nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒12‒11
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Wavelet-based methods for high-frequency lead-lag analysis By Takaki Hayashi; Yuta Koike
  2. A Bayesian Infinite Hidden Markov Vector Autoregressive Model By Didier Nibbering; Richard Paap; Michel van der Wel
  3. Macroeconomic now- and forecasting based on the factor error correction model using targeted mixed frequency indicators By Kurz-Kim, Jeong-Ryeol
  4. Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates By Matthieu Garcin
  5. Adaptive models and heavy tails with an application to inflation forecasting By Delle Monache, Davide; Petrella, Ivan

  1. By: Takaki Hayashi; Yuta Koike
    Abstract: We propose a novel framework to investigate lead-lag relationships between two financial assets. Our framework bridges a gap between continuous-time modeling based on Brownian motion and the existing wavelet methods for lead-lag analysis based on discrete-time models and enables us to analyze the multi-scale structure of lead-lag effects. We also present a statistical methodology for the scale-by-scale analysis of lead-lag effects in the proposed framework and develop an asymptotic theory applicable to a situation including stochastic volatilities and irregular sampling. Finally, we report several numerical experiments to demonstrate how our framework works in practice.
    Date: 2016–12
  2. By: Didier Nibbering (Erasmus University Rotterdam, The Netherlands); Richard Paap (Erasmus University Rotterdam, The Netherlands); Michel van der Wel (Erasmus University Rotterdam, The Netherlands)
    Abstract: We propose a Bayesian infinite hidden Markov model to estimate time-varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model, parameters can vary over potentially an infinite number of regimes. The Dirichlet process however favours a parsimonious model without imposing restrictions on the parameter space. An empirical application demonstrates the ability of the model to capture both smooth and abrupt parameter changes over time, and a real-time forecasting exercise shows excellent predictive performance even in large dimensional VARs.
    Keywords: Time-Varying Parameter Vector Autoregressive Model; Semi-parametric Bayesian Inference; Dirichlet Process Mixture Model; Hidden Markov Chain; Monetary Policy Analysis; Real-time Forecasting
    JEL: C11 C14 C32 C51 C54
    Date: 2016–12–06
  3. By: Kurz-Kim, Jeong-Ryeol
    Abstract: Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling technique. Other improvements are also studied mostly in two directions: a pre-selection is used to optimally choose a small number of indicators from a large number of indicators. The error correction mechanism takes into account the co-integrating relationship between the key variables and factors and, hence, captures the long-run dynamics of the non-stationary macroeconomic variables. This papers proposes the factor error correction model using targeted mixedfrequency indicators, which combines the three refinements for the dynamic factor model, namely the mixed data sampling technique, pre-selection methods, and the error correction mechanism. The empirical results based on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset models.
    Keywords: Factor model,MIDAS,Lasso,Elastic Net,ECM,Nowcasting,Forecasting
    JEL: C18 C23 C51 C52 C53
    Date: 2016
  4. By: Matthieu Garcin (Natixis Asset Management, Labex ReFi - Université Paris1 - Panthéon-Sorbonne)
    Abstract: Hurst exponents depict the long memory of a time series. For human-dependent phenomena, as in finance, this feature may vary in the time. It justifies modelling dynamics by multifractional Brownian motions, which are consistent with time-varying Hurst exponents. We improve the existing literature on estimating time-dependent Hurst exponents by proposing a smooth estimate obtained by variational calculus. This method is very general and not restricted to the sole Hurst framework. It is globally more accurate and easier than other existing non-parametric estimation techniques. Besides, in the field of Hurst exponents, it makes it possible to make forecasts based on the estimated multifractional Brownian motion. The application to high-frequency foreign exchange markets (GBP, CHF, SEK, USD, CAD, AUD, JPY, CNY and SGD, all against EUR) shows significantly good forecasts. When the Hurst exponent is higher than 0.5, what depicts a long-memory feature, the accuracy is higher.
    Keywords: Hurst exponent,Euler-Lagrange equation,non-parametric smoothing,foreign exchange forecast,Multifractional brownian motion
    Date: 2016–11–19
  5. By: Delle Monache, Davide; Petrella, Ivan
    Abstract: This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, the resulting model is observation-driven and is estimated by classical methods. In particular, we consider time variation in both coefficients and volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters so as to attain local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics with the following results: allowing for heavy tails leads to significant improvements in terms of fit and forecast, and the adoption of the Student-t distribution proves to be crucial in order to obtain well calibrated density forecasts. These results are obtained using the US CPI inflation rate and are confirmed by other inflation indicators, as well as for CPI inflation of the other G7 countries.
    Keywords: adaptive algorithms, inflation, score-driven models, student-t, time-varying parameters.
    JEL: C22 C51 C53 E31
    Date: 2016–09–01

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