nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒07‒25
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Time-Varying Multivariate Causal Processes By Jiti Gao; Bin Peng; Wei Biao Wu; Yayi Yan
  2. Nowcasting Macroeconomic Variables Using High-Frequency Fiscal Data By Robert Ambrisko
  3. ardl: Estimating autoregressive distributed lag and equilibrium correction models By Sebastian Kripfganz; Daniel C. Schneider
  4. Forecasting actuarial time series: a practical study of the effect of statistical pre-adjustments By Alexandros E. Milionis; Nikolaos G. Galanopoulos; Peter Hatzopoulos; Aliki Sagianou
  5. S-estimation in Linear Models with Structured Covariance Matrices By Ruiz-Gazen, Anne; Lopuhaä, Henrik Paul; Gares, Valérie
  6. Estimating spot volatility under infinite variation jumps with market microstructure noise By Qiang Liu; Zhi Liu
  7. The fractional volatility model and rough volatility By R. Vilela Mendes
  8. A Robust Test for Weak Instruments with Multiple Endogenous Regressors By Daniel J. Lewis; Karel Mertens

  1. By: Jiti Gao; Bin Peng; Wei Biao Wu; Yayi Yan
    Abstract: In this paper, we consider a wide class of time-varying multivariate causal processes which nests many classic and new examples as special cases. We first prove the existence of a weakly dependent stationary approximation for our model which is the foundation to initiate the theoretical development. Afterwards, we consider the QMLE estimation approach, and provide both point-wise and simultaneous inferences on the coefficient functions. In addition, we demonstrate the theoretical findings through both simulated and real data examples. In particular, we show the empirical relevance of our study using an application to evaluate the conditional correlations between the stock markets of China and U.S. We find that the interdependence between the two stock markets is increasing over time.
    Date: 2022–06
  2. By: Robert Ambrisko
    Abstract: Macroeconomic data are published with a time lag, making room for nowcasting macroeconomic variables using fiscal data. This is because a) monthly and daily fiscal data are available from the state budget in a very timely manner and b) many fiscal data are the function of macroeconomic variables. I employ two nowcasting models, bridge equations and MIDAS regressions, which link quarterly macroeconomic variables to monthly fiscal data for the Czech Republic. Bridge equations are found to be particularly suitable for nowcasting the wage bill using social contributions, achieving a 2% improvement in the root mean square error (RMSE) of one-quarter recursive forecasts compared to historical CNB forecasts. Further, I propose a tractable method for incorporating daily data into the nowcasting models, relying on STL decomposition by Cleveland et al. (1990). Depending on the timing, the RMSE for the wage bill can be up to 4% lower when the available daily data on social contributions are taken into account in the nowcasting models too.
    Keywords: Bridge equations, daily data, fiscal, midas, nowcasting, real-time data, short-term forecasting, STL
    JEL: C53 C82 E37
    Date: 2022–06
  3. By: Sebastian Kripfganz; Daniel C. Schneider
    Abstract: We present a Stata package for the estimation of autoregressive distributed lag (ARDL) models in a time-series context. The ardl command can be used to estimate an ARDL model with the optimal number of autoregressive and distributed lags based on the Akaike or Schwarz/Bayesian information cri- terion. The regression results can be displayed in the ARDL levels form or in the error-correction representation of the model. The latter separates long-run and short-run effects and is available in two different parameterizations of the long-run (cointegrating) relationship. The popular bounds testing procedure for the existence of a long-run levels relationship is implemented as a postestimation feature. Comprehensive critical values and approximate p-values obtained from response-surface regressions facilitate statistical inference.
