nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒05‒22
seven papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. Determination of the effective cointegration rank in high-dimensional time-series predictive regressions By Puyi Fang; Zhaoxing Gao; Ruey S. Tsay
  2. Bayesian Predictive Distributions of Oil Returns Using Mixed Data Sampling Volatility Models By Virbickaite, Audrone; Nguyen, Hoang; Tran, Minh-Ngoc
  3. Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series By Jarek Duda
  4. Averaging Impulse Responses Using Prediction Pools By Paul Ho; Thomas A. Lubik; Christian Matthes
  5. Long memory, fractional integration and cointegration analysis of real convergence in Spain By Mariam Kamal; Josu Arteche
  6. Breaks in the Phillips Curve: Evidence from Panel Data By Simon Smith; Allan Timmermann; Jonathan H. Wright
  7. The Evolution of the Natural Rate of Interest – Evidence from the Scandinavian Countries By Armelius, Hanna; Solberger, Martin; Spånberg, Erik; Österholm, Pär

  1. By: Puyi Fang; Zhaoxing Gao; Ruey S. Tsay
    Abstract: This paper proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank regression. For a HDUR process $\mathbf{x}_t\in \mathbb{R}^N$ and a stationary series $\mathbf{y}_t\in \mathbb{R}^p$ of interest, our goal is to predict future values of $\mathbf{y}_t$ using $\mathbf{x}_t$ and lagged values of $\mathbf{y}_t$. The proposed framework consists of a two-step estimation procedure. First, the Principal Component Analysis is used to identify all cointegrating vectors of $\mathbf{x}_t$. Second, the co-integrated stationary series are used as regressors, together with some lagged variables of $\mathbf{y}_t$, to predict $\mathbf{y}_t$. The estimated reduced rank is then defined as the effective cointegration rank of $\mathbf{x}_t$. Under the scenario that the autoregressive coefficient matrices are sparse (or of low-rank), we apply the Least Absolute Shrinkage and Selection Operator (or the reduced-rank techniques) to estimate the autoregressive coefficients when the dimension involved is high. Theoretical properties of the estimators are established under the assumptions that the dimensions $p$ and $N$ and the sample size $T \to \infty$. Both simulated and real examples are used to illustrate the proposed framework, and the empirical application suggests that the proposed procedure fares well in predicting stock returns.
    Date: 2023–04
  2. By: Virbickaite, Audrone (CUNEF Universidad); Nguyen, Hoang (Örebro University School of Business); Tran, Minh-Ngoc (Discipline of Business Analytics, The University of Sydney Business School)
    Abstract: This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative Bayesian estimation approach called the density-tempered sequential Monte Carlo method. Our findings indicate that the inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV-type models. Notably, GAS-family models exhibit superior performance in terms of in-sample fit, out-of-sample forecast accuracy, as well as Value-at-Risk and Expected Shortfall prediction.
    Keywords: ES; GARCH; GAS; log marginal likelihood; MIDAS; SV; VaR
    JEL: C22 C52 C58 G32
    Date: 2023–04–14
  3. By: Jarek Duda
    Abstract: The real life time series are usually nonstationary, bringing a difficult question of model adaptation. Classical approaches like GARCH assume arbitrary type of dependence. To prevent such bias, we will focus on recently proposed agnostic philosophy of moving estimator: in time $t$ finding parameters optimizing e.g. $F_t=\sum_{\tau
    Date: 2023–04
  4. By: Paul Ho; Thomas A. Lubik; Christian Matthes
    Abstract: Macroeconomists construct impulse responses using many competing time series models and different statistical paradigms (Bayesian or frequentist). We adapt optimal linear prediction pools to efficiently combine impulse response estimators for the effects of the same economic shock from this vast class of possible models. We thus alleviate the need to choose one specific model, obtaining weights that are typically positive for more than one model. Three Monte Carlo simulations and two monetary shock empirical applications illustrate how the weights leverage the strengths of each model by (i) trading off properties of each model depending on variable, horizon, and application and (ii) accounting for the full predictive distribution rather than being restricted to specific moments.
    Keywords: prediction pools; model averaging; impulse responses; misspecification
    JEL: C32 C52
    Date: 2023–02
  5. By: Mariam Kamal; Josu Arteche
    Abstract: This paper investigates economic convergence in terms of real income per capita among the autonomous regions of Spain. In order to converge, the series should cointegrate. This necessary condition is checked using two testing strategies recently proposed for fractional cointegration, finding no evidence of cointegration, which rules out the possibility of convergence between all or some of the Spanish regions. As an additional contribution, an extension of the critical values of one of the tests of fractional cointegration is provided for a different number of variables and sample sizes from those originally provided by the author, fitting those considered in this paper.
    Date: 2023–04
  6. By: Simon Smith; Allan Timmermann; Jonathan H. Wright
    Abstract: We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns of instability and to examine lead-lag patterns in how individual inflation series change. We find evidence of a marked flattening in the Phillips curves for US sectoral data and among EU countries, particularly poorer ones. Conversely, evidence of a flattening is weaker for MSA-level data and for the wage Phillips curve. US regional data and EU data point to a kink in the price Phillips curve which remains relatively steep when the economy is running hot.
    JEL: C11 C22 E51 E52
    Date: 2023–04
  7. By: Armelius, Hanna (Confederation of Swedish Enterprise); Solberger, Martin (Department of Statistics, Uppsala University); Spånberg, Erik (Department of Statistics, Stockholm University); Österholm, Pär (Örebro University School of Business)
    Abstract: In this paper, the natural rate of interest in Denmark, Norway and Sweden are estimated. This is done by augmenting the Laubach and Williams (2003) framework with a dynamic factor model linked to eco-nomic indicators – a modelling choice which allows us to better identify business cycle fluctuations. We estimate the model using Bayesian methods on data ranging from 1990Q1 to 2022Q4. The results indi-cate that the natural rate has declined substantially and in all countries is at a low level at the end of the sample.
    Keywords: Monetary policy; Business cycle; Bayesian filter; Dynamic factor model
    JEL: E31 E43 E52
    Date: 2023–04–28

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