nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒09‒13
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Different Strokes for Different Folks: LongMemory and Roughness By Shi, Shuping; Yu, Jun
  2. Ubiquitous multimodality in mixed causal-noncausal processes. By Kindop, Igor
  3. Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model By Rob Luginbuhl
  4. A Hitchhiker’s Guide to Empirical Macro Models By Fabio Canova; Filippo Ferroni
  5. Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs By Lucien Boulet
  6. Inferential Theory for Generalized Dynamic Factor Models By Matteo Barigozzi; Marc Hallin; Matteo Luciani; Paolo Zaffaroni
  7. On a quantile autoregressive conditional duration model applied to high-frequency financial data By Helton Saulo; Narayanaswamy Balakrishnan; Roberto Vila
  8. Forecasting Dynamic Term Structure Models with Autoencoders By Castro-Iragorri, C; Ramírez, J
  9. Approximate Factor Models with Weaker Loadings By Jushan Bai; Serena Ng
  10. Forecast Pooling or Information Pooling During Crises? MIDAS Forecasting of GDP in a Small Open Economy By Chow, Hwee Kwan; Han, Daniel
  11. Iterated and exponentially weighted moving principal component analysis By Paul Bilokon; David Finkelstein

  1. By: Shi, Shuping (Department of Economics, Macquarie University); Yu, Jun (School of Economics and Lee Kong Chian School of Business, SingaporeManagementUniversity)
    Abstract: The log realized volatility of financial assets is often modeled as an autoregressive fractionally integrated moving average model (ARFIMA) process, denoted by ARFIMA(p, d, q), with p = 1 and q = 0. Two conflicting results have been found in the literature regarding the dynamics. One stream shows that the data series has a long memory (i.e., the fractional parameter d > 0) with strong mean reversion (i.e., the autoregressive coefficient |α1| ≈ 0). The other stream suggests that the volatil-ity is rough (i.e., d
    Keywords: Long memory; fractional integration; roughness; short-run dynamics; realized volatility
    JEL: C15 C22 C32
    Date: 2021–08–03
  2. By: Kindop, Igor
    Abstract: According to the literature, the bimodality of estimates in mixed causal–non-causal autoregressive processes is due to unlucky starting values and happens only ocassionally. This paper shows that a unique and convergent solution is not always the case for models of this class. Instead, the likelihood function is not convex leading to the multimodality of estimated parameters. It can be attributed to the magnitude and sign of the autoregressive coefficients. Simultaneously, the number of local modes grows with the number of autoregressive parameters in the model. This multimodality depends on the parameters of the process and the chosen error distribution. We have to apply grid search methods to extract candidate solutions. The independence of residuals is a necessary hypothesis for the proper identification of the processes. A simple AIC criterion helps to select an independent model. Finally, I sketch a roadmap on estimating mixed causal-noncausal autoregressive models and illustrate the approach with Brent spot oil price returns.
    Keywords: non-causal model, non-convex likelihood, non-Gaussian, nonfundamentalness, multimodality.
    JEL: C13 C22 C51 C52 C53 E37
    Date: 2021–07–09
  3. By: Rob Luginbuhl (CPB Netherlands Bureau for Economic Policy Analysis)
    Abstract: We propose a model-based method to estimate a unique financial cycle based on a rank-restricted multivariate state-space model. This permits us to use mixed-frequency data, allowing for longer sample periods. In our model the financial cycle dynamics are captured by an unobserved trigonometric cycle component. We identify a single financial cycle from the multiple time series by imposing rank reduction on this cycle component. The rank reduction can be justified based on a principal components argument. The model also includes unobserved components to capture the business cycle, time-varying seasonality, trends, and growth rates in the data. In this way we can control for these effects when estimating the financial cycle. We apply our model to US and Dutch data and conclude that a bivariate model of credit and house prices is sufficient to estimate the financial cycle.
    JEL: E5 F3 G15 G01
    Date: 2020–02
  4. By: Fabio Canova; Filippo Ferroni
    Abstract: This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, to measure spillovers and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number of routines to extract cyclical information and to date business cycles. We describe the methodology employed and implementation of the functions with a number of practical examples.
    Keywords: VARs; Local Projections; Bayesian Inference; Identification; Forecasts; Missing Values; Filters and Cycles; MATLAB
    JEL: E52 E32 C10
    Date: 2021–09–04
  5. By: Lucien Boulet
    Abstract: Several academics have studied the ability of hybrid models mixing univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and neural networks to deliver better volatility predictions than purely econometric models. Despite presenting very promising results, the generalization of such models to the multivariate case has yet to be studied. Moreover, very few papers have examined the ability of neural networks to predict the covariance matrix of asset returns, and all use a rather small number of assets, thus not addressing what is known as the curse of dimensionality. The goal of this paper is to investigate the ability of hybrid models, mixing GARCH processes and neural networks, to forecast covariance matrices of asset returns. To do so, we propose a new model, based on multivariate GARCHs that decompose volatility and correlation predictions. The volatilities are here forecast using hybrid neural networks while correlations follow a traditional econometric process. After implementing the models in a minimum variance portfolio framework, our results are as follows. First, the addition of GARCH parameters as inputs is beneficial to the model proposed. Second, the use of one-hot-encoding to help the neural network differentiate between each stock improves the performance. Third, the new model proposed is very promising as it not only outperforms the equally weighted portfolio, but also by a significant margin its econometric counterpart that uses univariate GARCHs to predict the volatilities.
