nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒06‒15
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Structural Vector Autoregressive Models with more Shocks than Variables Identified via Heteroskedasticity By Helmut Lütkepohl
  2. Improvement on the LR Test Statistic on the Cointegrating Relations in VAR Models: Bootstrap Methods and Applications. By Canepa, Alessandra
  3. exuber: Recursive Right-Tailed Unit Root Testing with R By Enrique Martínez-García; Efthymios Pavlidis; Kostas Vasilopoulos
  4. Fractional trends and cycles in macroeconomic time series By Tobias Hartl; Rolf Tschernig; Enzo Weber
  5. Macroeconomic Forecasting with Fractional Factor Models By Tobias Hartl
  6. Machine learning time series regressions with an application to nowcasting By Andrii Babii; Eric Ghysels; Jonas Striaukas
  7. Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations By Håvard Hungnes
  8. Application of GARCH Models For Volatility Modelling of Stock Market Returns: Evidences From BSE India By Neeti Mathur; Himanshu Mathur
  9. Weather, pollution and Covid-19 spread: a time series and Wavelet reassessment By Olivier Damette; Stéphane Goutte
  10. Modeling the Covid-19 Epidemic Using Time Series Econometrics By Adam Golinski; Peter Spencer
  11. Information weighting under least squares learning By Jaqueson K. Galimberti

  1. By: Helmut Lütkepohl
    Abstract: In conventional structural vector autoregressive (VAR) models it is assumed that there are at most as many structural shocks as there are variables in the model. It is pointed out that heteroskedasticity can be used to identify more shocks than variables. However, even if there is heteroskedasticity, the number of shocks that can be identified is limited. A number of results are provided that allow a researcher to assess how many shocks can be identified from specific forms of heteroskedasticity.
    Keywords: Structural vector autoregression, identification through heteroskedasticity, structural shocks
    JEL: C32
    Date: 2020
  2. By: Canepa, Alessandra (University of Turin)
    Abstract: A Bartlett corrected likelihood ratio test for linear restrictions on the cointegrating relations is examined in Johansen (2000). Simulation results show that the performance of the corrected LR test statistic is highly dependent on the values of the parameters of the model. In order to reduce this dependency, it is proposed that the ?nite sample expectation of the LR test be estimated using the bootstrap. It is found that the bootstrap Bartlett correction often succeeds in this task.
    Date: 2020–03
  3. By: Enrique Martínez-García; Efthymios Pavlidis; Kostas Vasilopoulos
    Abstract: This paper introduces the R package exuber for testing and date-stamping periods of mildly explosive dynamics (exuberance) in time series. The package computes test statistics for the supremum ADF test (SADF) of Phillips, Wu and Yu (2011), the generalized SADF (GSADF) of Phillips, Shi and Yu (2015a,b), and the panel GSADF proposed by Pavlidis, Yusupova, Paya, Peel, Martínez-García, Mack and Grossman (2016); generates finite-sample critical values based on Monte Carlo and bootstrap methods; and implements the corresponding date-stamping procedures. The recursive least-squares algorithm that we introduce in our implementation of these techniques utilizes the matrix inversion lemma and in that way achieves significant speed improvements. We illustrate the speed gains in a simulation experiment, and provide illustrations of the package using artificial series and a panel on international house prices.
    Keywords: Mildly explosive time series; Right-tailed unit root tests; R
    JEL: C15 C22 C23 C53 C87
    Date: 2020–05–12
  4. By: Tobias Hartl; Rolf Tschernig; Enzo Weber
    Abstract: We develop a generalization of correlated trend-cycle decompositions that avoids prior assumptions about the long-run dynamic characteristics by modelling the permanent component as a fractionally integrated process and incorporating a fractional lag operator into the autoregressive polynomial of the cyclical component. We relate the model to the Beveridge-Nelson decomposition and derive a modified Kalman filter estimator for the fractional components. Identification and consistency of the maximum likelihood estimator are shown. For US macroeconomic data we demonstrate that, unlike non-fractional correlated unobserved components models, the new model estimates a smooth trend together with a cycle hitting all NBER recessions.
    Date: 2020–05
  5. By: Tobias Hartl
    Abstract: We combine high-dimensional factor models with fractional integration methods and derive models where nonstationary, potentially cointegrated data of different persistence is modelled as a function of common fractionally integrated factors. A two-stage estimator, that combines principal components and the Kalman filter, is proposed. The forecast performance is studied for a high-dimensional US macroeconomic data set, where we find that benefits from the fractional factor models can be substantial, as they outperform univariate autoregressions, principal components, and the factor-augmented error-correction model.
    Date: 2020–05
  6. By: Andrii Babii; Eric Ghysels; Jonas Striaukas
    Abstract: This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that the text data can be a useful addition to more traditional numerical data.
    Date: 2020–05
  7. By: Håvard Hungnes (Statistics Norway)
    Abstract: The paper derives a test for equal predictability of multi-step-ahead system forecasts that is invariant to linear transformations. The test is a multivariate version of the Diebold-Mariano test. An invariant metric for multi-step-ahead system forecasts is necessary as the conclusions otherwise can depend on how the forecasts are reported (e.g., as in levels or differences; or log-levels or growth rates). The test is used in comparing quarterly multi-step-ahead system forecasts made by Statistics Norway with similar forecasts made by Norges Bank.
