nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒11‒21
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Discovering Stars: Problems in Recovering Latent Variables from Models By Daniel Buncic; Adrian Pagan
  2. Uncertainty, Skewness, and the Business Cycle Through the MIDAS Lens By Efrem Castelnuovo; Lorenzo Mori
  3. Forecasting Oil Prices: Can Large BVARs Help? By Bao H. Nguyen; Bo Zhang

  1. By: Daniel Buncic; Adrian Pagan
    Abstract: There exist many latent variables in macroeconometrics that are commonly referred to as "stars". Examples of such "stars" are the NAIRU, potential GDP, and the neutral real rate of interest. Because these "stars" are defined as latent variables, they are estimated using the Kalman filter and/or smoother from models that can be expressed in State Space Form. When there are more shocks than observables in the State Space Form representation of such models, issues arise related to the recoverability of these "stars" from the data. Recoverability is problematic in this setting even if the assumed model for the data is correct and all model parameters are known. In this paper, we examine recoverability in a range of popular models and show that many of these "stars" cannot be recovered.
    Keywords: Recoverability, excess shocks, latent variables, neutral rates, Kalman Filter
    JEL: E37 C51 C52
    Date: 2022–09
  2. By: Efrem Castelnuovo (University of Padova); Lorenzo Mori (University of Padova)
    Abstract: We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles of the estimated conditional distribution to produce measures of uncertainty and skewness. Embedding these measures in a VAR framework, we show that unexpected changes in uncertainty are associated with an increase in (left) skewness and a downturn in real activity. Empirical findings related to VAR impulse responses and forecast error variance decomposition are shown to depend on the inclusion/omission of monthly-level information on financial conditions when estimating real GDP growth’s conditional density. Effects are significantly downplayed if we consider a quarterly-only quantile regression model. A counterfactual simulation conducted by shutting down the endogenous response of skewness to uncertainty shocks shows that skewness substantially amplifies the recessionary effects of uncertainty.
    Keywords: Uncertainty, skewness, quantile regressions, vector autoregressions, MIDAS
    Date: 2022–10
  3. By: Bao H. Nguyen; Bo Zhang
    Abstract: Large Bayesian Vector Autoregressions (BVARs) have been a successful tool in the forecasting literature and most of this work has focused on macroeconomic variables. In this paper, we examine the ability of large BVARs to forecast the real price of crude oil using a large dataset with over 100 variables. We find consistent results that the large BVARs do not beat the BVARs with small and medium sizes for short forecast horizons but offer better forecasts at long horizons. In line with the forecasting macroeconomic literature, we also find that the forecast ability of the large models further improves upon the competing standard BVARs once endowed with flexible error structures.
    Keywords: forecasting, non-Gaussian, stochastic volatility, oil prices, big data
    JEL: C11 C32 C52 Q41 Q47
    Date: 2022–10

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