nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒01‒25
ten papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Forecasting in a changing world: from the great recession to the COVID-19 pandemic By Mariia Artemova; Francisco Blasques; Siem Jan Koopman; Zhaokun Zhang
  2. Spatial and Spatio-temporal Error Correction, Networks and Common Correlated Effects By Arnab Bhattacharjee; Jan Ditzen; Sean Holly
  3. Fast and accurate variational inference for large Bayesian VARs with stochastic volatility By Joshua C.C. Chan; Xuewen Yu
  4. A Multivariate GARCH-Jump Mixture Model By Li, Chenxing; Maheu, John M
  5. Structural Panel Bayesian VAR with Multivariate Time-varying Volatility to jointly deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues By Pacifico, Antonio
  6. Time-Varying Mixture Copula Models with Copula Selection By Bingduo Yang; Zongwu Cai; Christian M. Hafner; Guannan Liu
  7. Efficient VAR Discretization By Grey Gordon
  8. Dynamic Ordering Learning in Multivariate Forecasting By Bruno P. C. Levy; Hedibert F. Lopes
  9. Solving the Price Puzzle Via A Functional Coefficient Factor-Augmented VAR Model By Zongwu Cai; Xiyuan Liu
  10. An unobserved components model of total factor productivity and the relative price of investment By Joshua C.C. Chan; Edouard Wemy

  1. By: Mariia Artemova (Vrije Universiteit Amsterdam); Francisco Blasques (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam); Zhaokun Zhang (Shanghai University)
    Abstract: We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified linear autoregressive models. The method weighs data points in the observed sample and is useful in the presence of data generating processes featuring structural breaks, complex nonlinearities, or other time-varying properties which cannot be easily captured by model design. Additionally, the method reduces to classical maximum likelihood when the model is well specified, which results in weights which are set uniformly to one. We show how the optimal weights can be set by means of a cross-validation procedure. In a set of Monte Carlo experiments we reveal that the estimation method can significantly improve the forecasting accuracy of autoregressive models. In an empirical study concerned with forecasting the U.S. Industrial Production, we show that the forecast accuracy during the Great Recession can be significantly improved by giving greater weight to observations associated with past recessions. We further establish that the same empirical finding can be found for the 2008-2009 global financial crisis, for different macroeconomic time series, and for the COVID-19 recession in 2020.
    Keywords: Autoregressive Models, Cross-Validation, Kullback-Leibler Divergence, Stationarity and Ergodicity, Macroeconomic Time Series
    JEL: C10 C22 C32 C51
    Date: 2021–01–14
  2. By: Arnab Bhattacharjee (Heriot-Watt University and National Institute of Economic & Social Research, UK); Jan Ditzen (Free University of Bozen-Bolzano, Italy, and Center for Energy Economics Research and Policy (CEERP), Heriot-Watt University, Edinburgh, UK); Sean Holly (Faculty of Economics, University of Cambridge, UK)
    Abstract: We provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be nonstationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and we propose the mean group, common corrrelated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. We apply this model to the 324 local authorities of England, and show that our approach is useful for modelling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics, and provides exciting new insights.
    Keywords: Spatio-temporal dynamics; Error Correction Models; Weak and strong cross sectional dependence
    JEL: C21 C22 C23 R3
    Date: 2021–01
  3. By: Joshua C.C. Chan; Xuewen Yu
    Abstract: We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the entire support of the joint distribution. In a Monte Carlo study we show that the new global approximation is over an order of magnitude more accurate than existing alternatives. We illustrate the proposed methodology with an application of a 96-variable VAR with stochastic volatility to measure global bank network connectedness. Our measure is able to detect the drastic increase in global bank network connectedness much earlier than rolling-window estimates from a homoscedastic VAR.
    Keywords: large vector autoregression, stochastic volatility, Variational Bayes, volatility network, connectedness
    JEL: C11 C32 C55 G21
    Date: 2020–12
  4. By: Li, Chenxing; Maheu, John M
    Abstract: This paper proposes a new parsimonious multivariate GARCH-jump (MGARCH-jump) mixture model with multivariate jumps that allows both jump sizes and jump arrivals to be correlated among assets. Dependent jumps impact the conditional moments of returns as well as beta dynamics of a stock. Applied to daily stock returns, the model identifies co-jumps well and shows that both jump arrivals and jump sizes are highly correlated. The jump model has better predictions compared to a benchmark multivariate GARCH model.
    Keywords: Multivariate GARCH; Jumps; Multinomial; Co-jump; beta dynamics; Value-at-Risk
    JEL: C32 C53 C58 G1 G10
    Date: 2020–12
  5. By: Pacifico, Antonio
    Abstract: This paper improves a standard Structural Panel Bayesian Vector Autoregression model in order to jointly deal with issues of endogeneity, because of omitted factors and unobserved heterogeneity, and volatility, because of policy regime shifts and structural changes. Bayesian methods are used to select the best model solution for examining if international spillovers come from multivariate volatility, time variation, or contemporaneous relationship. An empirical application among Central-Eastern and Western Europe economies is conducted to describe the performance of the methodology, with particular emphasis on the Great recession and post-crisis periods. Findings from evidence-based forecasting are also addressed to evaluate the impact of an ongoing pandemic crisis on the global economy.
