nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒09‒30
six papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Robust Multivariate Local Whittle Estimation and Spurious Fractional Cointegration By Becker, Janis; Leschinski, Christian; Sibbertsen, Philipp
  2. Asymptotic post-selection inference for Akaike’s information criterion By Ali Charkhi; Gerda Claeskens
  3. A New Adaptive Moving Average (Vama) Technical Indicator For Financial Data Smoothing By Pierrefeu, Alex
  4. Proxy-SVAR as a Bridge for Identification with Higher Frequency Data By Andrea Giovanni Gazzani; Alejandro Vicondoa
  5. Turning point and oscillatory cycles By Mariano Kulish; Adrian Pagan
  6. Steady-state growth By Emanuel Kohlscheen; Jouchi Nakajima

  1. By: Becker, Janis; Leschinski, Christian; Sibbertsen, Philipp
    Abstract: This paper derives a multivariate local Whittle estimator for the memory parameter of a possibly long memory process and the fractional cointegration vector robust to low frequency contaminations. This estimator as many other local Whittle based procedures requires a priori knowledge of the cointegration rank. Since low frequency contaminations bias inference on the cointegration rank, we also provide a robust estimator of the cointegration rank. As both estimators are based on the trimmed periodogram we further derive some insights in the behaviour of the periodogram of a process under very general types of low frequency contaminations. An extensive Monte Carlo exercise shows the applicability of our estimators in finite samples. Our procedures are applied to realized betas of two American energy companies discovering that the series are fractionally cointegrated. As the series exhibit low frequency contaminations, standard procedures are unable to detect this relation.
    Keywords: Multivariate Long Memory; Fractional Cointegration; Random Level Shifts; Semiparametric Estimation
    JEL: C13 C32
    Date: 2019–09
  2. By: Ali Charkhi; Gerda Claeskens
    Abstract: Ignoring the model selection step in inference after selection is harmful. This paper studies the asymptotic distribution of estimators after model selection using the Akaike information criterion. First, we consider the classical setting in which a true model exists and is included in the candidate set of models. We exploit the overselection property of this criterion in the construction of a selection region, and obtain the asymptotic distribution of estimators and linear combinations thereof conditional on the selected model. The limiting distribution depends on the set of competitive models and on the smallest overparameterized model. Second, we relax the assumption about the existence of a true model, and obtain uniform asymptotic results. We use simulation to study the resulting postselection distributions and to calculate confidence regions for the model parameters. We apply the method to data.
    Keywords: Akaike information criterion, Confidence region, Likelihood model, Model selection, post-selection inference
    Date: 2018–02
  3. By: Pierrefeu, Alex
    Abstract: The separation of the trend from random fluctuations (noise) is a major objective in technical analysis and for a long time two commons filters, the simple moving average and the exponential moving average have been used to achieve this goal, those two filters use one parameter to control this degree of separation, higher degree of separation involve smoother results but also more lag. Lag is defined as the effect of a moving average to show past trends instead of new ones, this effect his unavoidable with causal filters and is a major drawback in decision timing . In this article I will introduce a new adaptive moving average technical indicator (VAMA) who aim to provide smooth results as well as providing fast decision timing. This new method will be used for the construction of a simple MA crossover strategy in EURUSD, the results of this strategy will then be compared to the results of the same strategy using other adaptive moving averages to provide a comparison of the profitability of this indicator.
    Keywords: Moving Average · Adaptive Moving Average · Smoothing · Filters · Technical indicator · Technical Analysis · Volatility
    JEL: G00 G17
    Date: 2019–05–30
  4. By: Andrea Giovanni Gazzani (Bank of Italy); Alejandro Vicondoa (Pontificia Universidad Catolica de Chile)
    Abstract: High frequency identification around key events has recently solved many puzzles in empirical macroeconomics. This paper proposes a novel methodology, the Bridge Proxy-SVAR, to identify structural shocks in Vector Autoregressions (VARs) by exploiting high frequency information in a more general framework. Our methodology comprises three steps: (I) identify the structural shocks of interest in high frequency systems; (II) aggregate the series of high frequency shocks at a lower frequency employing the correct filter; (III) use the aggregated series of shocks as a proxy for the corresponding structural shock in lower frequency VARs. Both analytically and through simulations, we show that our methodology significantly improves the identification of VARs, recovering the true impact effect. In a first empirical application on US data, we show that financial shocks identified at daily frequency produce unambiguously macroeconomic effects consistent with a demand shock. In a second application, we identify U.S. monetary policy shocks that are highly correlated with the series of monetary policy surprises but, contrary to the latter ones, are invertible and so valid external instruments for low-frequency VARs.
    Date: 2019
  5. By: Mariano Kulish; Adrian Pagan
    Abstract: The early history of cycles research involved locating turning points in the data. Later, the development of methods such as spectral analysis led to a focus on oscillations. A distinction between cycles and oscillations is needed - both imply turning points, but turning points do not necessarily imply oscillations. Comin and Gertler (2006) argue that attention should be paid to medium term oscillations of 8-30 years rather than the standard 2-8 years of the business cycle, while Beaudry et al. (2019) suggest that there is a cycle of 9/10 years in series such as hours per capita. We investigate what the evidence is for the latter and find that it explains little of the variance of the data. We then show that some of the fillters being used to locate either turning points or oscillations in the series are not appropriate for the nature of the series being analyzed, specifically whether they are I(1) or I(0). Finally, we assess if the concepts of medium term and 9/10 year cycles are useful for comparing models and data. This is done by examining models of endogenous versus exogenous technology as well as limit cycles due to accumulation and complementarity mechanisms.
    Keywords: Medium-term cycles, band-pass filter, business cycles
    JEL: C52 E32
    Date: 2019–09
  6. By: Emanuel Kohlscheen; Jouchi Nakajima
    Abstract: We compute steady-state economic growth - defined as the rate of growth that the economy would converge to in the absence of new shocks. This rate can be computed in real-time by means of a parsimonious time-varying parameter (TVP) VAR model. Our procedure offers a relatively agnostic estimation of benchmark equilibrium growth rates. Estimates show that the steady-state GDP growth rate in the case of the United States declined from just above 3% per year in the 1990s to 2.4% at present. Results for other six advanced economies and the euro area indicate that the steady-state growth rate, which is consistent with stable inflation and financial conditions, has been relatively stable since 2010 in most cases in spite of a recent slowdown in actual GDP growth rates.
    Keywords: economic growth, financial conditions, inflation, monetary policy, potential output, time-varying
    JEL: C11 C15 E30 O40
    Date: 2019–09

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