nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒09‒28
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model By Bram van Os; Dick van Dijk
  2. Identification of Structural VAR Models via Independent Component Analysis: A Performance Evaluation Study By Alessio Moneta; Gianluca Pallante
  3. Measuring Monetary Policy with Residual Sign Restrictions at Known Shock Dates By Harald Badinger; Stefan Schiman
  4. Forecasting Low Frequency Macroeconomic Events with High Frequency Data By Ana B. Galvão; Michael T. Owyang
  5. Electricity Consumption, Urbanization and Economic Growth in Nigeria: New Insights from Combined Cointegration amidst Structural Breaks By Solomon P. Nathaniel; Festus V. Bekun
  6. Asymmetric Effects of Exchange Rate Changes on Exports: A Sectoral Nonlinear Cointegration Analysis for Turkey By Bilgin, Cevat

  1. By: Bram van Os (Erasmus University Rotterdam); Dick van Dijk (Erasmus University Rotterdam)
    Abstract: The dynamic factor Markov-switching (DFMS) model introduced by Chauvet (1998) has proven to be a powerful framework to measure the business cycle. We extend the DFMS model by allowing for time-varying transition probabilities, with the aim of accelerating the real-time dating of turning points between expansion and recession regimes. Time-variation of the transition probabilities is brought about endogenously using the accelerated score-driven approach and exogenously using the term spread. In a real-time application using the four components of The Conference Board’s Coincident Economic Index for the period 1959-2020, we find that signaling power for recessions is significantly improved.
    Keywords: business cycles, turning points, Markov-Switching, time-varying transition probabilities, generalized autoregressive score model
    JEL: E32 C32
    Date: 2020–09–15
  2. By: Alessio Moneta; Gianluca Pallante
    Abstract: Independent Component Analysis (ICA) is a statistical method that transforms a set of random variables in least dependent linear combinations. Under the assumption that the observed data are mixtures of non-Gaussian and independent processes, ICA is able to recover the underlying components, but a scale and order indeterminacy. Its application to structural vector autoregressive (SVAR) models allows the researcher to recover the impact of independent structural shocks on the observed series from estimated residuals. We analyze different ICA estimators, recently proposed within the field of SVAR identification, and compare their performance in recovering structural coefficients. Moreover, after suggesting an algorithm that solve the ICA indeterminacy problem, we assess the size distortions of the estimators in hypothesis testing. We conduct our analysis by focusing on distributional scenarios that get gradually close the Gaussian case, which is the case where ICA methods fail to recover the independent components. In terms of statistical properties of the ICA estimators, we find no evidence that a method outperforms all others. We finally present an empirical illustration using US data to identify the effects of government spending and tax cuts on economic activity, thus providing an example where ICA techniques can be used for hypothesis testing.
    Keywords: Independent Component Analysis; Identification; Structural VAR; Impulse response functions; Non-Gaussianity; Generalized normal distribution
    Date: 2020–09–12
  3. By: Harald Badinger (Department of Economics, Vienna University of Economics and Business); Stefan Schiman (Austrian Institute of Economic Research (WIFO))
    Abstract: We propose a novel identification strategy to measure monetary policy in a structural VAR. It is based exclusively on known past policy shocks, which are uncovered from high-frequency data, and does not rely on any theoretical a-priori restrictions. Our empirical analysis for the euro area reveals that interest rate decisions of the ECB surprised financial markets at least fifteen times since 1999. This information is used to restrict the sign and magnitude of the structural residuals of the policy rule equation at these shock dates accordingly. In spite of its utmost agnostic nature, this approach achieves strong identification, suggesting that unexpected ECB decisions have an immediate impact on the short-term money market rate, the narrow money stock, commodity prices, consumer prices and the Euro-Dollar exchange rate, and that real output responds gradually. Our close to assumption-free approach obtains as an outcome what traditional sign restrictions on impulse responses impose as an assumption.
    Keywords: Structural VAR, Set Identification, Monetary Policy, ECB
    JEL: C32 E52 N14
    Date: 2020–07
  4. By: Ana B. Galvão; Michael T. Owyang
    Abstract: High-frequency financial and economic activity indicators are usually time aggregated before forecasts of low-frequency macroeconomic events, such as recessions, are computed. We propose a mixed-frequency modelling alternative that delivers high-frequency probability forecasts (including their confidence bands) for these low-frequency events. The new approach is compared with single-frequency alternatives using loss functions adequate to rare event forecasting. We provide evidence that: (i) weekly-sampled spread improves over monthly-sampled to predict NBER recessions, (ii) the predictive content of the spread and the Chicago Fed Financial Condition Index (NFCI) is supplementary to economic activity for one-year-ahead forecasts of contractions, and (iii) a weekly activity index can date the 2020 business cycle peak two months in advance using a mixed-frequency filtering.
    Keywords: mixed frequency models; recession; financial indicators; weekly activity index; event probability forecasting
    JEL: C25 C53 E32
    Date: 2020–09
  5. By: Solomon P. Nathaniel (University of Lagos, Akoka, Nigeria); Festus V. Bekun (Istanbul Gelisim University, Istanbul, Turkey)
    Abstract: The study explores the link between electricity consumption, urbanization and economic growth in Nigeria from 1971-2014. The bounds test and the Bayer and Hanck (2013) cointegration tests affirm cointegrating relationship. Electricity consumption increases economic growth in both time periods, while the impact of urbanization appears to inhibit growth. The fully modified OLS (FMOLS), dynamic OLS (DOLS) and the canonical cointegrating regression (CCR) confirm the robustness of the findings. The vector error correction model (VECM) Granger causality test supports the neutrality hypothesis in the short run and the feedback hypothesis among the variables in the long run. Therefore, policies to ensure efficient electricity supply, curb rapid urbanization and promote sustainable economic growth were suggested.
    Keywords: Electricity Consumption; Urbanization; Economic Growth;ARDL; Nigeria.
    Date: 2020–01
  6. By: Bilgin, Cevat
    Abstract: This paper examines the effects of the real exchange rate changes on the selected sectoral exports of Turkey’s manufacturing industry in the context of nonlinear auto-regressive distributed lag model (NARDL). NARDL method includes short-run and long-run coefficient estimates and embraces the asymmetric effects. The previous studies generally used the linear models on the aggregated data and they offered ambiguous results. The latest studies have preferred to use the method of NARDL on the bilateral trade balance data. Instead of using bilateral data, this paper considers the data of sectoral exports, specifically the exports of the selected Turkey’s manufacturing sectors. The estimated NARDL models supply the empirical information about the asymmetric effects of the real exchange rate on the sectoral exports. Results from the model for each sector provide the evidence indicating that the depreciation and appreciation of the domestic currency have asymmetric significant effects on the sectoral exports.
    Keywords: Real exchange rate, Sectoral Export, Nonlinear Cointegration, Asymmetric Effects
    JEL: C13 C51 C52 F14 F31 F41
    Date: 2020

This nep-ets issue is ©2020 by Jaqueson K. Galimberti. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.