nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒10‒05
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Aggregation of Seasonal Long-Memory Processes By del Barrio Castro, Tomás; Rachinger, Heiko
  2. Bayesian modelling of time-varying conditional heteroscedasticity By Sayar Karmakar; Arkaprava Roy
  3. Nowcasting the output gap By Tino Berger; James Morley; Benjamin Wong
  4. Time and frequency connectedness among oil shocks, electricity and clean energy markets By Muhammad Abubakr Naeem; Zhe Peng; Mouhammed Tahir Suleman; Rabindra Nepal; Syed Jawad Hussain Shahzad
  5. No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates By Andrea Carriero; Todd E. Clark; Massimiliano Marcellino
  6. Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield Spread By Sudiksha Joshi
  7. Household Expenditure In Africa: Evidence Of Mean Reversion By Yaya, OlaOluwa S; Olalude, Gbenga A; Olayinka, Hameed A; Jimoh, Toheeb A; Adebiyi, Aliu A
  8. Uncertainty and monetary policy during extreme events By Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano

  1. By: del Barrio Castro, Tomás; Rachinger, Heiko
    Abstract: To understand the impact of temporal aggregation on the properties of a seasonal long-memory process, the effects of skip and cumulation sampling on both stationary and nonstationary processes with poles at several potential frequencies are analyzed. By allowing for several poles in the disaggregated process, their interaction in the aggregated series is investigated. Further, by definning the process according to the truncated Type II definition, the proposed approach encompasses both stationary and nonstationary processes without requiring prior knowledge of the case. The frequencies in the aggregated series to which the poles in the disaggregated series are mapped can be directly deduced. Specifically, unlike cumulation sampling, skip sampling can impact on non-seasonal memory properties. Moreover, with cumulation sampling, seasonal long-memory can vanish in some cases. Using simulations, the mapping of the frequencies implied by temporal aggregation is illustrated and the estimation of the memory at the different frequencies is analyzed
    Keywords: Aggregation, cumulation sampling, skip sampling, seasonal long memory.
    JEL: C12 C22
    Date: 2020–04
  2. By: Sayar Karmakar; Arkaprava Roy
    Abstract: Conditional heteroscedastic (CH) models are routinely used to analyze financial datasets. The classical models such as ARCH-GARCH with time-invariant coefficients are often inadequate to describe frequent changes over time due to market variability. However we can achieve significantly better insight by considering the time-varying analogues of these models. In this paper, we propose a Bayesian approach to the estimation of such models and develop computationally efficient MCMC algorithm based on Hamiltonian Monte Carlo (HMC) sampling. We also established posterior contraction rates with increasing sample size in terms of the average Hellinger metric. The performance of our method is compared with frequentist estimates and estimates from the time constant analogues. To conclude the paper we obtain time-varying parameter estimates for some popular Forex (currency conversion rate) and stock market datasets.
    Date: 2020–09
  3. By: Tino Berger; James Morley; Benjamin Wong
    Abstract: We propose a way to directly nowcast the output gap using the Beveridge-Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of potential, or the HP filter. We find that within-quarter nowcasts for the output gap are more reliable than for output growth, with monthly indicators for a credit risk spread, consumer sentiment, and the unemployment rate providing particularly useful new information about the final estimate of the output gap. An out-of-sample analysis of the COVID-19 crisis anticipates the exceptionally large negative output gap of -8.3% in 2020Q2 before the release of real GDP data for the quarter, with both conditional and scenario nowcasts tracking a dramatic decline in the output gap given the April data.
    Keywords: Nowcasting, output gap, COVID-19
    JEL: C32 C55 E32
    Date: 2020–08
  4. By: Muhammad Abubakr Naeem; Zhe Peng; Mouhammed Tahir Suleman; Rabindra Nepal; Syed Jawad Hussain Shahzad
    Abstract: This paper examines the time and frequency dynamics of connectedness between oil price shocks (demand and supply), and energy, electricity, carbon and clean energy markets using the methodology developed by Diebold and Yilmaz (2012) and Barunik and Krehlik (2018). The empirical findings show that there is time-varying connectedness among all variables in the sample. We find increased connectedness during the global financial crisis as well as in the shale oil revolution period. The total connectedness is more significant and higher in the short-term compared to the long-term. Net pairwise directional connectedness become more important during the shale oil revolution among oil supply, oil demand and clean energy index. The findings of the static full sample and sub-samples (GFC and SOR) provide significant evidence of the electricity futures as diversifier and safe-haven asset for oil shocks. These results can have important implications for investors and policymakers with different time horizons.