    Keywords: Structural Panel VAR; Bayesian Methods; Multivariate Volatility; Policy Regime Shifts Endogeneity Issues; Central-Eastern and Western Europe.
    JEL: C1 C5 E6
    Date: 2020
  6. By: Bingduo Yang (Lingnan (University) College, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Christian M. Hafner (Department of Economics, Tulane University, New Orleans, LA 70118, USA); Guannan Liu (School of Economics and WISE, Xiamen University, Xiamen, Fujian 361005, China)
    Abstract: Modeling the joint tails of multiple financial time series has many important implications for risk management. Classical models for dependence often encounter a lack of fit in the joint tails, calling for additional flexibility. This paper introduces a new semiparametric time-varying mixture copula model, in which both weights and dependence parameters are deterministic and unspecified functions of time. We propose penalized time-varying mixture copula models with group smoothly clipped absolute deviation penalty functions to do the estimation and copula selection simultaneously. Monte Carlo simulation results suggest that the shrinkage estimation procedure performs well in selecting and estimating both constant and time-varying mixture copula models. Using the proposed model and method, we analyze the evolution of the dependence among four international stock markets, and find substantial changes in the levels and patterns of the dependence, in particular around crisis periods.
    Keywords: Copula Selection; EM Algorithm; Mixture Copula; SCAD; Time-Varying Distribution.
    JEL: C14 C22
    Date: 2021–01
  7. By: Grey Gordon
    Abstract: The standard approach to discretizing VARs uses tensor grids. However, when the VAR components exhibit significant unconditional correlations or when there are more than a few variables, this approach creates large inefficiencies because some discretized states will be visited with only vanishingly small probability. I propose pruning these low-probability states, thereby constructing an efficient grid. I investigate how much an efficient grid improves accuracy in the context of an AR(2) model and a small-scale New Keynesian model featuring four shocks. In both contexts, the efficient grid vastly increases accuracy.
    Keywords: VAR; Autoregressive; Discretization; New Keynesian
    JEL: C32 C63 E32 E52
    Date: 2020–06–05
  8. By: Bruno P. C. Levy; Hedibert F. Lopes
    Abstract: In many fields where the main goal is to produce sequential forecasts for decisionmaking problems, the good understanding of the contemporaneous relations among different series is crucial for the estimation of the covariance matrix. In recent years, the modified Cholesky decomposition appeared as a popular approach to covariance matrix estimation. However, its main drawback relies on the imposition of the series ordering structure. In this work, we propose a highly flexible and fast method to deal with the problem of ordering uncertainty in a dynamic fashion with the use of Dynamic Order Probabilities. We apply the proposed method in a dynamic portfolio allocation problem, where the investor is able to learn the contemporaneous relations among different currencies. We show that our approach generates not just significant statistical improvements, but also huge economic gains for a mean-variance investor relative to the Random Walk benchmark and using fixed orders over time.
    Date: 2021–01
  9. By: Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Xiyuan Liu (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)
    Abstract: Effects of monetary policy shocks on large amounts of macroeconomic variables are identified by a new class of functional-coefficient factor-augmented vector autoregressive (FAVAR) models, which allows coefficients of classical FAVAR models to vary with some variable. In the empirical study, we analyze the impulse response functions estimated by the newly proposed model and compare our results with those from classical FAVAR models. Our empirical finding is that our new model has an ability to eliminate the well-known price puzzle without adding new variables into the dataset.
    Keywords: Factor-augmented vector autoregressive; Functional coefficient models; Impulse response functions; Nonparametric estimation; Price puzzle
    JEL: C14 C32 E30 E31
    Date: 2021–01
  10. By: Joshua C.C. Chan; Edouard Wemy
    Abstract: This paper applies the common stochastic trends representation approach to the time series of total factor productivity and the relative price of investment to investigate the relationship between neutral technology and investment-specific technology. The permanent and transitory movements in both series are estimated efficiently via MCMC methods using band matrix algorithms. The results indicate that total factor productivity and the relative price of investment are, each, well-represented by an integrated process of order one. In addition, their time series share a common trend component that we interpret as reflecting changes in General Purpose Technology. These results suggest that (1) the traditional view of assuming that neutral technology and investment-specific technology follow independent processes is not supported by the features of the time series and (2) advances in information and communication technologies are general purpose technological progress that drive the trend in aggregate TFP in the United States.
    Keywords: Business cycles, Investment-specific technological change, Total Factor Productivity, Unobserved Components Model
    JEL: E22 E32 C32
    Date: 2020–12

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