t and F tests.
    JEL: C12 C13 C32
    Date: 2020–01
  6. By: Heikki Kauppi (University of Turku); Timo Virtanen (University of Turku)
    Abstract: We apply the boosting estimation method to investigate to what ex-tent and at what horizons macroeconomic time series have nonlinearpredictability coming from their own history. Our results indicate thatthe U.S. macroeconomic time series have more exploitable nonlinearpredictability than previous studies have found. On average, the mostfavorable out-of-sample performance is obtained by a two-stage proce-dure, where a conventional linear prediction model is fine-tuned by theboosting technique.
    Keywords: boosting, forecasting, linear autoregression, mean squarederror, non-linearity
    JEL: C22 C53 E27 E37 E47
    Date: 2018–12
  7. By: Theologos Dergiades (Department of International & European Studies, University of Macedonia); Costas Milas (Liverpool University); Theodore Panagiotidis (Department of Economics, University of Macedonia)
    Abstract: We employ a Mixed-Frequency VAR to study the effect of four valuation ratios (the price-dividend ratio, the price-earnings ratio, the Cyclically Adjusted Price Earnings Ratio and the Total Return Cyclically Adjusted Price Earnings Ratio) on the US stock market. We quantify the interaction between high and low frequency data. We show that all valuation ratios (observed at a monthly frequency) significantly affect stock market returns (observed at a daily frequency) at both long and short horizons.
    Keywords: Stock Index Returns; Valuation Ratios; MF-VAR; Impulse Response Analysis.
    JEL: G1 C12 C13
    Date: 2019–11
  8. By: Karsten Schweikert
    Abstract: In this paper, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well-known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso if it is tuned correctly and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation and the tuning parameter satisfies some further restrictions. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long-run US money demand function demonstrates the practical importance of this methodology.
    Date: 2020–01

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