nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒07‒15
ten papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Realized variance modeling: decoupling forecasting from estimation By Fabrizio Cipollini; Giampiero M. Gallo; Alessandro Palandri
  2. Realized Volatility Forecasting: Robustness to Measurement Errors By Fabrizio Cipollini; Giampiero M. Gallo; Edoardo Otranto
  3. Dynamic Factor Models By Catherine Doz; Peter Fuleky
  4. Simulation smoothing for nowcasting with large mixed-frequency VARs By Sebastian Ankargren; Paulina Jon\'eus
  5. Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall By Chao Wang; Richard Gerlach
  6. Dynamic time series clustering via volatility change-points By Nick Whiteley
  7. Do SVARs with sign restrictions not identify unconventional monetary policy shocks? By Jef Boeckx; Maarten Dossche; Alessandro Galesi; Boris Hofmann; Gert Peersman
  8. Temporal Disaggregation of Business Dynamics: New Evidence for U.S. Economy By Lorenza Rossi; Emilio Zanetti Chini
  9. Slack and Cyclically Sensitive Inflation By James H. Stock; Mark W. Watson
  10. On the Evolution of U.S. Temperature Dynamics By Francis X. Diebold; Glenn D. Rudebusch

  1. By: Fabrizio Cipollini (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Giampiero M. Gallo (Italian Court of Audits, and New York University in Florence); Alessandro Palandri (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze)
    Abstract: In this paper we evaluate the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and estimation criteria . Our empirical findings highlight that: independently of the econometrician’s forecasting loss function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the HAR family, for any of the forecasting loss functions considered; the (2,1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-run dependence as well as the HAR family.
    Keywords: Variance modeling; Variance forecasting; Heterogeneous Autoregressive (HAR) model; Multiplicative Error Model (MEM); Realized variance space
    JEL: C32 C53 C58 G17
    Date: 2019–07
  2. By: Fabrizio Cipollini (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Giampiero M. Gallo (Corte dei Conti and NYU in Florence, Italy); Edoardo Otranto (Università di Messina, Italy)
    Abstract: In this paper, we reconsider the issue of measurement errors affecting the estimates of a dynamic model for the conditional expectation of realized variance arguing that heteroskedasticity of such errors may be adequately represented with a multiplicative error model. Empirically we show that the significance of quarticity/quadratic terms capturing attenuation bias is very important within an HAR model, but is greatly diminished within an AMEM, and more so when regime specific dynamics account for a faster mean reversion when volatility is high. Model Confidence Sets confirm such robustness both in– and out–of–sample.
    Keywords: Realized volatility, Forecasting, Measurement errors, HAR, AMEM, Markov switching, Volatility of volatility
    JEL: C22 C51 C53 C58
    Date: 2019–07
  3. By: Catherine Doz (Paris School of Economics and University Paris); Peter Fuleky (Department of Economics, University of Hawaii at Manoa, UHERO)
    Abstract: Dynamic factor models are parsimonious representations of relationships among time series variables. With the surge in data availability, they have proven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework.
    Keywords: dynamic factor models, big data, two-step estimation, time domain, frequency domain, structural breaks
    JEL: C32 C38 C53
    Date: 2019–07
  4. By: Sebastian Ankargren; Paulina Jon\'eus
    Abstract: There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macroeconomic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows. We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.
    Date: 2019–07
  5. By: Chao Wang; Richard Gerlach
    Abstract: A joint conditional autoregressive expectile and Expected Shortfall framework is proposed. The framework is extended through incorporating a measurement equation which models the contemporaneous dependence between the realized measures and the latent conditional expectile. Nonlinear threshold specification is further incorporated into the proposed framework. A Bayesian Markov Chain Monte Carlo method is adapted for estimation, whose properties are assessed and compared with maximum likelihood via a simulation study. One-day-ahead VaR and ES forecasting studies, with seven market indices, provide empirical support to the proposed models.
