nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒09‒27
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. "Particle Rolling MCMC with Double-Block Sampling " By Naoki Awaya; Yasuhiro Omori
  2. Algorithms for Inference in SVARs Identified with Sign and Zero Restrictions By Matthew Read
  3. Refining Set-Identification in VARs through Independence By Thorsten Drautzburg; Jonathan H. Wright
  4. Gold and Silver prices, their stocks and market fear gauges: Testing fractional cointegration using a robust approach By Yaya, OlaOluwa S; Vo, Xuan Vinh; Olayinka, Hammed Abiola
  5. Market Efficiency of Asian Stocks: Evidence based on Narayan-Liu-Westerlund GARCH-based Unit root test By Yaya, OlaOluwa S.; Vo, Xuan Vinh; Adekoya, Oluwasegun B.
  6. Unemployment Hysteresis in Middle East and North Africa Countries: Panel SUR-based Unit root test with a Fourier function By Awolaja, Oladapo G.; Yaya, OlaOluwa S; Vo, Xuan Vinh; Ogbonna, Ahamuefula; Joseph, Solomon O.
  7. Discussing anthropogenic global warming from an econometric perspective: a change scenario based on the Arima paleoclimate time series model By Gilmar V. F. Santos; Lucas G. Cordeiro; Claudio A. Rojo e Edison L. Leismann
  8. Testing Conditional Independence in Macroeconomic Policy Evaluation for Time Series Data By Ying Fang; Ming Lin; Shengfang Tang; Zongwu Cai

  1. By: Naoki Awaya (Graduate School of Economics, The University of Tokyo); Yasuhiro Omori (Faculty of Economics, The University of Tokyo)
    Abstract: An efficient particle Markov chain Monte Carlo methodology is proposed for the rollingwindow estimation of state space models. The particles are updated to approximate the long sequence of posterior distributions as we move the estimation window. To overcome the wellknown weight degeneracy problem that causes the poor approximation, we introduce a practical double-block sampler with the conditional sequential Monte Carlo update where we choose one lineage from multiple candidates for the set of current state variables. Our proposed sampler is justified in the augmented space through theoretical discussions. In the illustrative examples, it is shown to be successful to accurately estimate the posterior distributions of the model parameters.
    Date: 2021–09
  2. By: Matthew Read
    Abstract: I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms proposed in Amir-Ahmadi and Drautzburg (2021) to check whether the identified set is nonempty and to sample from the identified set without rejection sampling. I compare the new algorithms to alternatives by applying them to variations of the model considered by Arias et al (2019), who estimate the effects of US monetary policy using sign and zero restrictions on the monetary policy reaction function. The new algorithms are particularly useful when a large number of sign restrictions substantially truncate the identified set given the zero restrictions.
    Date: 2021–09
  3. By: Thorsten Drautzburg; Jonathan H. Wright
    Abstract: Identification in VARs has traditionally mainly relied on second moments. Some researchers have considered using higher moments as well, but there are concerns about the strength of the identification obtained in this way. In this paper, we propose refining existing identification schemes by augmenting sign restrictions with a requirement that rules out shocks whose higher moments significantly depart from independence. This approach does not assume that higher moments help with identification; it is robust to weak identification. In simulations we show that it controls coverage well, in contrast to approaches that assume that the higher moments deliver point-identification. However, it requires large sample sizes and/or considerable non-normality to reduce the width of confidence intervals by much. We consider some empirical applications. We find that it can reject many possible rotations. The resulting confidence sets for impulse responses may be non-convex, corresponding to disjoint parts of the space of rotation matrices. We show that in this case, augmenting sign and magnitude restrictions with an independence requirement can yield bigger gains
    Keywords: vector-autoregression; sign restrictions; set-identification; weak identification; non-convex confidence set; independent shock
    JEL: C51 C32
    Date: 2021–09–17
  4. By: Yaya, OlaOluwa S; Vo, Xuan Vinh; Olayinka, Hammed Abiola
    Abstract: The present paper investigates the long-run relationships between daily prices, stocks and fear gauges of gold and silver by employing an updated fractional cointegrating framework, that is, the Fractional Cointegrating Vector Autoregression (FCVAR). The initial unit root tests results indicate that the series are I(d)s with values of d around 1 in all cases, and these are homogenous in the paired cointegrating series. Evidence of cointegration is found in the three pairs (prices, stocks and market gauge indices), while these cointegrations are only time-varying in the case of market gauge indices for the commodities. The fact that cointegration exists in prices and stocks of gold and silver implies the possibility that gold and silver prices and stocks can interchangeably be used to access the performances of the commodity markets, with the recommendation that the two commodities are not to be traded in the same portfolio.
