nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒06‒12
six papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. FIEGARCH, modulus asymmetric FILog-GARCH and trend-stationary dual long memory time series By Yuanhua Feng; Thomas Gries; Sebastian Letmathe
  2. Monitoring multicountry macroeconomic risk By Dimitris Korobilis; Maximilian Schröder
  3. Does the SPF Help Predict the Shape of Recessions in Real Time?  By Yunjong Eo; James Morley
  4. The Role of Wages in Trend Inflation: Back to the 1980s? By Michael T. Kiley
  5. Range Volatility Spillover across Sectoral Stock Indices during COVID-19 Pandemic: Evidence from Indian Stock Market By Datta, Susanta; Hatekar, Neeraj
  6. Estimation of Nonlinear Exchange Rate Dynamics in Evolving Regimes By Jeffrey Frankel

  1. By: Yuanhua Feng (Paderborn University); Thomas Gries (Paderborn University); Sebastian Letmathe (Paderborn University)
    Abstract: A novel long memory volatility model MAFILog-GARCH (modulus asymmetric FILog-GARCH) is introduced, which has some advantages compared to the FIEGARCH. A general dual long memory FARIMA with them as error processes is defined. Moreover, a trend-stationary dual long memory model is proposed. The FIEGARCH and MAFILog-GARCH are first applied to returns of eight top US firms. It is found that their practical performances are comparable. Both are superior to the FIGARCH and FILog-GARCH. Further application provides evidence of trend-stationary dual long memory time series in different fields.
    Keywords: Modulus asymmetric FILog-GARCH, FIEGARCH, dual long memory, trend-stationary dual long memory, implementation in R
    Date: 2023–05
  2. By: Dimitris Korobilis (University of Glasgow, UK; Rimini Centre for Economic Analysis); Maximilian Schröder (BI Norwegian Business School, Norway; Norges Bank, Norway)
    Abstract: We propose a multicountry quantile factor augmented vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the effects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implement joint risk scenario analysis.
    Keywords: quantile VAR, MCMC, variational Bayes, dynamic factor model
    JEL: C11 C32 E31 E32 E37 E66
    Date: 2023–05
  3. By: Yunjong Eo; James Morley
    Abstract: We revisit our Markov-switching model of U.S. real GDP that accommodates different shapes of recessions to determine what it suggests about the nature of the COVID-19 recession. As with linear time series models, we find that it is important to account for the extreme outliers during the pandemic when estimating model parameters, but a simple decay function for volatility from 2020Q2 leads to robust inferences compared to our original estimates and clearly suggests that the COVID-19 recession was more U shaped than L shaped. We then consider the extent to which our model can be used to predict the shape of recessions in a real-time setting, rather than just classifying recessions ex post. Considering the last four recessions with real-time data and estimation, we find that feeding SPF data through our model can help accurately predict the nature of recovery at the time of the trough of each recession.
    Keywords: L-shaped recession, U-shaped recession, COVID-19, Markov switching, real-time analysis
    JEL: C22 C51 E32 E37
    Date: 2023–05
  4. By: Michael T. Kiley
    Abstract: This paper examines whether the measurement of trend inflation can be improved by using wage data in a dynamic factor model of disaggregated prices and wages for the United States. The model features time-varying coefficients and stochastic volatility. An estimate of trend inflation is a time-varying distributed lag of prices and wages, where the weight on a series depends on its time-varying volatility, persistence, and comovement with other series. The results show that wages inform estimates of trend inflation. The weight on wages was highest around 1980, drifted down through the 2000s, and returned to its 1980s value by 2022. In addition, inflation in the 2020s appears to have unmoored moderately from the 2 percent range that prevailed for decades, as the role of the persistent component of inflation increased in recent year. However, accounting for wages lowers the model's view of the increase in the volatility of trend inflation.
    Keywords: Price Inflation; Wage Inflation; Unobserved Components Model; Factor Model
    JEL: E37 E31 C32
    Date: 2023–04–17
  5. By: Datta, Susanta; Hatekar, Neeraj
    Abstract: The study examines volatility spillover across sectoral stock indices from one Emerging Market Economies, viz. India during COVID-19 pandemic. Our contributions are threefold: (a) incorporation of range volatility during the pandemic, (b) comparative assessment of volatility spillover at the sectoral level, and (c) identify evidence of volatility spillover across different sectoral indices. Using daily historical price data for 11 sectoral stock indices during the first wave of the pandemic; we find that Range GARCH (1, 1) performs better not only during the crisis but also during pandemic periods. The multivariate Range DCC model confirms evidence of volatility spillover across sectoral stock indices.
    Keywords: Forecasting, Volatility, Spillover, Return, Range, NIFTY, COVID 19
    JEL: C22 C58 G17
    Date: 2022–04–04
  6. By: Jeffrey Frankel
    Abstract: This paper develops a new econometric framework to estimate and classify exchange rate regimes. They are classified into four distinct categories: fixed exchange rates, BBC (band, basket and crawl), managed floating, and freely floating. The procedure captures the patterns of exchange rate dynamics and the interventions by authorities under each of the regimes. We pay particular attention to the BBC and offer a new approach to parameter estimation by utilizing a three-regime Threshold Auto Regressive (TAR) model to reveal the nonlinear nature of exchange rate dynamics. We further extend our benchmark framework to allow the evolution of exchange rate regimes over time by adopting the minimum description length (MDL) principle, to overcome the challenge of simultaneous two-dimensional inference of nonlinearity in the state dimension and structural breaks in the time dimension. We apply our framework to 26 countries. The results suggest that exchange rate dynamics under different regimes are well captured by our new framework.
    Keywords: Exchange rate regime, MDL, Minimum Description Length, structural breaks, TAR, Threshold Autoregression
    JEL: F33
    Date: 2023–03

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