nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒12‒14
seven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A simple unit root test consistent against any stationary alternative By Frédérique Bec; Alain Guay
  2. Semi-Structural VAR and Unobserved Components Models to Estimate Finance-Neutral Output Gap By Gabor Katay; Lisa Kerdelhué; Matthieu Lequien
  3. A Two-Way Transformed Factor Model for Matrix-Variate Time Series By Zhaoxing Gao; Ruey S. Tsay
  4. Visual Forecasting of Time Series with Image-to-Image Regression By Naftali Cohen; Srijan Sood; Zhen Zeng; Tucker Balch; Manuela Veloso
  5. Impulse response analysis in conditional quantile models with an application to monetary policy By Dong Jin Lee; Tae-Hwan Kim; Paul Mizen
  6. Season. A Mathematica Package for Seasonal Adjustment By Schlicht, Ekkehart
  7. Functional Principal Component Analysis of Cointegrated Functional Time Series By Won-Ki Seo

  1. By: Frédérique Bec; Alain Guay (Université de Cergy-Pontoise, THEMA)
    Abstract: This paper proposes t-like unit root tests which are consistent against any stationary alternatives, nonlinear or noncausal ones included. It departs from existing tests in that it uses an unbounded grid set including all possible values taken by the series. In our setup, thanks to the very simple nonlinear stationary alternative specification and the particular choice of the thresholds set, the proposed unit root test contains the standard ADF test as a special case. This, in turn, yields a sufficient condition for consistency against any ergodic stationary alternative. From a Monte-Carlo study, it turns out that the power of our unbounded non adaptive tests, in their average and exponential versions, outperforms existing bounded tests, either adaptive or not. This is illustrated by an application to interest rate spread data.
    Keywords: Unit root test, Threshold autoregressive model, Interest rate spread.
    JEL: C12 C22 C32 E43
    Date: 2020
  2. By: Gabor Katay (European Commission – JRC); Lisa Kerdelhué (Banque de France, Aix-Marseille Université); Matthieu Lequien (Institut National de la Statistique et des Études Économiques (INSEE), Paris School of Economics)
    Abstract: The paper assesses the impact of adding information on financial cycles on the output gap estimates for eight advanced economies using two unobserved components models: a reduced form extended Hodrick-Prescott filter, and a standard semi-structural unobserved components model. To complement these models, a semi-structural vector autoregression model is proposed in which only supply shocks are identified. The accuracy of the output gap estimates is assessed based on their performance in predicting recessions. The models with financial variables generally produce more accurate output gap estimates at the expense of increased real-time volatility. While the extended Hodrick-Prescott filter is particularly appealing for its real-time stability, it lags behind the two semi-structural models in terms of forecasting performance. The vector autoregression model augmented with financial variables features the best in-sample forecasting performance, and it has similar real-time prediction capabilities to the semi-structural unobserved components model. Overall, financial cycles appear to be relevant in Japan, Spain, the UK, and – to a lesser extent – in the US and in France, while they are relatively muted in Canada, Germany, and Italy.
    Keywords: unobserved components model, semi-structural VAR, output gap, financial cycle, sustainable growth, credit, house prices, advanced economies
    JEL: C32 E32 E44 G01 O11 O16
    Date: 2020–11
  3. By: Zhaoxing Gao; Ruey S. Tsay
    Abstract: We propose a new framework for modeling high-dimensional matrix-variate time series by a two-way transformation, where the transformed data consist of a matrix-variate factor process, which is dynamically dependent, and three other blocks of white noises. Specifically, for a given $p_1\times p_2$ matrix-variate time series, we seek common nonsingular transformations to project the rows and columns onto another $p_1$ and $p_2$ directions according to the strength of the dynamic dependence of the series on the past values. Consequently, we treat the data as nonsingular linear row and column transformations of dynamically dependent common factors and white noise idiosyncratic components. We propose a common orthonormal projection method to estimate the front and back loading matrices of the matrix-variate factors. Under the setting that the largest eigenvalues of the covariance of the vectorized idiosyncratic term diverge for large $p_1$ and $p_2$, we introduce a two-way projected Principal Component Analysis (PCA) to estimate the associated loading matrices of the idiosyncratic terms to mitigate such diverging noise effects. A diagonal-path white noise testing procedure is proposed to estimate the order of the factor matrix. %under the assumption that the idiosyncratic term is a matrix-variate white noise process. Asymptotic properties of the proposed method are established for both fixed and diverging dimensions as the sample size increases to infinity. We use simulated and real examples to assess the performance of the proposed method. We also compare our method with some existing ones in the literature and find that the proposed approach not only provides interpretable results but also performs well in out-of-sample forecasting.
