nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒04‒13
seven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Partial cointegrated vector autoregressive models with structural breaks in deterministic terms By Takamitsu Kurita; B. Nielsen
  2. Nonlinear Models of Convergence By Gluschenko, Konstantin
  3. Stock Market Volatility Analysis: A Case Study of TUNindex By NEIFAR, MALIKA
  4. High-dimensional mixed-frequency IV regression By Andrii Babii
  5. Some forecasting principles from the M4 competition By Jennifer L. Castle; Jurgen A. Doornik; David Hendry
  6. Sequential monitoring for cointegrating regressions By Lorenzo Trapani; Emily Whitehouse
  7. Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach By Francis X. Diebold; Maximilian G\"obel; Philippe Goulet Coulombe; Glenn D. Rudebusch; Boyuan Zhang

  1. By: Takamitsu Kurita (Faculty of Economics, Fukuoka University); B. Nielsen (Nuffield College, University of Oxford)
    Abstract: This paper proposes a class of partial cointegrated models allowing for structural breaks in their deterministic terms. Details of the proposed models and their moving-average representations are examined. It is then shown that, under the assumption of martingale di§erence innovations, the limit distributions of partial quasi-likelihood ratio tests for cointegrating rank have a close connection to those for standard full models. This connection facilitates a response surface analysis which is required to extract critical information about moments from large-scale simulation studies. An empirical illustration of the proposed methodology is also provided. This paper renders partial cointegrated models more áexible and reliable devices for the study of non-stationary time series data with structural breaks.
    Keywords: Partial cointegrated vector autoregressive models, Structural breaks, Deterministic terms, Weak exogeneity, Cointegrating rank, Response surface.
    JEL: C12 C32 C50
    Date: 2018–10–22
  2. By: Gluschenko, Konstantin
    Abstract: A sufficient issue in studies of economic development is whether economies (countries, regions of a country, etc.) converge to one another in terms of per capita income. In this paper, nonlinear asymptotically subsiding trends of income gap in a pair of economies model the convergence process. A few specific forms of such trends are proposed: log-exponential trend, exponential trend, and fractional trend. A pair of economies is deemed converging if time series of their income gap is stationary about any of these trends. To test for stationarity, standard unit root tests are applied with non-standard test statistics that are estimated for each kind of the trends.
    Keywords: income convergence; time series econometrics; nonlinear time-Series model; unit root
    JEL: C32 C51
    Date: 2020–03–28
    Abstract: Volatility is directly associated with risks and returns. This study aims to examine the volatility characteristics on Tunisian stock market index (5 days a weak TUNindex) that include clustering volatility, leptokurtosis, and leverage effect. The first objective is then to use the GARCH type models to estimate volatility of the daily returns series, consisting of 2191 observations from 01/02/2011 to 19/11/2019, with no significant weekdays effect. We use both symmetric and asymmetric models. The main findings suggest that the symmetric GARCHM and asymmetric TGARCH /APGARCH models can capture characteristics of TUNindex whereas EGARCH reveals no significant support for leverage effect existence. Looking at news impact curves, GJR model appears to be relatively better than other models. However, the volatility of stock returns is more affected by the past volatility than the related news from the previous period. The second objective is to use GARCHM- X S models to capture the effect of macro-economic instability via exchange rate growth and exchange rate volatility. For policy, GARCHM-XS2 turned to be the best model. The macroeconomic environment should be favourable to ensure growth in the stock market. Policies to reduce volatility in the the economy (more stable exchange rate) are a necessity for stock market.
    Keywords: Tunisia, Stock Market, Tunindex, Volatility, Symmetric and Asymmetric GARCH Models, GARCH, TGARCH, GARCH-M, EGARCH, GARCHM-XS, Leverage Effect., Risk Premium, Stability.
    JEL: C22 D8 D81 D82 E44 E47 O16
    Date: 2020–03–17
  4. By: Andrii Babii
    Abstract: This paper introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonov-regularized estimator and derive the convergence rate of its mean-integrated squared error for time series data. The estimator has a closed-form expression that is easy to compute and demonstrates excellent performance in our Monte Carlo experiments. We estimate the real-time price elasticity of supply on the Australian electricity spot market. Our estimates suggest that the supply is relatively inelastic and that its elasticity is heterogeneous throughout the day.
    Date: 2020–03
  5. By: Jennifer L. Castle (Magdelen College, University of Oxford); Jurgen A. Doornik (Nuffield College, University of Oxford); David Hendry (Nuffield College, University of Oxford)
    Abstract: Economic forecasting is difficult, largely because of the many sources of nonstationarity. The M4 competition aims to improve the practice of economic forecasting by providing a large data set on which the efficacy of forecasting methods can be evaluated. We consider the general principles that seem to be the foundation for successful forecasting, and show how these are relevant for methods that do well in M4. We establish some general properties of the M4 data set, which we use to improve the basic benchmark methods, as well as the Card method that we created for our submission to the M4 competition. A data generation process is proposed that captures the salient features of the annual data in M4.
    Keywords: Automatic forecasting, Calibration, Prediction intervals, Regression, M4, Seasonality, Software, Time series, Unit roots
    Date: 2019–01–09
  6. By: Lorenzo Trapani; Emily Whitehouse
    Abstract: We develop monitoring procedures for cointegrating regressions, testing the null of no breaks against the alternatives that there is either a change in the slope, or a change to non-cointegration. After observing the regression for a calibration sample m, we study a CUSUM-type statistic to detect the presence of change during a monitoring horizon m+1,...,T. Our procedures use a class of boundary functions which depend on a parameter whose value affects the delay in detecting the possible break. Technically, these procedures are based on almost sure limiting theorems whose derivation is not straightforward. We therefore define a monitoring function which - at every point in time - diverges to infinity under the null, and drifts to zero under alternatives. We cast this sequence in a randomised procedure to construct an i.i.d. sequence, which we then employ to define the detector function. Our monitoring procedure rejects the null of no break (when correct) with a small probability, whilst it rejects with probability one over the monitoring horizon in the presence of breaks.
    Date: 2020–03
  7. By: Francis X. Diebold; Maximilian G\"obel; Philippe Goulet Coulombe; Glenn D. Rudebusch; Boyuan Zhang
    Abstract: The diminishing extent of Arctic sea ice is a key indicator of climate change as well as an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that accounts for their differing volatility and cross-correlations. From this model, we extract an optimal combined measure of Arctic sea ice extent using the Kalman smoother.
    Date: 2020–03

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