    Keywords: Fractional cointegration; FCVAR; Gold; Silver; Mean reversion; Market fear gauges
    JEL: C22 C32
    Date: 2021–05–06
  5. By: Yaya, OlaOluwa S.; Vo, Xuan Vinh; Adekoya, Oluwasegun B.
    Abstract: This study uses the recently developed Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model-based unit root test of Narayan et al. (2016) to examine the stock market efficiency of 19 Asian countries, using daily prices. The model flexibly accounts for heteroskedasticity and two structural breaks, the presence of which can lead to inaccurate results if neglected. Our results disclose the stock markets of 14 countries as inefficient following the rejection of the unit root null hypothesis. However, the stock markets of China, Hong Kong, Japan and the Korea Republic are adjudged efficient. We further extend the model to accommodate a maximum of five breaks to check the robustness of our results to higher breaks. We observe that the results are largely consistent except for Lebanon and Singapore. For completeness, we compare the results with those of conventional GARCH models that do not account for structural breaks and discover differing results for some countries. Hence, the role of structural breaks is not negligible in assessing market efficiency. Future studies should also incorporate heteroskedasticity and structural breaks in their modelling framework to obtain accurate results.
    Keywords: Stock market efficiency; GARCH; Unit root; Structural breaks; Asia
    JEL: C22 G01 G15
    Date: 2021–09–14
  6. By: Awolaja, Oladapo G.; Yaya, OlaOluwa S; Vo, Xuan Vinh; Ogbonna, Ahamuefula; Joseph, Solomon O.
    Abstract: Unemployment hysteresis of the Middle East and North African (MENA) countries is investigated under a battery of unit root testing frameworks in the extant literature, including a recently proposed Panel SUR Dickey-Fuller-like unit root test with Fourier and Exponential Smooth Transition Regression (ESTR) nonlinearities. The Fourier function allows for smooth nonlinear breaks, while the ESTR nonlinearity allows for instantaneous breaks. The two nonlinearity types make the recent approach quite appealing. It has, however, been scarcely applied to empirically test unemployment hysteresis hypothesis. Although we find conflicting stances from ADF, FADF and ADF-SB testing frameworks, evidence of unemployment hysteresis effect in Lebanon is consistent across all three tests. The ADF and FADF tests confirm the hysteresis hypothesis in Kuwait and Lebanon, while FADF-SB rejects the unemployment hysteresis hypothesis across all the 19 MENA countries. The results from the KSS and FKSS unit root testing frameworks consistently affirmed the hysteresis effect in Oman and Turkey, while there are mixed stances for Kuwait and Lebanon. The results from SURADF and SURKSS only support the hysteresis hypothesis in Turkey, while the same is confirmed only for Bahrain under the SURFADF and SURFKSS testing frameworks. Unemployment hysteresis hypothesis is confirmed for 12 (about 63.15% of the total number considered) MENA economies.
    Keywords: Unemployment rate; MENA countries; Fourier function; Seemingly Unrelated Regression; Panel data; Unit root test
    JEL: C22 C23 E24 J64
    Date: 2021–03–02
  7. By: Gilmar V. F. Santos; Lucas G. Cordeiro; Claudio A. Rojo e Edison L. Leismann
    Abstract: Global warming has divided the scientific community worldwide with predominance for anthropogenic alarmism. This article aims to project a climate change scenario using a stochastic model of paleotemperature time series and compare it with the dominant thesis. The ARIMA model, an integrated autoregressive process of moving averages, popularly known as Box-Jenkins, was used for this purpose. The results showed that the estimates of the model parameters were below 1 degree Celsius for a scenario of 100 years which suggests a period of temperature reduction and a probable cooling, contrary to the prediction of the IPCC and the anthropogenic current of an increase in 1,50 degree to 2,0 degree Celsius by the end of this century. Thus, we hope with this study to contribute to the discussion by adding a statistical element of paleoclimate in counterpoint to the current consensus and to placing the debate in a long term historical dimension, in line with other research already present in the scientific literature.
    Date: 2021–09
  8. By: Ying Fang (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian 361005, China); Ming Lin (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian 361005, China); Shengfang Tang (Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian 361005, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)
    Abstract: In this paper, we propose a new procedure to test conditional independence assumption for macroeconomic policy evaluation in a time series context. The unconfoundedness assumption is transformed to a nonparametric conditional moment test using auxiliary variables which are allowed to affect potential outcomes but the dependence can be fully captured by potential outcomes and observable controls. When the policy choice is binary, a nonparametric statistic test is developed further for testing the unconfoundedness assumption conditional on policy propensity score. The proposed test statistics are shown to have the limiting normal distribution under the null hypotheses for time series data. Monte Carlo simulations are conducted to examine the finite sample performances of the proposed test statistics. Finally, the proposed test method is applied to testing the conditional unconfoundedness in a real example as considered in Angrist and Kuersteiner (2011).
    Keywords: Bootstrap; Macroeconomic policy evaluation; Moment test; Nonparametric estimation; Selection on observable; Treatment effect; Unconfoundedness assumption.
    JEL: C12 C13 C14 C23
    Date: 2021–09

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