    Keywords: Macroeconomic forecasts; Econometric models; Forecast performance; Forecast evaluation; Forecast comparison
    JEL: C32 C53
    Date: 2020–05
  8. By: Neeti Mathur (NIIT University, Neemrana); Himanshu Mathur (Bright Minds Education Society)
    Abstract: The National Stock Exchange and Bombay Stock Exchange are the two major stock exchanges in India. The Bombay Stock Exchange is the first stock exchange of Asia and 10th largest stock exchange in the world in the terms of market capitalisation. Stock markets significantly contributes in the economic development of India. The stock markets have volatile character which results into the uncertainty of the returns, volatility is caused by the variability in speculative market prices and the instability of business performance. Volatility plays a significant role in financial decisions of the investors, managers, policy makers and the researchers as it can assess the risk exposures in their investments and the uncertainty in stocks returns. The risk averse investor avoid investment in highly volatile market. The stock return forecasting leads to volatility forecasting. This paper has made an attempt to analyse the volatility with reference to Bombay Stock Exchange. The daily data of S&P Sensex 30 has been collected and used to calculate the volatility of stock market in India for last 3 years (April 2016 to March 2019). The preliminary analysis is done on the basis of descriptive statistics Stationery test, Normality test and serial correlation test. Volatility modelling is done by the ARCH and GARCH family models.The findings of the study will help investors in taking good investment decisions in Indian stock market in the presence of its volatile character.
    Keywords: ARCH Model, Custer Analysis Diversification, Expansion, Generalized ARCH Model (GARCH Model), Growth, Return, Risk
    JEL: C55 C19
    Date: 2020–02
  9. By: Olivier Damette (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - CNRS - Centre National de la Recherche Scientifique - UL - Université de Lorraine - UNISTRA - Université de Strasbourg); Stéphane Goutte (Cemotev - Centre d'études sur la mondialisation, les conflits, les territoires et les vulnérabilités - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines)
    Abstract: Faced with the global pandemic of Covid-19, we need to better understand the links between meteorological factors, air quality and the virus. In the vein of a recent empirical literature, we reassess the impact of weather factors like temperatures, humidity and air quality indicators on Covid-19 daily cases in China both for Wuhan and Beijing. Using a consistent number of observations (104), we compute, for the first time, correlations but also Granger causality and above all, a spectral analysis using Wavelet methods. Our results go further previous studies and reveal the complexity of the studied relationships when both time and frequency domains are taken into account. Wavelet analysis enables us to go further usual correlations analysis. Though negative humidity impact on Covid-19 cases was expected to be relatively clear regarding previous literature based on correlations, we do not find evidence of such a result. The controversial effect of warmer temperatures on the Covid-19, often difficult to identify or sometimes identified as surprisingly positive, can negatively emerge via Wavelet analysis for some periods only. This result is however clear-cut for the Hubei Province but for the Beijing one. Finally, our results reveal a bi-directional causality between air quality and the number of infected people. Short-run causality from Covid-19 to air quality (better induced air quality) via lockdown policies disappear in a medium-run and turns to become a significant causal link from induced air quality improvement to Covid-19 daily cases (reduction of infected people).
    Date: 2020–05–27
  10. By: Adam Golinski; Peter Spencer
    Abstract: The classic "logistic" model has provided a realistic model of the behavior of Covid-19 in China and many East Asian countries. Once these countries passed the peak, the daily case count fell back, mirroring its initial climb in a symmetric way, just as the classic model predicts. However, in Italy and Spain, and now the UK and many other Western countries, the experience has been very different. The daily count has fallen back gradually from the peak but remained stubbornly high. The reason for the divergence from the classical model remain unclear. We take an empirical stance on this issue and develop a model that is based upon the statistical characteristics of the time series. With the possible exception of China, the workhorse logistic model is decisively rejected against more flexible alternatives
    Date: 2020–05
  11. By: Jaqueson K. Galimberti
    Abstract: This paper evaluates how adaptive learning agents weight different pieces of information when forming expectations with a recursive least squares algorithm. The analysis is based on a renewed and more general non-recursive representation of the learning algorithm, namely, a penalized weighted least squares estimator, where a penalty term accounts for the effects of the learning initials. The paper then draws behavioral implications of alternative specifications of the learning mechanism, such as the cases with decreasing, constant, regime-switching, adaptive, and age-dependent gains, as well as practical recommendations on their computation. One key new finding is that without a proper account for the uncertainty about the learning initial, a constant-gain can generate a time-varying profile of weights given to past observations, particularly distorting the estimation and behavioral interpretation of this mechanism in small samples of data. In fact, simulations and empirical estimation of a Phillips curve model with learning indicate that this particular misspecification of the initials can lead to estimates where inflation rates are less responsive to expectations and output gaps than in reality, or “flatter” Phillips curves.
    Keywords: bounded rationality, expectations, adaptive learning, memory
    JEL: D83 D84 D90 E37 C32 C63
    Date: 2020–05

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