    Date: 2021–08
  6. By: Matteo Barigozzi; Marc Hallin; Matteo Luciani; Paolo Zaffaroni
    Abstract: We provide the asymptotic distributional theory for the so-called General or Generalized Dynamic Factor Model (GDFM), laying the foundations for an inferential approach in the GDFM analysis of high-dimensional time series. Our results are exploiting the duality between common shocksand dynamic loadings under a random cross-section approach to derive the asymptotic distribution of a class of estimators for common shocks, dynamic loadings, common components, and impulse response functions. An empirical application aimed at the construction of a “core” inflation indicator for the U.S. economy is presented, empirically demonstrating the superiority of the GDFM-based indicator over the most commonly adopted approaches, outperforming, in particular, the one based on Principal Components.
    Keywords: High-dimensional time series, Generalized Dynamic Factor Models, One-sided representations of dynamic factor models, Asymptotic distribution, Confidence intervals
    Date: 2021–08
  7. By: Helton Saulo; Narayanaswamy Balakrishnan; Roberto Vila
    Abstract: Autoregressive conditional duration (ACD) models are primarily used to deal with data arising from times between two successive events. These models are usually specified in terms of a time-varying conditional mean or median duration. In this paper, we relax this assumption and consider a conditional quantile approach to facilitate the modeling of different percentiles. The proposed ACD quantile model is based on a skewed version of Birnbaum-Saunders distribution, which provides better fitting of the tails than the traditional Birnbaum-Saunders distribution, in addition to advancing the implementation of an expectation conditional maximization (ECM) algorithm. A Monte Carlo simulation study is performed to assess the behavior of the model as well as the parameter estimation method and to evaluate a form of residual. A real financial transaction data set is finally analyzed to illustrate the proposed approach.
    Date: 2021–09
  8. By: Castro-Iragorri, C; Ramírez, J
    Abstract: Principal components analysis (PCA) is a statistical approach to build factor models in finance. PCA is also a particular case of a type of neural network known as an autoencoder. Recently, autoencoders have been successfully applied in financial applications using factor models, Gu et al. (2020), Heaton and Polson (2017). We study the relationship between autoencoders and dynamic term structure models; furthermore we propose different approaches for forecasting. We compare the forecasting accuracy of dynamic factor models based on autoencoders, classical models in term structure modelling proposed in Diebold and Li (2006) and neural network-based approaches for time series forecasting. Empirically, we test the forecasting performance of autoencoders using the U.S. yield curve data in the last 35 years. Preliminary results indicate that a hybrid approach using autoencoders and vector autoregressions framed as a dynamic term structure model provides an accurate forecast that is consistent throughout the sample. This hybrid approach overcomes in-sample overfitting and structural changes in the data.
    Keywords: autoencoders, factor models, principal components, recurrentneural networks
    JEL: C45 C53 C58
    Date: 2021–07–29
  9. By: Jushan Bai; Serena Ng
    Abstract: Pervasive cross-section dependence is increasingly recognized as an appropriate characteristic of economic data and the approximate factor model provides a useful framework for analysis. Assuming a strong factor structure, early work established convergence of the principal component estimates of the factors and loadings to a rotation matrix. This paper shows that the estimates are still consistent and asymptotically normal for a broad range of weaker factor loadings, albeit at slower rates and under additional assumptions on the sample size. Standard inference procedures can be used except in the case of extremely weak loadings which has encouraging implications for empirical work. The simplified proofs are of independent interest.
    Date: 2021–09
  10. By: Chow, Hwee Kwan (School of Economics, Singapore Management University); Han, Daniel (School of Economics, Singapore Management University)
    Abstract: This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into factor MIDAS models, for short-term Singapore GDP growth forecasting with a large ragged-edge mixed frequency dataset. We investigate their relative predictive performance in a pseudo-out-of-sample forecasting exercise from 2007Q4 to 2020Q3. In the stable growth non-crisis period, no substantial difference in predictive performance is found across forecast models. We find factor MIDAS models dominate both the quarterly benchmark model and the forecast pooling strategy by wide margins in the Global Financial Crisis and the Covid-19 crisis. Reflecting the small open nature of the economy, pooling single indicator forecasts from a small subgroup of foreign-related indicators beats the benchmark, offering a quick method to incorporate timely information for practitioners who have difficulty updating a large dataset. Nonetheless, the information pooling approach retains its superior ability at tracking rapid output changes during crises.
    Keywords: Forecast evaluation; Factor MIDAS; pooling GDP forecasts; global financial crisis; Covid-19 pandemic crisis
    JEL: C22 C53 C55
    Date: 2021–07–01
  11. By: Paul Bilokon; David Finkelstein
    Abstract: The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several technical difficulties, such as numerical instability and nonstationarity. We attempt to resolve them by proposing two new variants of PCA: an iterated principal component analysis (IPCA) and an exponentially weighted moving principal component analysis (EWMPCA). Both variants rely on the Ogita-Aishima iteration as a crucial step.
    Date: 2021–08

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