nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒12‒12
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Robust Testing for Explosive Behavior with Strongly Dependent Errors By Lui, Yiu Lim; Phillips, Peter C.B.; Yu, Jun
  2. Sequential unit root test for first-order autoregressive processes with initial values By Jianwei Jin; Keiji Nagai
  3. Robust estimation for Threshold Autoregressive Moving-Average models By Greta Goracci; Davide Ferrari; Simone Giannerini; Francesco ravazzolo
  4. Unit root tests considering initial values and a concise method for computing powers By Kohtaro Hitomi; Jianwei Jin; Keiji Nagai; Yoshihiko Nishiyama; Junfan Tao
  5. Macroeconomic Responses to Uncertainty Shocks: The Perils of Recursive Orderings By Lutz Kilian; Michael D. Plante; Alexander W. Richter
  6. Cointegration with Occasionally Binding Constraints By James A. Duffy; Sophocles Mavroeidis; Sam Wycherley
  7. Density and Risk Prediction with Non-Gaussian COMFORT Models By Marc S. Paolella; Pawel Polak
  8. A solution to the global identification problem in DSGE models By Andrzej Kocięcki; Marcin Kolasa

  1. By: Lui, Yiu Lim (Dongbei University of Finance and Economics); Phillips, Peter C.B. (Yale University); Yu, Jun (Singapore Management University)
    Abstract: A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite sample performance of the proposed test and estimators. An empirical application to the S&P 500 index highlights the usefulness of the proposed procedures in practical work.
    Keywords: HAR test; Long memory; Explosiveness; Unit root test; S&P 500
    JEL: C12 C22 G01
    Date: 2022–10–28
  2. By: Jianwei Jin (Yokohama National University); Keiji Nagai (Yokohama National University)
    Abstract: This paper examines the effect of initial values and small-sample properties in sequential unit root tests of the first-order autoregressive (AR(1)) process with a coefficient expressed by a local parameter. Adopting a stopping rule based on observed Fisher information defined by Lai and Siegmund (1983), we use the sequential least squares estimator (LSE) of the local parameter as the test statistic. The sequential LSE is represented as a time-changed Brownian motion with drift. The stopping time is written as the integral of the reciprocal of twice of a Bessel process with drift generated by the time-changed Brownian motion. The time change is applied to the joint density and joint Laplace transform derived from the Bessel bridge of the squared Bessel process by Pitman and Yor (1982), by which we derive the limiting joint density and joint Laplace transform for the sequential LSE and stopping time. The joint Laplace transform is needed to calculate joint moments because the joint density oscillates wildly as the value of the stopping time approaches zero. Moreover, this paper also earns the exact distribution of stopping time by Imhof's formula for both normally distributed and fixed initial values. When the autoregressive coefficient is less than 1, the question arises as to whether the local-to-unity or the strong stationary model should be used. We make the decision by comparing joint moments for respective models with those calculated from the exact distribution or simulations.
    Keywords: Stopping time, observed Fisher information, DDS Brownian motion, local asymptotic normality, Bessel process, initial values, exact distributions
    JEL: C12 C22 C46
    Date: 2022–11
  3. By: Greta Goracci; Davide Ferrari; Simone Giannerini; Francesco ravazzolo
    Abstract: Threshold autoregressive moving-average (TARMA) models are popular in time series analysis due to their ability to parsimoniously describe several complex dynamical features. However, neither theory nor estimation methods are currently available when the data present heavy tails or anomalous observations, which is often the case in applications. In this paper, we provide the first theoretical framework for robust M-estimation for TARMA models and also study its practical relevance. Under mild conditions, we show that the robust estimator for the threshold parameter is super-consistent, while the estimators for autoregressive and moving-average parameters are strongly consistent and asymptotically normal. The Monte Carlo study shows that the M-estimator is superior, in terms of both bias and variance, to the least squares estimator, which can be heavily affected by outliers. The findings suggest that robust M-estimation should be generally preferred to the least squares method. Finally, we apply our methodology to a set of commodity price time series; the robust TARMA fit presents smaller standard errors and leads to superior forecasting accuracy compared to the least squares fit. The results support the hypothesis of a two-regime, asymmetric nonlinearity around zero, characterised by slow expansions and fast contractions.
    Date: 2022–11
  4. By: Kohtaro Hitomi (Kyoto Institute of Technology); Jianwei Jin (Yokohama National University); Keiji Nagai (Yokohama National University); Yoshihiko Nishiyama (Institute of Economic Research, Kyoto University); Junfan Tao (Institute of Economic Research, Kyoto University)
    Abstract: The Dickey-Fuller (DF) unit root tests are widely used in empirical studies on economics. In the local-to-unity asymptotic theory, the effects of initial values vanish as the sample size grows. However, for a small sample size, the initial value will affect the distribution of the test statistics. When ignoring the effect of the initial value, the left-sided unit root test sets the critical value smaller than it should be. Therefore, the size and power of the test become smaller. This paper investigates the effect of the initial value for the DF test (including the t test). Limiting approximations of the DF test statistics are the ratios of two integrals which are represented via a one-dimensional squared Bessel process. We derive the joint density of the squared Bessel process and its integral, enabling us to compute this ratio's distribution. For independent normal errors, the exact distribution of the Dickey-Fuller coefficient test statistic is obtained using the Imhof (1961) method for non-central chi-squared distribution. Numerical results show that when the sample size is small, the limiting distributions of the DF test statistics with initial values fit well with the exact or simulated distributions. We transform the DF test with respect to a local parameter into the test for a shift in the location parameter of normal distributions. As a result, a concise method for computing the powers of DF tests is derived.