    Date: 2022–04
  4. By: Alexandros E. Milionis (Bank of Greece and University of the Aegean); Nikolaos G. Galanopoulos (University of the Aegean); Peter Hatzopoulos (University of the Aegean); Aliki Sagianou (University of the Aegean)
    Abstract: One of the most important risks in the actuarial industry is the longevity risk. The accurate prediction of mortality rates plays a crucial role in the management of the aforementioned risk. Such predictions are performed by modelling the mortality rates using mortality models. Aiming at possible improvements in such forecasts, in this work we examine the effect of data transformation and “linearization†on the quality of time series forecasts of mortality rate data. By the term time series “linearization†is meant the treatment of causes that disrupt the underlying stochastic process measured by a time series. The dataset consists of the time series of the period indices uncovering the mortality trend for England-Wales according to published mortality models. Results indicate a clear improvement in interval forecasts. However, the result on point forecasts is not as clear as is the case of interval forecasts. The documented improvement in interval forecasts can significantly affect the Solvency Capital Requirement, and subsequently the Solvency Ratio for a pension fund. Such an improvement might put some pension providers at a competitive advantage as they have less capital locked in their liabilities. In addition, it was confirmed that the transformed-linearized time series of mortality rates satisfy to a higher extent the need for normality as compared to the original series.
    Keywords: Time series transformation and ‘’linearization’’; Outliers; Actuarial time series forecasts; Mortality rates; Covid-19
    JEL: C22 C51 C53 C87 G22
    Date: 2022–05
  5. By: Ruiz-Gazen, Anne; Lopuhaä, Henrik Paul; Gares, Valérie
    Abstract: We provide a unified approach to S-estimation in balanced linear models with structured covariance matrices. Of main interest are S-estimators for linear mixed effects models, but our approach also includes S-estimators in several other standard multivariate models, such as multiple regression, multivariate regression, and multivariate location and scatter. We provide sufficient conditions for the existence of S-functionals and S-estimators, establish asymptotic properties such as consistency and asymptotic normality, and derive their robustness prop-erties in terms of breakdown point and influence function. All the results are obtained for general identifiable covariance structures and are established under mild conditions on the distribution of the observations, which goes far beyond models with elliptically contoured densities. Some of our results are new and others are more general than existing ones in the literature. In this way this manuscript completes and improves results on S-estimation in a wide variety of multivariate models. We illustrate our results by means of a simulation study and an application to data from a trial on the treatment of lead-exposed children.
    Date: 2022–06–22
  6. By: Qiang Liu; Zhi Liu
    Abstract: Jumps and market microstructure noise are stylized features of high-frequency financial data. It is well known that they introduce bias in the estimation of volatility (including integrated and spot volatilities) of assets, and many methods have been proposed to deal with this problem. When the jumps are intensive with infinite variation, the estimation of spot volatility in a noisy setting is not available and is thus in need. To this end, we propose a novel estimator of spot volatility with a hybrid use of the pre-averaging technique and the empirical characteristic function. Under mild assumptions, the consistency and asymptotic normality results of our estimation were established. Furthermore, we showed that our estimator achieves an almost efficient convergence rate with optimal variance. Simulation studies verified our theoretical conclusions. We also applied our proposed estimator to conduct empirical analyses, such as estimating the weekly volatility curve using second-by-second transaction price data.
    Date: 2022–05
  7. By: R. Vilela Mendes
    Abstract: The question of the volatility roughness is interpreted in the framework of a data-reconstructed fractional volatility model, where volatility is driven by fractional noise. Some examples are worked out and also, using Malliavin calculus for fractional processes, an option pricing equation and its solution are obtained.
    Date: 2022–06
  8. By: Daniel J. Lewis; Karel Mertens
    Abstract: We extend the popular bias-based test of Stock and Yogo (2005) for instrument strength in linear instrumental variables regressions with multiple endogenous regressors to be robust to heteroskedasticity and autocorrelation. Equivalently, we extend the robust test of Montiel Olea and Pflueger (2013) for one endogenous regressor to the general case with multiple endogenous regressors. We describe a simple procedure for applied researchers to conduct our generalized first-stage test of instrument strength and provide efficient and easy-to-use Matlab code for its implementation. We demonstrate our testing procedures by considering the estimation of the state-dependent effects of fiscal policy as in Ramey and Zubairy (2018).
    Keywords: Instrumental Variables; Weak Instruments Test; Multiple Endogenous Regressors; Heteroskedasticity; Serial Correlation
    JEL: C26 C36
    Date: 2022–06–22

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