nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒02‒06
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Imputing Monthly Values for Quarterly Time Series. An Application Performed with Swiss Business Cycle Data By Klaus Abberger; Michael Graff; Oliver Müller; Boriss Siliverstovs
  2. Bayesian VARs and prior calibration in times of COVID-19 By Hartwig, Benny
  3. Symbolic Stationarization of Dynamic Equilibrium Models By Fabio Canova; Kenneth Sæterhagen Paulsen
  4. Statistical Properties of Two Asymmetric Stochastic Volatility in Mean Models By Antonis Demos
  5. Quantile Autoregression-based Non-causality Testing By Weifeng Jin
  6. Reflections on "Testing for Unit Roots in Heterogeneous Panels" By Im, K S.; Pesaran, M. H.; Shin, Y.
  7. Time-Varying Coefficient DAR Model and Stability Measures for Stablecoin Prices: An Application to Tether By Antoine Djobenou; Emre Inan; Joann Jasiak
  8. Moments, Shocks and Spillovers in Markov-switching VAR Models By Erik Kole; Dick van Dijk
  9. Identifying Monetary Policy Shocks Through External Variable Constraints By Francesco Fusari
  10. Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves By Xiaohong Chen; Yuan Liao; Weichen Wang
  11. Ensemble MCMC sampling for robust Bayesian inference By Böhl, Gregor

  1. By: Klaus Abberger; Michael Graff; Oliver Müller; Boriss Siliverstovs
    Abstract: This paper documents a comparative application of algorithms to deal with the problem of missing values in higher frequency data sets. We refer to Swiss business tendency survey (BTS) data which are conducted in both monthly and quarterly frequency, where an information sub-set is collected at quarterly frequency only. This occurs in many countries, for example, the harmonised survey programme of the European Union also has this frequency pattern. There is a wide range of ways to address this problem, comprising univariate and multivariate approaches. To evaluate the suitability of the different approaches, we apply them to series that are artificially quarterly, i.e., de facto monthly, from which we create quarterly data by deleting two out of three data points from each quarter. The target series for imputation of missing (deleted) observations comprise the set of time series from the monthly KOF manufacturing BTS survey. At the same time, theses series are ideal to deliver higher frequency information for multivariate imputation algorithms, as they share a common theme, the Swiss business cycle. With this set of indicators, we conduct the different imputations. On this basis, we then run standard tests of forecasting accuracy by comparing the imputed monthly series to the original monthly series. Finally, we take a look at the congruence of the imputed monthly series from the quarterly survey question on firms’ technical capacities with existing monthly data on the Swiss economy. The results show that for our data corpus, algorithms based on the approach suggested by Chow and Lin deliver the most precise imputations, followed by multiple OLS regressions.
    Keywords: temporal disaggregation, business tendency surveys, out-of-sample validation, mixed-frequency data
    JEL: C19 C22 C53
    Date: 2022
  2. By: Hartwig, Benny
    Abstract: This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a rare event rather than a persistent increase in macroeconomic volatility. For forecasting, the choice among outlier-robust error structures is less important, however, when a large cross-section of information is used. Besides the error structure, this paper shows that the standard Minnesota prior calibration is an important source of changing macroeconomic transmission channels during the pandemic, altering the predictability of real and nominal variables. To alleviate this sensitivity, an outlier-robust prior calibration is proposed.
    Keywords: forecasting, multivariate t errors, common time-varying volatility, outlier-robust prior calibration
    JEL: C11 C51 C53
    Date: 2022
  3. By: Fabio Canova; Kenneth Sæterhagen Paulsen
    Abstract: Dynamic equilibrium models are specified to track time series with unit root-like behavior. Thus, unit roots are typically introduced and the optimality conditions adjusted. This step requires tedious algebra and often leads to algebraic mistakes, especially in models with several unit roots. We propose a symbolic algorithm that simplies the step of rendering non-stationary models stationary. It is easy to implement and works when trends are stochastic or deterministic, exogenous or endogenous. Three examples illustrate the mechanics and the properties of the approach. A comparison with existing methods is provided.