    Keywords: Dickey-Fuller tests, Squared Bessel process, joint density, powers approximated by normal distribution, exact distribution
    JEL: C12 C22 C46
    Date: 2022–11
  5. By: Lutz Kilian; Michael D. Plante; Alexander W. Richter
    Abstract: A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autoregressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on economic activity. We prove by counterexample that this practice is invalid in general, whether the data generating process is a structural VAR model or a dynamic stochastic general equilibrium model.
    Keywords: Cholesky Decomposition; endogeneity; uncertainty; business cycle
    JEL: C32 C51 E32
    Date: 2022–11–23
  6. By: James A. Duffy; Sophocles Mavroeidis; Sam Wycherley
    Abstract: In the literature on nonlinear cointegration, a long-standing open problem relates to how a (nonlinear) vector autoregression, which provides a unified description of the short- and long-run dynamics of a collection of time series, can generate 'nonlinear cointegration' in the profound sense of those series sharing common nonlinear stochastic trends. We consider this problem in the setting of the censored and kinked structural VAR (CKSVAR), which provides a flexible yet tractable framework within which to model time series that are subject to threshold-type nonlinearities, such as those arising due to occasionally binding constraints, of which the zero lower bound (ZLB) on short-term nominal interest rates provides a leading example. We provide a complete characterisation of how common linear and nonlinear stochastic trends may be generated in this model, via unit roots and appropriate generalisations of the usual rank conditions, providing the first extension to date of the Granger-Johansen representation theorem from a linear to a nonlinear setting, and thereby giving the first successful treatment of the open problem. The limiting common trend processes include regulated, censored and kinked Brownian motions, none of which have previously appeared in the literature on cointegrated VARs. Our results and running examples illustrate that the CKSVAR is capable of supporting a far richer variety of long-run behaviour than is a linear VAR, in ways that may be particularly useful for the identification of structural parameters. En route to establishing our main results, we also develop a set of sufficient conditions for the processes generated by a CKSVAR to be stationary, ergodic, and weakly dependent.
    Date: 2022–11
  7. By: Marc S. Paolella (University of Zurich - Department of Banking and Finance; Swiss Finance Institute); Pawel Polak (Stony Brook University-Department of Applied Mathematics and Statistics)
    Abstract: The CCC-GARCH model, and its dynamic correlation extensions, form the most important model class for multivariate asset returns. For multivariate density and portfolio risk forecasting, a drawback of these models is the underlying assumption of Gaussianity. This paper considers the so-called COMFORT model class, which is the CCC-GARCH model but endowed with multivariate generalized hyperbolic innovations. The novelty of the model is that parameter estimation is conducted by joint maximum likelihood, of all model parameters, using an EM algorithm, and so is feasible for hundreds of assets. This paper demonstrates that (i) the new model is blatantly superior to its Gaussian counterpart in terms of forecasting ability, and (ii) also outperforms ad-hoc three step procedures common in the literature to augment the CCC and DCC models with a fat-tailed distribution. An extensive empirical study confirms the COMFORT model’s superiority in terms of multivariate density and Value-at-Risk forecasting.
    Keywords: GJR-GARCH, Multivariate Generalized Hyperbolic Distribution, Non-Ellipticity, Value-at-Risk.
    JEL: C51 C53 G11 G17
    Date: 2022–11
  8. By: Andrzej Kocięcki (University of Warsaw, Faculty of Economic Sciences); Marcin Kolasa (SGH Warsaw School of Economics; International Monetary Fund)
    Abstract: We develop an analytical framework to study global identification in structural models with forward-looking expectations. Our identification condition combines the similarity transformation linking the observationally equivalent state space systems with the constraints imposed on them by the model parameters. The key step of solving the identification problem then reduces to finding all roots of a system of polynomial equations. We show how it can be done using the concept of a Gröbner basis and recently developed algorithms to compute it analytically. In contrast to papers relying on numerical search, our approach can effectively prove whether a model is identified or not at the given parameter point, explicitly delivering the complete set of observationally equivalent parameter vectors. We present the solution to the global identification problem for several popular DSGE models. Our findings indicate that observational equivalence in medium-sized models of this class might be actually not as widespread as suggested by earlier, small model-based evidence.
    Keywords: global identification, state space systems, DSGE models, Gröbner basis
    JEL: C10 C51 C65 E32
    Date: 2022

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