nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒10‒18
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A Unified Framework to Estimate Macroeconomic Stars By Saeed Zaman
  2. Nonparametric Tests of Conditional Independence for Time Series By Xiaojun Song; Haoyu Wei
  3. The time-varying evolution of inflation risks By Korobilis, Dimitris; Landau, Bettina; Musso, Alberto; Phella, Anthoulla
  4. A time-varying skewness model for Growth-at-Risk By Martin Iseringhausen
  5. A mixed frequency BVAR for the euro area labour market By Consolo, Agostino; Foroni, Claudia; Hernández, Catalina Martínez

  1. By: Saeed Zaman
    Abstract: We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" — i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, the price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey expectations and stars to improve the stars' econometric estimation. Our approach permits time variation in the relationships between various components, including time variation in error variances. To tractably estimate the large multivariate model, we use a recently developed precision sampler that relies on Bayesian methods. The by-products of this approach are the time-varying estimates of the wage and price Phillips curves, and the pass-through between prices and wages, both of which provide new insights into these empirical relationships' instability in US data. Generally, the contours of the stars echo those documented elsewhere in the literature — estimated using smaller models — but at times the estimates of stars are different, and these differences can matter for policy. Furthermore, our estimates of the stars are among the most precise. Lastly, we document the competitive real-time forecasting properties of the model and, separately, the usefulness of stars' estimates if they were used as steady-state values in external models.
    Keywords: state-space model; Bayesian analysis; time-varying parameters; natural rates; survey expectations; COVID-19
    JEL: C5 E24 E31 E4 O4
    Date: 2021–10–14
  2. By: Xiaojun Song; Haoyu Wei
    Abstract: We propose consistent nonparametric tests of conditional independence for time series data. Our methods are motivated from the difference between joint conditional cumulative distribution function (CDF) and the product of conditional CDFs. The difference is transformed into a proper conditional moment restriction (CMR), which forms the basis for our testing procedure. Our test statistics are then constructed using the integrated moment restrictions that are equivalent to the CMR. We establish the asymptotic behavior of the test statistics under the null, the alternative, and the sequence of local alternatives converging to conditional independence at the parametric rate. Our tests are implemented with the assistance of a multiplier bootstrap. Monte Carlo simulations are conducted to evaluate the finite sample performance of the proposed tests. We apply our tests to examine the predictability of equity risk premium using variance risk premium for different horizons and find that there exist various degrees of nonlinear predictability at mid-run and long-run horizons.
    Date: 2021–10
  3. By: Korobilis, Dimitris; Landau, Bettina; Musso, Alberto; Phella, Anthoulla
    Abstract: This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to model flexibly the whole distribution of inflation. In order to make our approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state-space form of the TVP quantile regression into an equivalent high-dimensional regression form. An application of this methodology points to a good forecasting performance of quantile regressions with TVPs augmented with specific credit and money-based indicators for the prediction of the conditional distribution of inflation in the euro area, both in the short and longer run, and specifically for tail risks. JEL Classification: C11, C22, C52, C53, C55, E31, E37, E51
    Keywords: Bayesian shrinkage, euro area, Horseshoe, inflation tail risks, MCMC, quantile regression, time-varying parameters
    Date: 2021–10
  4. By: Martin Iseringhausen (ESM)
    Abstract: This paper studies macroeconomic risks in a panel of advanced economies based on a stochastic volatility model in which macro-financial conditions shape the predictive growth distribution. We find sizable time variation in the skewness of these distributions, conditional on the macro-financial environment. Tightening financial conditions signal increasing downside risk in the short term, but this link reverses at longer horizons. When forecasting downside risk, the proposed model, on average, outperforms existing approaches based on quantile regression and a GARCH model, especially at short horizons. In forecasting upside risk, it improves the average accuracy across all horizons up to four quarters ahead. The suggested approach can inform policy makers' assessment of macro-financial vulnerabilities by providing a timely signal of shifting risks and a quantification of their magnitude.
    Keywords: Bayesian analysis, downside risk, macro-financial linkages, time variation
    JEL: C11 C23 C53 E44
    Date: 2021–06–10
  5. By: Consolo, Agostino; Foroni, Claudia; Hernández, Catalina Martínez
    Abstract: We introduce a Bayesian Mixed-Frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results in terms of forecasting, especially when looking at quarterly variables, such as employment growth and the job finding rate. Furthermore, we look into the shocks that drove the labour market and macroeconomic dynamics from 2002 to early 2020, with a first insight also on the COVID-19 recession. While domestic and foreign demand shocks were the main drivers during the Global Financial Crisis, aggregate supply conditions and labour supply factors reflecting the degree of lockdown-related restrictions have been important drivers of key labour market variables during the pandemic. JEL Classification: J6, C53, C32, C11
    Keywords: Bayesian VAR, labour market, mixed frequency data
    Date: 2021–10

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