    Keywords: DSGE models, unit roots, endogenous growth, symbolic computation
    Date: 2021–12–20
  4. By: Antonis Demos (
    Abstract: Here we investigate the statistical properties of two normal asymmetric SV models with possibly time varying risk premia. In fact, we investigate two popular autoregressive stochastic volatility specifications. These, although they seem very similar, it turns out, that they possess quite different statistical properties. The derived properties can be employed to develop tests or to check stationarity of various orders, something important for the asymptotic properties of various estimators.
    Date: 2023–01–19
  5. By: Weifeng Jin
    Abstract: Non-causal processes have been drawing attention recently in Macroeconomics and Finance for their ability to display nonlinear behaviors such as asymmetric dynamics, clustering volatility, and local explosiveness. In this paper, we investigate the statistical properties of empirical conditional quantiles of non-causal processes. Specifically, we show that the quantile autoregression (QAR) estimates for non-causal processes do not remain constant across different quantiles in contrast to their causal counterparts. Furthermore, we demonstrate that non-causal autoregressive processes admit nonlinear representations for conditional quantiles given past observations. Exploiting these properties, we propose three novel testing strategies of non-causality for non-Gaussian processes within the QAR framework. The tests are constructed either by verifying the constancy of the slope coefficients or by applying a misspecification test of the linear QAR model over different quantiles of the process. Some numerical experiments are included to examine the finite sample performance of the testing strategies, where we compare different specification tests for dynamic quantiles with the Kolmogorov-Smirnov constancy test. The new methodology is applied to some time series from financial markets to investigate the presence of speculative bubbles. The extension of the approach based on the specification tests to AR processes driven by innovations with heteroskedasticity is studied through simulations. The performance of QAR estimates of non-causal processes at extreme quantiles is also explored.
    Date: 2023–01
  6. By: Im, K S.; Pesaran, M. H.; Shin, Y.
    Abstract: This article is our personal perspective on the IPS test and the subsequent developments of unit root and cointegration tests in dynamic panels with and without cross-section dependence. In this note, we discuss the main idea behind the test and the publication process that led to Im, Pesaran and Shin (2003).
    Keywords: Dickey and Fuller statistic, stationarity, panel unit root tests, prevalence of unit roots.
    JEL: C01 C23
    Date: 2023–01–11
  7. By: Antoine Djobenou; Emre Inan; Joann Jasiak
    Abstract: This paper examines the dynamics of Tether, the stablecoin with the largest market capitalization. We show that the distributional and dynamic properties of Tether/USD rates have been evolving from 2017 to 2021. We use local analysis methods to detect and describe the local patterns, such as short-lived trends, time-varying volatility and persistence. To accommodate these patterns, we consider a time varying parameter Double Autoregressive tvDAR(1) model under the assumption of local stationarity of Tether/USD rates. We estimate the tvDAR model non-parametrically and test hypotheses on the functional parameters. In the application to Tether, the model provides a good fit and reliable out-of-sample forecasts at short horizons, while being robust to time-varying persistence and volatility. In addition, the model yields a simple plug-in measure of stability for Tether and other stablecoins for assessing and comparing their stability.
    Date: 2023–01
  8. By: Erik Kole (Erasmus University Rotterdam); Dick van Dijk (Erasmus University Rotterdam)
    Abstract: To investigate how economies, financial markets or institutions can deal with stress, we often analyze the effects of shocks conditional on a recession or a bear market. MSVAR models are perfectly suited for such analyses because they combine gradual movements with sudden switches. In this paper, we develop a comprehensive methodology to conduct these analyses. We derive first and second moments conditional only on the regime distribution and propose impulse response functions for both moments. By formulating the MSVAR as an extended linear non-Gaussian VAR, all results are in closed-form. We illustrate our methods with an application to stock and bond return predictability. We show how forecasts of means, volatilities and (auto-)correlations depend on the regimes. The effect of shocks becomes highly nonlinear, and they propagate via different channels. During bear markets, shocks have stronger e?ects on means and volatilities and die out more slowly.
