nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒10‒04
nine papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Gaussian and Student's $t$ mixture vector autoregressive model By Savi Virolainen
  2. Smoothing long memory time series using R By Yuanhua Feng; André Uhde; Sebastian Letmathe
  3. Four Australian Banks and the Multivariate Time-Varying Smooth Transition Correlation GARCH model By Anthony D. Hall; Annastiina Silvennoinen; Timo Teräsvirta
  4. Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach By Ulrich Hounyo; Kajal Lahiri
  5. Macroeconomic forecasting with LSTM and mixed frequency time series data By Sarun Kamolthip
  7. Forecasting Electricity Prices with Expert, Linear and Non-Linear Models By Anna Gloria Billé; Angelica Gianfreda; Filippo Del Grosso; Francesco Ravazzolo
  8. Economic Growth and the Rate of Profit in Colombia 1967-2019: A VAR Time-Series Analysis By Duque Garcia, Carlos Alberto
  9. Stock index futures trading impact on spot price volatility. The CSI 300 studied with a TGARCH model By Marcel Ausloos; Yining Zhang; Gurjeet Dhesi

  1. By: Savi Virolainen
    Abstract: A new mixture vector autoressive model based on Gaussian and Student's $t$ distributions is introduced. The G-StMVAR model incorporates conditionally homoskedastic linear Gaussian vector autoregressions and conditionally heteroskedastic linear Student's $t$ vector autoregressions as its mixture components, and mixing weights that, for a $p$th order model, depend on the full distribution of the preceding $p$ observations. Also a structural version of the model with time-varying B-matrix and statistically identified shocks is proposed. We derive the stationary distribution of $p+1$ consecutive observations and show that the process is ergodic. It is also shown that the maximum likelihood estimator is strongly consistent, and thereby has the conventional limiting distribution under conventional high-level conditions.
    Date: 2021–09
  2. By: Yuanhua Feng (Paderborn University); André Uhde (University of Konstanz); Sebastian Letmathe (Paderborn University)
    Abstract: The paper at hand provides a detailed description of the smootslm R-package, which is an extension of the already published smoots package, enabling the data-driven local-polynomial smoothing of time series with long-memory. In this regard a sim- ple data-driven algorithm is proposed based on the well-known iterative plug in algorithm for SEMIFAR (semiparametric fractional autoregressive) models. Two new functions for data-driven estimation of the trend and its derivatives under the presence of long-memory are introduced. smootslm is applied to various environ- mental and financial time series with long memory, e.g. mean monthly Northern Hemisphere changes, daily observations of the air quality index of London (Britain) and quarterly G7-GDP. Moreover, smootslm is applied to model daily trading vol- ume of the S&P500. It is worth mentioning that this package can be applied to any suitable time series with long memory.
    Keywords: long memory, data-driven smoothing, SEMIFAR, estimation of derivatives
    JEL: C14 C51
    Date: 2021–09
  3. By: Anthony D. Hall; Annastiina Silvennoinen (NCER, Queensland University of Technology); Timo Teräsvirta (Aarhus University, CREATES, C.A.S.E, Humboldt-Universität zu Berlin)
    Abstract: This paper looks at changes in the correlations of daily returns between the four major banks in Australia. Revelations from the analysis are of importance to investors, but also to government involvement, due to the large proportion of the highly concentrated financial sector relying on the stability of the Big Four. For this purpose, a methodology for building Multivariate Time-Varying STCC-GARCH models is developed. The novel contributions in this area are the specification tests related to the correlation component, the extension of the general model to allow for additional correlation regimes, and a detailed exposition of the systematic, improved modelling cycle required for such nonlinear models. There is an R-package that includes the steps in the modelling cycle. Simulations evidence the robustness of the recommended model building approach. The empirical analysis reveals an increase in correlations of the Australia's four largest banks that coincides with the stagnation of the home loan market, technology changes, the mining boom, and Basel II alignment, increasing the exposure of the Australian financial sector to shocks.
    Keywords: Unconditional correlation, modelling volatility, modelling correlations, multivariate autoregressive conditional heteroskedasticity
    JEL: C32 C52 C58
    Date: 2021–09–28
  4. By: Ulrich Hounyo (University at Albany and CREATES); Kajal Lahiri (University at Albany)
    Abstract: This paper considers bootstrap inference in model averaging for predictive regressions. We first consider two different types of bootstrap methods in predictive regressions: standard pairwise bootstrap and standard fixed-design residual-based bootstrap. We show that these procedures are not valid in the context of model averaging. These common bootstrap approaches induce a bias-related term in the bootstrap variance of averaging estimators. We then propose and justify a fixed-design residual-based bootstrap resampling approach for model averaging. In a local asymptotic framework, we show the validity of the bootstrap in estimating the variance of a combined forecast and the asymptotic covariance matrix of a combined parameter vector with fixed weights. Our proposed method preserves non-parametrically the cross-sectional dependence between different models and the time series dependence in the errors simultaneously. The finite sample performance of these methods are assessed via Monte Carlo simulations. We illustrate our approach using an empirical study of the Taylor rule equation with 24 alternative specifications.