    Keywords: Oil shocks, Time-frequency connectedness, Electricity market, Carbon price, Clean energy
    JEL: G14 G15 Q41 Q42
    Date: 2020–09
  5. By: Andrea Carriero; Todd E. Clark; Massimiliano Marcellino
    Abstract: We derive a Bayesian prior from a no-arbitrage affine term structure model and use it to estimate the coefficients of a vector autoregression of a panel of government bond yields, specifying a common time-varying volatility for the disturbances. Results based on US data show that this method improves the precision of both point and density forecasts of the term structure of government bond yields, compared to a fully fledged term structure model with time-varying volatility and to a no-change random walk forecast. Further analysis reveals that the approach might work better than an exact term structure model because it relaxes the requirements that yields obey a strict factor structure and that the factors follow a Markov process. Instead, the cross-equation no-arbitrage restrictions on the factor loadings play a marginal role in producing forecasting gains.
    Keywords: Term structure; volatility; density forecasting; no arbitrage
    JEL: C32 C53 E43 E47 G12
    Date: 2020–09–22
  6. By: Sudiksha Joshi
    Abstract: In this research paper, I have applied various econometric time series and two machine learning models to forecast the daily data on the yield spread. First, I decomposed the yield curve into its principal components, then simulated various paths of the yield spread using the Vasicek model. After constructing univariate ARIMA models, and multivariate models such as ARIMAX, VAR, and Long Short Term Memory, I calibrated the root mean squared error to measure how far the results deviate from the current values. Through impulse response functions, I measured the impact of various shocks on the difference yield spread. The results indicate that the parsimonious univariate ARIMA model outperforms the richly parameterized VAR method, and the complex LSTM with multivariate data performs equally well as the simple ARIMA model.
    Date: 2020–09
  7. By: Yaya, OlaOluwa S; Olalude, Gbenga A; Olayinka, Hameed A; Jimoh, Toheeb A; Adebiyi, Aliu A
    Abstract: This present paper investigates the mean reversion in household consumption expenditure in 38 Africa countries, the expenditure series obtained as the percentage of nominal Gross Domestic Product (GDP), each spanning 1990 to 2018. Due to the small sample point of available time series of household expenditure, with possible structural breaks, the Fourier unit root test approach, allowing for modelling both smooth and instantaneous breaks in the expenditure series was utilised. The results showed non-mean reversion in the consumption expenditure pattern of Egypt, Madagascar, and Tunisia, while mean reversion was detected in the remaining 35 countries. Thus, the majority of African countries are on the verge of recession once shocks that affect the growth of GDP are triggered. Findings in this paper are of relevance to poverty alleviation programmes in those selected countries.
    Keywords: Household expenditure; Poverty level; Mean reversion; Africa
    JEL: C2 C22
    Date: 2020–09–11
  8. By: Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano
    Abstract: How damaging are uncertainty shocks during extreme events such as the great recession and the Covid-19 outbreak? Can monetary policy limit output losses in such situations? We use a nonlinear VAR framework to document the large response of real activity to a financial uncertainty shock during the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept of uncertainty comparable to that in our VAR. We employ the DSGE model to quantify the impact on real activity of an uncertainty shock under different Taylor rules estimated with normal times vs. great recession data (the latter associated with a stronger response to output). We find that the uncertainty shock-induced output loss experienced during the 2007-09 recession could have been twice as large if policymakers had not responded aggressively to the abrupt drop in output in 2008Q3. Finally, we use our estimated DSGE framework to simulate different paths of uncertainty associated to different hypothesis on the evolution of the coronavirus pandemic. We find that: i) Covid-19-induced uncertainty could lead to an output loss twice as large as that of the great recession; ii) aggressive monetary policy moves could reduce such loss by about 50%.
    Keywords: Uncertainty shock, nonlinear IVAR, nonlinear DSGE framework, minimum-distance estimation, great recession, Covid-19
    JEL: C22 E32 E52
    Date: 2020–09

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