    Date: 2020–11
  4. By: Naftali Cohen; Srijan Sood; Zhen Zeng; Tucker Balch; Manuela Veloso
    Abstract: Time series forecasting is essential for agents to make decisions in many domains. Existing models rely on classical statistical methods to predict future values based on previously observed numerical information. Yet, practitioners often rely on visualizations such as charts and plots to reason about their predictions. Inspired by the end-users, we re-imagine the topic by creating a framework to produce visual forecasts, similar to the way humans intuitively do. In this work, we take a novel approach by leveraging advances in deep learning to extend the field of time series forecasting to a visual setting. We do this by transforming the numerical analysis problem into the computer vision domain. Using visualizations of time series data as input, we train a convolutional autoencoder to produce corresponding visual forecasts. We examine various synthetic and real datasets with diverse degrees of complexity. Our experiments show that visual forecasting is effective for cyclic data but somewhat less for irregular data such as stock price. Importantly, we find the proposed visual forecasting method to outperform numerical baselines. We attribute the success of the visual forecasting approach to the fact that we convert the continuous numerical regression problem into a discrete domain with quantization of the continuous target signal into pixel space.
    Date: 2020–11
  5. By: Dong Jin Lee; Tae-Hwan Kim; Paul Mizen
    Abstract: This paper presents a new method to analyse the effect of shocks on time series using a quantile impulse response function (QIRF). While conventional impulse response analysis is restricted to evaluation using the conditional mean function, here, we propose an alternative impulse response analysis that traces the effect of economic shocks on the conditional quantile function. By changing the quantile index over the unit interval, it is possible to measure the effects of shocks on the entire conditional distribution of a variable of interest in our framework. Therefore, we can observe the complete distributional consequences of policy interventions, especially at the upper and lower tails of the distribution as well as the mean. Using the new approach, it becomes possible to evaluate two distinct features (called "distributional effects"): (i) a change in the dispersion of the conditional distribution of interest is changed after a shock, and (ii) a change in the degree of skewness of the conditional distribution caused by a policy intervention. None of these features can be observed in the conventional impulse response analysis exclusively based on the conditional mean function. In addition to proposing the QIRF, our second contribution is to present a new way to jointly estimate a system of multiple quantile functions. Our proposal system quantile estimator is obtained by extending the result of Jun and Pinkse (2009) to the time series context. We illustrate the QIFR on a VAR model in a manner similar to Romer and Romer (2004) in order to assess the impact of a monetary policy shock on the US economy.
    Keywords: quantile vector autoregression, monetary policy shock, quantile impulse response function, structural vector autoregression
    Date: 2020
  6. By: Schlicht, Ekkehart
    Abstract: This Zip-Archive provides a Mathematica package and documentation for the seasonal adjustment method proposed by Schlicht and Pauly (1983) and Schlicht (1984) covering Mathematica versions 6 and up. The method makes use of non-parametric splines. It decomposes a time series into a trend, a seasonal component, and an irregular component. The method combines the trend filter proposed by Leser (1961) (also known as the HP-Filter), the seasonal filter proposed by Schlicht and Pauly (1983) and the orthogonal parametrization proposed by Schlicht (1984). In contrast to prevailing methods, it is based on an explicit statistical model (state-space) and estimates the smoothing parameters by a maximum-likelihood method.
    Keywords: Seasonal adjustment; flexible seasonal adjustment; splines; non-parametric splines; state-space; HP filter; Mathematica; penalized öleast squares
    JEL: C01 C14 C22
    Date: 2020–11
  7. By: Won-Ki Seo
    Abstract: Functional principal component analysis (FPCA) has played an important role in the development of functional time series (FTS) analysis. This paper investigates how FPCA can be used to analyze cointegrated functional time series and propose a modification of FPCA as a novel statistical tool. Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel KPSS-type tests for examining some essential properties of cointegrated time series. As an empirical illustration, our methodology is applied to the time series of log-earning densities.
    Date: 2020–11

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.