    Date: 2019–06
  6. By: Nick Whiteley
    Abstract: This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points. The model accommodates some classical stylized features of returns and its relation to GARCH is discussed. Clustering is performed using a probability metric evaluated between posterior distributions of the most recent change-point associated with each series. This implies series are grouped together at a given time if there is evidence the most recent shifts in their respective volatilities were coincident or closely timed. The clustering method is dynamic, in that groupings may be updated in an online manner as data arrive. Numerical results are given analyzing daily returns of constituents of the S&P 500.
    Date: 2019–06
  7. By: Jef Boeckx (National Bank of Belgium); Maarten Dossche (European Central Bank); Alessandro Galesi (Banco de España); Boris Hofmann (Bank for International Settlements); Gert Peersman (Ghent University)
    Abstract: A growing empirical literature has shown, based on structural vector autoregressions (SVARs) identified through sign restrictions, that unconventional monetary policies implemented after the outbreak of the Great Financial Crisis (GFC) had expansionary macroeconomic effects. In a recent paper, Elbourne and Ji (2019) conclude that these studies fail to identify true unconventional monetary policy shocks in the euro area. In this note, we show that their findings are actually fully consistent with a successful identification of unconventional monetary policy shocks by the earlier studies and that their approach does not serve the purpose of evaluating identification strategies of SVARs.
    Keywords: unconventional monetary policy, SVARs
    JEL: C32 E30 E44 E51 E52
    Date: 2019–07
  8. By: Lorenza Rossi; Emilio Zanetti Chini
    Abstract: We provide new disaggregated data and stylized facts on firm dynamics of the U.S economy by using a state-space method to transform Census yearly data of entry and exit from 1977 to 2013 into quarterly frequency. Entry is lagging and symmetric, while exit is leading and asymmetric along the business cycle. We select the most significant determinants of these variables by matching Census data with a new database by Federal Reserve. These determinants differ considerably among entry and exit. Finally, standard macroeconometric models estimated on our disaggregated series support the recent theoretical literature, according to which the cleansing effect of recession is mainly due to exit asymmetry.
    Keywords: Bayesian VAR; Firms and Establishments; Productivity; State-Space Models
    JEL: C13 C32 C40 E30 E32
    Date: 2019–06
  9. By: James H. Stock; Mark W. Watson
    Abstract: We investigate the flattening Phillips relation by making two departures from standard specifications. First, we measure slack using real activity variables that are bandpass filtered or year-over-year changes in activity (these are similar), instead of gaps. Second, we study the components of inflation instead of the standard aggregates. We find that some inflation components have strong and stable correlations with the cyclical component of real activity; these components tend to be relatively well-measured and domestically determined. Other components, typically prices that are poorly measured or internationally determined, have weak and/or unstable correlations with cyclical activity. We construct a new inflation index, Cyclically Sensitive Inflation, that weights the components by their joint cyclical covariation with real activity. The index has strong and stable correlations with cyclical activity and provides a real-time measure of cyclical movements in inflation.
    JEL: E31 E32
    Date: 2019–06
  10. By: Francis X. Diebold (Department of Economics, University of Pennsylvania); Glenn D. Rudebusch (Federal Reserve Bank of San Francisco)
    Abstract: Climate change is a multidimensional shift. While much research has documented rising mean temperature levels, we also examine range-based measures of daily temperature volatility. Specifically, using data for select U.S. cities over the past half-century, we compare the evolving time series dynamics of the average temperature level, AVG, and the diurnal temperature range, DTR (the difference between the daily maximum and minimum temperatures at a given location). We characterize trend and seasonality in these two series using linear models with time-varying coecients. These straightforward yet flexible approximations provide evidence of evolving DTR seasonality, stable AVG seasonality, and conditionally Gaussian but heteroskedastic innovations for both DTR and AVG.
    Keywords: Unemployment DTR, temperature volatility, temperature variability, climate modeling, climate change
    JEL: Q54 C22
    Date: 2019–07–05

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