1,270 research outputs found

    Time Series Analysis

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    We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.time series analysis, time domain, frequency domain

    Time Series Analysis

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    We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.time series analysis, time domain, frequency domain, Research Methods/ Statistical Methods,

    Nonlinearities and fractional integration in the US unemployment rate

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    This paper proposes a model of the US unemployment rate which accounts for both its asymmetry and its long memory. Our approach introduces fractional integration and nonlinearities simultaneously into the same framework, using a Lagrange Multiplier procedure with a standard null limit distribution. The empirical results suggest that the US unemployment rate can be specified in terms of a fractionally integrated process, which interacts with some non-linear functions of labour demand variables such as real oil prices and real interest rates. We also find evidence of a long-memory component. Our results are consistent with a hysteresis model with path dependency rather than a NAIRU model with an underlying unemployment equilibrium rate, thereby giving support to more activist stabilisation policies. However, any suitable model should also include business cycle asymmetries, with implications for both forecasting and policy-making.

    Forecasting interest rate swap spreads using domestic and international risk factors: Evidence from linear and non-linear models.

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    This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non-linear framework. We reject linearity for the US and UK swap spreads in favour of a regime-switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non-linear nearest-neighbours model as well as that of linear AR and VAR models. We find some evidence that the non-linear models predict better than the linear ones. At short horizons, the nearest-neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models.Interest rate swap spreads, term structure of interest rates, factor models, regime switching, smooth transition models, nearest-neighbours, forecasting.

    Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model

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    The purpose of this paper is to compare the forecasting power of DFM and LBVAR models as they are used to forecast house price growth rates for 42 metropolitan areas in the United States. The forecasting performances of these two large-scale models are compared based on the Theil U-statistic.Housing market, DFM, LBVAR, dynamic PCA, Demand and Price Analysis,

    On the predictability of common risk factors in the US and UK interest rate swap markets: Evidence from non-linear and linear models.

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    This paper explores the ability of common risk factors to predict the dynamics of US and UK interest rate swap spreads within a linear and a non-linear framework. We reject linearity for the US and UK swap spreads in favour of a regime-switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. The first regime is characterised by a "flat" term structure of US interest rates, while the alternative is characterised by an "upward" sloping US term structure. We compare the ability of the STVAR model to predict swap spreads with that of a non-linear nearest-neighbours model as well as that of linear AR and VAR models. We find some evidence that the nearest-neighbours and STVAR models predict better than the linear AR and VAR models. However, the evidence is not overwhelming as it is sensitive to swap spread maturity. We also find that within the non-linear class of models, the nearest-neighbours model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions.Interest rate swap spreads, term structure of interest rates, regime switching, smooth transition models, nearest-neighbours, forecasting.

    The Nobel Memorial Prize for Robert F. Engle

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    I review and interpret two of Robert Engle's most important contributions: the theory and application of cointegration, and the theory and application of dynamic volatility models. I treat the latter much more extensively, de-emphasizing technical aspects and focusing instead on the intuition, nuances and importance of the work.
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