nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒12‒05
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A Residuals-Based Nonparametric Variance Ratio Test for Cointegration By Karsten Reichold
  2. Identifying Proxy VARs with Restrictions on the Forecast Error Variance By Härtl, Tilmann
  3. Dynamic Identification in VARs By Fève, Patrick; Beaudry, Paul; Collard, Fabrice; Guay, Alain; Portier, Franck
  4. Boosted p-Values for High-Dimensional Vector Autoregression By Xiao Huang
  5. On the Optimal Forecast with the Fractional Brownian Motion By Wang, Xiaohu; Yu, Jun; Zhang, Chen
  6. Multifractality in time series is due to temporal correlations By Jaros{\l}aw Kwapie\'n; Pawel Blasiak; Stanis{\l}aw Dro\.zd\.z; Pawe{\l} O\'swi\k{e}cimka
  7. The financial accelerator mechanism: does frequency matter? By Claudia Foroni; Paolo Gelain; Massimiliano Marcellino
  8. Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models By Francis X. Diebold; Maximilian Gobel; Philippe Goulet Coulombe

  1. By: Karsten Reichold
    Abstract: This paper derives asymptotic theory for Breitung's (2002, Journal of Econometrics 108, 343-363) nonparameteric variance ratio unit root test when applied to regression residuals. The test requires neither the specification of the correlation structure in the data nor the choice of tuning parameters. Compared with popular residuals-based no-cointegration tests, the variance ratio test is less prone to size distortions but has smaller local asymptotic power. However, this paper shows that local asymptotic power properties do not serve as a useful indicator for the power of residuals-based no-cointegration tests in finite samples. In terms of size-corrected power, the variance ratio test performs relatively well and, in particular, does not suffer from power reversal problems detected for, e.g., the frequently used augmented Dickey-Fuller type no-cointegration test. An application to daily prices of cryptocurrencies illustrates the usefulness of the variance ratio test in practice.
    Date: 2022–11
  2. By: Härtl, Tilmann
    JEL: C32
    Date: 2022
  3. By: Fève, Patrick; Beaudry, Paul; Collard, Fabrice; Guay, Alain; Portier, Franck
    Abstract: Most macroeconomic models, both fully structural models as well as SVAR models, view economic outcomes as the product of a combination of endogenous and exogenous dynamic forces. In particular, the exogenous forces are generally modeled as a set of linearly independent dynamics processes. In this paper we begin by showing that this dual dynamic structure is sufficient to identify the entire set of structural impulse responses inherent to any such model. No extra restrictions are necessary. We then use this observation to suggest how it can be used to evaluate common SVAR restrictions (impact restrictions, long-run restrictions and proxy-VAR), as well as help transpire the role of cross-equation restrictions inherent to more structural models.
    Keywords: Structural Shocks; Dynamic Identification; SVARs; DSGE models
    JEL: C32 E32
    Date: 2022–11–18
  4. By: Xiao Huang
    Abstract: Assessing the statistical significance of parameter estimates is an important step in high-dimensional vector autoregression modeling. Using the least-squares boosting method, we compute the p-value for each selected parameter at every boosting step in a linear model. The p-values are asymptotically valid and also adapt to the iterative nature of the boosting procedure. Our simulation experiment shows that the p-values can keep false positive rate under control in high-dimensional vector autoregressions. In an application with more than 100 macroeconomic time series, we further show that the p-values can not only select a sparser model with good prediction performance but also help control model stability. A companion R package boostvar is developed.
    Date: 2022–11
  5. By: Wang, Xiaohu (Fudan University); Yu, Jun (Singapore Management University); Zhang, Chen (Singapore Management University)
    Abstract: This paper examines the performance of alternative forecasting formulaewith the fractional Brownian motion based on a discrete and Önite sample.One formula gives the optimal forecast when a continuous record over theinÖnite past is available. Another formula gives the optimal forecast whena continuous record over the Önite past is available. Alternative discretiza-tion schemes are proposed to approximate these formulae. These alternative discretization schemes are then compared with the conditional expectationof the target variable on the vector of the discrete and Önite sample. It isshown that the conditional expectation delivers more accurate forecasts thanthe discretization-based formulae using both simulated data and daily realizedvolatility (RV) data. Empirical results based on daily RV indicate that theconditional expectation enhances the already-widely known great performanceof fBm in forecasting future RV.
    Keywords: Fractional Gaussian noise; Conditional expectation; Anti-persistence; Continuous record; Discrete record; Optimal forecast
    JEL: C12 C22 G01
    Date: 2022–10–28
  6. By: Jaros{\l}aw Kwapie\'n; Pawel Blasiak; Stanis{\l}aw Dro\.zd\.z; Pawe{\l} O\'swi\k{e}cimka
    Abstract: Based on the rigorous mathematical arguments formulated within the Multifractal Detrended Fluctuation Analysis (MFDFA) approach it is shown that in the uncorrelated time series the effects resembling multifractality asymptotically disappear when the length of time series increases. The related effects are also illustrated by numerical simulations. This documents that the genuine multifractality in time series may only result from the long-range temporal correlations and the fatter distribution tails of fluctuations may broaden the width of singularity spectrum only when such correlations are present. The frequently asked question of what makes multifractality in time series - temporal correlations or broad distribution tails - is thus ill posed.
    Date: 2022–11
  7. By: Claudia Foroni; Paolo Gelain; Massimiliano Marcellino
    Abstract: We use mixed-frequency (quarterly-monthly) data to estimate a dynamic stochastic general equilibrium model embedded with the financial accelerator mechanism à la Bernanke et al. (1999). We find that the financial accelerator can work very differently at monthly frequency compared to quarterly frequency; that is, we document its inversion. That is because aggregating monthly data into quarterly data leads to large biases in the estimated quarterly parameters and, as a consequence, to a deep change in the transmission of shocks.
    Keywords: DSGE models; financial accelerator; mixed-frequency data
    JEL: C52 E32 E52
    Date: 2022–11–07
  8. By: Francis X. Diebold (University of Pennsylvania); Maximilian Gobel (University of Lisbon); Philippe Goulet Coulombe (University of Quebec)
    Abstract: We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regression (FELR) forecasts of Diebold and Gobel (2022), and to compare FELR forecasts to naive pure-trend benchmark forecasts. Then we introduce a much more sophisticated feature-engineered machine learning (FEML) model, and we use glide charts to evaluate FEML forecasts and compare them to a FELR benchmark. Our substantive results include the frequent appearance of predictability thresholds, which differ across months, meaning that accuracy initially fails to improve as the target date is approached but then increases progressively once a threshold lead time is crossed. Also, we find that FEML can improve appreciably over FELR when forecasting "turning point" months in the annual cycle at horizons of one to three months ahead.
    Keywords: Seasonal climate forecasting, forecast evaluation and comparison, prediction
    JEL: Q54 C22 C52 C53
    Date: 2022–06–23

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