nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒01‒02
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Pruned Skewed Kalman Filter and Smoother: With Application to the Yield Curve By Guljanov, Gaygysyz; Mutschler, Willi; Trede, Mark
  2. An Infinite Hidden Markov Model with Stochastic Volatility By Li, Chenxing; Maheu, John M; Yang, Qiao
  3. Spectral estimation for mixed causal-noncausal autoregressive models By Alain Hecq; Daniel Velasquez-Gaviria
  4. Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network By Aryan Bhambu; Arabin Kumar Dey
  5. On a Moving Average with Internal Degrees of Freedom By Linda Boudjemila; Alexander Bobyl; Vadim Davydov; Vladislav Malyshkin
  6. Conditional density forecasting: a tempered importance sampling approach By Montes-Galdón, Carlos; Paredes, Joan; Wolf, Elias

  1. By: Guljanov, Gaygysyz; Mutschler, Willi; Trede, Mark
    Abstract: The Skewed Kalman Filter is a powerful tool for statistical inference of asymmetrically distributed time series data. However, the need to evaluate Gaussian cumulative distribution functions (cdf) of increasing dimensions, creates a numerical barrier such that the filter is usually applicable for univariate models and under simplifying conditions only. Based on the intuition of how skewness propagates through the state-space system, a computationally efficient algorithm is proposed to prune the overall skewness dimension by discarding elements in the cdfs that do not distort the symmetry up to a pre-specified numerical threshold. Accuracy and efficiency of this Pruned Skewed Kalman Filter for general multivariate state-space models are illustrated through an extensive simulation study. The Skewed Kalman Smoother and its pruned implementation are also derived. Applicability is demonstrated by estimating a multivariate dynamic Nelson-Siegel term structure model of the US yield curve with Maximum Likelihood methods. We find that the data clearly favors a skewed distribution for the innovations to the latent level, slope and curvature factors.
    Keywords: state-space models; skewed Kalman filter; skewed Kalman smoother; closed skew-normal; dimension reduction; yield curve; term structure; dynamic Nelson-Siegel
    Date: 2022–12
  2. By: Li, Chenxing; Maheu, John M; Yang, Qiao
    Abstract: This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density beyond that attributed to parametric latent volatility. The new model nests several special cases as well as the SV-DPM. We also discuss posterior and predictive density simulation methods for the model. Applied to equity returns, foreign exchange rates, oil price growth and industrial production growth, the new model improves density forecasts, compared to the SV-DPM, a stochastic volatility with Student-t innovations and other fat-tailed volatility models.
    Keywords: stochastic volatility; Markov-switching; MCMC; Bayesian; nonparametric; semiparametric
    JEL: C11 C14 C22 C53 C58
    Date: 2022–11–25
  3. By: Alain Hecq; Daniel Velasquez-Gaviria
    Abstract: This paper investigates new ways of estimating and identifying causal, noncausal, and mixed causal-noncausal autoregressive models driven by a non-Gaussian error sequence. We do not assume any parametric distribution function for the innovations. Instead, we use the information of higher-order cumulants, combining the spectrum and the bispectrum in a minimum distance estimation. We show how to circumvent the nonlinearity of the parameters and the multimodality in the noncausal and mixed models by selecting the appropriate initial values in the estimation. In addition, we propose a method of identification using a simple comparison criterion based on the global minimum of the estimation function. By means of a Monte Carlo study, we find unbiased estimated parameters and a correct identification as the data depart from normality. We propose an empirical application on eight monthly commodity prices, finding noncausal and mixed causal-noncausal dynamics.
    Date: 2022–11
  4. By: Aryan Bhambu; Arabin Kumar Dey
    Abstract: In this paper we propose a novel procedure to construct a confidence interval for multivariate time series predictions using long short term memory network. The construction uses a few novel block bootstrap techniques. We also propose an innovative block length selection procedure for each of these schemes. Two novel benchmarks help us to compare the construction of this confidence intervals by different bootstrap techniques. We illustrate the whole construction through S\&P $500$ and Dow Jones Index datasets.
    Date: 2022–11
  5. By: Linda Boudjemila; Alexander Bobyl; Vadim Davydov; Vladislav Malyshkin
    Abstract: A new type of moving average is developed. Whereas a regular moving average (e.g. of price) has a built-in internal time scale (time-window, exponential weight, etc.), the moving average developed in this paper has the weight as the product of a polynomial by window factor. The polynomial is the square of a wavefunction obtained from an eigenproblem corresponding to other observable (e.g. execution flow I=dV/dt , the number of shares traded per unit time). This allows to obtain an immediate "switch" without lagging typical for regular moving average.
    Date: 2022–11
  6. By: Montes-Galdón, Carlos; Paredes, Joan; Wolf, Elias
    Abstract: This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future paths as it is usually done in the conditional forecasting literature. The proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target distributions the researcher has access to. As an example, this paper shows how to implement the algorithm by conditioning the forecasting densities of a BVAR and a DSGE model on information about the marginal densities of future oil prices. The results show that increased asymmetric upside risks to oil prices result in upside risks to inflation as well as higher core-inflation over the considered forecasting horizon. Finally, a real-time forecasting exercise yields that introducing market-based information on the oil price improves inflation and GDP forecasts during crises times such as the COVID pandemic. JEL Classification: C11, C53, E31, E37
    Keywords: Bayesian analysis, forecasting, importance sampling, inflation-at-risk
    Date: 2022–12

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