nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒01‒22
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Sparse Bayesian time-varying covariance estimation in many dimensions By Gregor Kastner
  2. Frequency Domain Estimation of Cointegrating Vectors with Mixed Frequency and Mixed Sample Data By Chambers, MJ
  3. Maximum Likelihood Estimation in Possibly Misspecified Dynamic Models with Time-Inhomogeneous Markov Regimes By Demian Pouzo; Zacharias Psaradakis; Martin Sola
  4. Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks By Christiane J.S. Baumeister; James D. Hamilton
  5. A Flexible Fourier Form Nonlinear Unit Root Test Based on ESTAR Model By Güriş, Burak
  6. Macroeconomic Indicator Forecasting with Deep Neural Networks By Cook, Thomas R.; Smalter Hall, Aaron
  7. Predict Forex Trend via Convolutional Neural Networks By Yun-Cheng Tsai; Jun-Hao Chen; Jun-Jie Wang
  8. A Dynamic Correlation Analysis of Financial Contagion: Evidence from the Eurozone Stock Markets By Trabelsi, Mohamed Ali; Hmida, Salma

  1. By: Gregor Kastner
    Abstract: We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global-local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500 members. Our approach yields precise correlation estimates, strong implied minimum variance portfolio performance and superior forecasting accuracy in terms of log predictive scores when compared to typical benchmarks.
    Date: 2016–08
  2. By: Chambers, MJ
    Abstract: This paper proposes a model suitable for exploiting fully the information contained in mixed frequency and mixed sample data in the estimation of cointegrating vectors. The asymptotic properties of easy-to-compute spectral regression estimators of the cointegrating vectors are derived and these estimators are shown to belong to the class of optimal cointegration estimators. Furthermore, Wald statistics based on these estimators have asymptotic chi-square distributions which enable inferences to be made straightforwardly. Simulation experiments suggest that the finite sample performance of a spectral regression estimator in an augmented mixed frequency model is particularly encouraging as it is capable of dramatically reducing the root mean squared error obtained in an entirely low frequency model to the levels comparable to an infeasible high frequency model. The finite sample size and power properties of the Wald statistic are also found to be good. An empirical example, to stock price and dividend data, is provided to demonstrate the methods in practice.
    Keywords: mixed frequency data, mixed sample data, cointegration, spectral regression
    Date: 2018–01
  3. By: Demian Pouzo; Zacharias Psaradakis; Martin Sola
    Abstract: This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency and local asymptotic normality of the ML estimator under general conditions which allow for autoregressive dynamics in the observable process, time-inhomogeneous Markov regime sequences, and possible model misspecification. A Monte Carlo study examines the finite-sample properties of the ML estimator. An empirical application is also discussed.
    Date: 2016–12
  4. By: Christiane J.S. Baumeister; James D. Hamilton
    Abstract: Traditional approaches to structural vector autoregressions can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global economic activity after a significant lag, whereas shocks to oil demand do not.
    JEL: C32 E32 Q43
    Date: 2017–12
  5. By: Güriş, Burak
    Abstract: This study suggests a new nonlinear unit root test procedure with Fourier function. In this test procedure, structural breaks are modeled by means of a Fourier function and nonlinear adjustment is modeled by means of an Exponential Smooth Threshold Autoregressive (ESTAR) model. The Monte Carlo simulation results indicate that the proposed test has good size and power properties. This test eliminates the problems of over-acceptance of the null of nonstationarity to allow multiple smooth temporary breaks and nonlinearity together into the test procedure.
    Keywords: Flexible Fourier Form, Unit Root Test, Nonlinearity
    JEL: C12 C2 C22
    Date: 2017–12
  6. By: Cook, Thomas R. (Federal Reserve Bank of Kansas City); Smalter Hall, Aaron (Federal Reserve Bank of Kansas City)
    Abstract: Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models {{p}} that exhibit model dependence and have high data demands. {{p}} We explore deep neural networks as an {{p}} opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to {{p}} functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, based on an Encoder Decoder architecture outperforms benchmark models at every forecast horizon (up to four quarters).
    Keywords: Neural networks; Forecasting; Macroeconomic indicators
    JEL: C14 C45 C53
    Date: 2017–09–29
  7. By: Yun-Cheng Tsai; Jun-Hao Chen; Jun-Jie Wang
    Abstract: Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts; this study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The three steps involved are as follows: 1. Before training, we pre-process the input data from quantitative data to images. 2. We use a convolutional neural network (CNN), a type of deep learning, to train our trading model. 3. We evaluate the model's performance in terms of the accuracy of classification. A trading model is obtained with this approach to help devise trading strategies. The main application is designed to help clients automatically obtain personalized trading strategies.
    Date: 2018–01
  8. By: Trabelsi, Mohamed Ali; Hmida, Salma
    Abstract: The contagion generated by the US subprime crisis and the European sovereign debt crisis that hit the Eurozone stock markets is still a highly debated subject. In this paper, we try to determine whether there are contagion effects across the Greek stock market and the Belgian, French, Portuguese, Irish, Italian and Spanish stock markets during both crises periods. To this end, we used a bivariate DCC-GARCH model to measure the extent of dynamic correlations between stock returns of our sample. Our results point to the presence of a contagion effect between all market pairs during the subprime crisis and between the Greek and Portuguese stock markets during the European sovereign debt crisis. On the other hand, our results indicate that credit ratings revisions have a relatively limited effect on the dynamic correlations of the Eurozone stock markets.
    Keywords: Financial contagion; European debt crisis; Dynamic conditional correlations
    JEL: C22 G01 G15
    Date: 2017

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