Econometric Time Series
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Econometric Time Series
2016-10-16
Revisiting the transitional dynamics of business-cycle phases with mixed frequency data
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01358595&r=ets
This paper introduces a Markov-switching model in which transition probabilities depend on higher frequency indicators and their lags through polynomial weight-ing schemes. The MSV-MIDAS model is estimated via maximum likelihood (ML) methods. The estimation relies on a slightly modified version of Hamilton's recursive filter. We use Monte Carlo simulations to assess the robustness of the estimation procedure and related test statistics. The results show that ML provides accurate estimates, but they suggest some caution in interpreting the tests of the parameters involved in the transition probabilities. We apply this new model to the detection and forecasting of business cycle turning points in the United States. We properly detect recessions by exploiting the link between GDP growth and higher frequency variables from financial and energy markets. The spread term is a particularly useful indicator to predict recessions in the United States. The empirical evidence also supports the use of functional polynomial weights in the MIDAS specification of the transition probabilities.
Marie Bessec
Markov-switching,mixed frequency data,business cycles
2016-09-01
Confidence Sets for the Break Date in Cointegrating Regressions
http://d.repec.org/n?u=RePEc:hit:econdp:2016-07&r=ets
In this paper, we propose constructing confidence sets for a break date in cointegrating regressions by inverting a test for the break location, which is obtained by maximizing the weighted average of power. It is found that the limiting distribution of the test depends on the number of I(1) regressors whose coefficients sustain structural change and the number of I(1) regressors whose coefficients are fixed throughout the sample. By Monte Carlo simulations, we then show that compared with a confidence interval developed by using the existing method based on the limiting distribution of the break point estimator under the assumption of the shrinking shift, the confidence set proposed in the present paper has a more accurate coverage rate, while the length of the confidence set is comparable. By using the method developed in this paper, we then investigate the cointegrating regressions of Russian macroeconomic variables with oil prices with a break.
KUROZUMI, Eiji
SKROBOTOV, Anton
Confidence interval, structural change, cointegration, Russian economy, oil price
2016-09
Interpreting the latent dynamic factors by threshold FAVAR model
http://d.repec.org/n?u=RePEc:boe:boeewp:0622&r=ets
This paper proposes a method to interpret factors which are otherwise difficult to assign economic meaning to by utilizing a threshold factor-augmented vector autoregression (FAVAR) model. We observe the frequency of the factor loadings being induced to zero when they fall below the estimated threshold to infer the economic relevance that the factors carry. The results indicate that we can link the factors to particular economic activities, such as real activity, unemployment, without any prior specification on the data set. By exploiting the flexibility of FAVAR models in structural analysis, we examine impulse response functions of the factors and individual variables to a monetary policy shock. The results suggest that the proposed method provides a useful framework for the interpretation of factors and associated shock transmission.
Hacioglu, Sinem
Tuzcuoglu, Kerem
Factor models; FAVAR; latent threshold; MCMC; interpretation of latent factors; shrinkage estimation
2016-10-07
The Jacobi Stochastic Volatility Model
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01338330&r=ets
We introduce a novel stochastic volatility model where the squared volatility of the asset return follows a Jacobi process. It contains the Heston model as a limit case. We show that the the joint distribution of any finite sequence of log returns admits a Gram-Charlier A expansion in closed-form. We use this to derive closed-form series representations for option prices whose payoff is a function of the underlying asset price trajectory at finitely many time points. This includes European call, put, and digital options, forward start options, and forward start options on the underlying return. We derive sharp analytical and numerical bounds on the series truncation errors. We illustrate the performance by numerical examples, which show that our approach offers a viable alternative to Fourier transform techniques.
Damien Ackerer
Damir Filipovic
Sergio Pulido
Jacobi process,option pricing,polynomial model,stochastic volatility
2016-06-28