    Keywords: Markov-switching VAR, moments, impulse response analysis, bull and bear markets; Markov-switching, VAR, moments, impulse response analysis, bull and bear markets
    JEL: C32 C58 G01 G17
    Date: 2022–04–25
  9. By: Francesco Fusari (University of Surrey)
    Abstract: This paper proposes a new strategy for the identification of monetary policy shocks in structural vector autoregressions (SVARs). It combines traditional sign restrictions with external variable constraints on high-frequency monetary surprises and central bank’s macroeconomic projections. I use it to characterize the transmission of US monetary policy over the period 1965-2007. First, I find that contractionary monetary policy shocks unequivocally decrease output, sharpening the ambiguous implications of standard sign-restricted SVARs. Second, I show that the identified structural models are consistent with narrative sign restrictions and restrictions on the monetary policy equation. On the contrary, the shocks identified through these alternative methodologies turn out to be correlated with the information set of the central bank and to weakly comove with monetary surprises. Finally, I implement an algorithm for robust Bayesian inference in set-identified SVARs, providing further evidence in support of my identification strategy.
    JEL: E52 C51
    Date: 2023–01
  10. By: Xiaohong Chen; Yuan Liao; Weichen Wang
    Abstract: General nonlinear sieve learnings are classes of nonlinear sieves that can approximate nonlinear functions of high dimensional variables much more flexibly than various linear sieves (or series). This paper considers general nonlinear sieve quasi-likelihood ratio (GN-QLR) based inference on expectation functionals of time series data, where the functionals of interest are based on some nonparametric function that satisfy conditional moment restrictions and are learned using multilayer neural networks. While the asymptotic normality of the estimated functionals depends on some unknown Riesz representer of the functional space, we show that the optimally weighted GN-QLR statistic is asymptotically Chi-square distributed, regardless whether the expectation functional is regular (root-$n$ estimable) or not. This holds when the data are weakly dependent beta-mixing condition. We apply our method to the off-policy evaluation in reinforcement learning, by formulating the Bellman equation into the conditional moment restriction framework, so that we can make inference about the state-specific value functional using the proposed GN-QLR method with time series data. In addition, estimating the averaged partial means and averaged partial derivatives of nonparametric instrumental variables and quantile IV models are also presented as leading examples. Finally, a Monte Carlo study shows the finite sample performance of the procedure
    Date: 2022–12
  11. By: Böhl, Gregor
    Abstract: This paper proposes a Differential-Independence Mixture Ensemble (DIME) sampler for the Bayesian estimation of macroeconomic models. It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may also be computationally expensive to evaluate. DIME is a "Swiss Army knife", combining the advantages of a broad class of gradient-free global multi-start optimizers with the properties of a Monte Carlo Markov chain. This includes (i) fast burn-in and convergence absent any prior numerical optimization or initial guesses, (ii) good performance for multimodal distributions, (iii) a large number of chains (the "ensemble") running in parallel, (iv) an endogenous proposal density generated from the state of the full ensemble, which (v) respects the bounds of the prior distribution. I show that the number of parallel chains scales well with the number of necessary ensemble iterations. DIME is used to estimate the medium-scale heterogeneous agent New Keynesian ("HANK") model with liquid and illiquid assets, thereby for the first time allowing to also include the households' preference parameters. The results mildly point towards a less accentuated role of household heterogeneity for the empirical macroeconomic dynamics.
    Keywords: Bayesian Estimation, Monte Carlo Methods, Heterogeneous Agents, Global Optimization, Swiss Army Knife
    JEL: C11 C13 C15 E10
    Date: 2022

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