    Keywords: Bootstrap, Local asymptotic theory, Model average estimators, Wild bootstrap, Variance of consensus forecast
    JEL: C33 C53 C80
    Date: 2021–09–28
  5. By: Sarun Kamolthip
    Abstract: This paper demonstrates the potentials of the long short-term memory (LSTM) when applyingwith macroeconomic time series data sampled at different frequencies. We first present how theconventional LSTM model can be adapted to the time series observed at mixed frequencies when thesame mismatch ratio is applied for all pairs of low-frequency output and higher-frequency variable. Togeneralize the LSTM to the case of multiple mismatch ratios, we adopt the unrestricted Mixed DAtaSampling (U-MIDAS) scheme (Foroni et al., 2015) into the LSTM architecture. We assess via bothMonte Carlo simulations and empirical application the out-of-sample predictive performance. Ourproposed models outperform the restricted MIDAS model even in a set up favorable to the MIDASestimator. For real world application, we study forecasting a quarterly growth rate of Thai realGDP using a vast array of macroeconomic indicators both quarterly and monthly. Our LSTM withU-MIDAS scheme easily beats the simple benchmark AR(1) model at all horizons, but outperformsthe strong benchmark univariate LSTM only at one and six months ahead. Nonetheless, we find thatour proposed model could be very helpful in the period of large economic downturns for short-termforecast. Simulation and empirical results seem to support the use of our proposed LSTM withU-MIDAS scheme to nowcasting application.
    Date: 2021–09
  6. By: Mohamed CHIKHI; Claude DIEBOLT
    Abstract: In this paper, we consider the daily Xtrackers CAC 40 UCITS from 2009 to 2020 for the analysis as it is supposed to capture more information compared to other French stock markets. After application of different statistical tests including BDS test, Hinich bispectrum test, Tsay test for linearity, long memory test and automatic serial correlation tests, we try to test the weak form efficiency of French ETF market through a logistic smooth transition AR model with nonlinear asymmetric logistic smooth transition GARCH errors using semiparametric maximum likelihood where the innovation distribution is replaced by a nonparametric estimate based on the kernel density function. After analyzing the forecasting results, we show that the price fluctuations appear as the result of transitory shocks and the predictions provided by the LSTAR-ANSTGARCH model are better than those of other models for some time horizons. The predictions from this model are also better than those of the random walk model; accordingly, the XCAC 40 price is not weak form of efficient market for the entire period because its successive return are nonlinearly dependent and doesn't generate randomly.
    Keywords: LSTAR model, ANLSTGARCH model, semiparametric maximum likelihood, nonlinearity, informational shocks, kernel, bandwidth, market efficiency.
    JEL: C14 C12 C22 C58 G14
    Date: 2021
  7. By: Anna Gloria Billé (Department of Statistical Sciences, University of Padua, Italy); Angelica Gianfreda (Department of Economics, University of Modena and Reggio Emilia, Italy; Energy Markets Group, London Business School, UK); Filippo Del Grosso (Faculty of Economics and Management, Free University of Bozen, Italy); Francesco Ravazzolo (Faculty of Economics and Management, Free University of Bozen, Italy; BI Norwegian Business School; Rimini Centre for Economic Analysis)
    Abstract: This paper provides an iterative model selection for forecasting day–ahead hourly electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources (RES), fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA–GARCH models hence including parsimonious autoregressive specifications (known as expert-type models). Results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass and waste, weighted imports and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting. Considering the northern Italian prices and using the Model Confidence Set, predictions indicate a strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and RES improves the forecast accuracy of electricity prices more than predictions freely available to researchers.
    Keywords: Demand, Wind, Solar, Biomass, Waste, Fossil Fuels (coal, natural gas, CO2), Weighted Inflows, Commercial and Public Forecasts
    JEL: C13 C22 C53 Q47
    Date: 2021–09
  8. By: Duque Garcia, Carlos Alberto
    Abstract: In recent decades there has been a growing literature dealing with the empirical estimation of the rate of profit and other Marxian variables in several countries. Nonetheless, there has been a paucity of econometric research about the impact of those Marxian variables on the growth rate in developing countries. This paper seeks to evaluate the rate of profit and the rate of accumulation as determinants of the growth rate in Colombia during 1967-2019, using a VAR model. We find that both variables are statistically significant and, in concordance with Marxian theory predictions, affect positively the growth rate. We also identify direct impacts of growth rate over the profit rate and the accumulation rate as well as an inverse relationship between these last variables.
    Keywords: Marxian political economy; rate of profit; time-series analysis; Colombia
    JEL: B51 C32 O54
    Date: 2021–09–23
  9. By: Marcel Ausloos; Yining Zhang; Gurjeet Dhesi
    Abstract: A TGARCH modeling is argued to be the optimal basis for investigating the impact of index futures trading on spot price variability. We discuss the CSI-300 index (China-Shanghai-Shenzhen-300-Stock Index) as a test case. The results prove that the introduction of CSI-300 index futures (CSI-300-IF) trading significantly reduces the volatility in the corresponding spot market. It is also found that there is a stationary equilibrium relationship between the CSI-300 spot and CCSI-300-IF markets. A bidirectional Granger causality is also detected. ''Finally'', it is deduced that spot prices are predicted with greater accuracy over a 3 or 4 lag day time span.
    Date: 2021–08

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