A growing literature uses now widely available data on beliefs and expectations in the estimation of structural models. In this chapter, we review this literature, with an emphasis on models of individual and household behavior. We first show how expectations data have been used to relax strong...

Using a unique data set of individual professional forecasts, we document disagreement about the future path of monetary policy, particularly at longer horizons. The stark differences in short rate forecasts imply strong disagreement about the risk-return trade-off of longer-term bonds....

To gain insights in the current status of the economy, macroeconomic time series are often decomposed into trend, cycle and irregular components. This can be done by nonparametric band-pass filtering methods in the frequency domain or by model-based decompositions based on autoregressive moving...

Korean Abstract : 이 논문에서는 한국의 주별 익일물 콜금리를 사용해 국면전환 확산과정모형을 추정했다. 일반적인 비선형 추세 함수와 분산 탄력성이 상수인 변동성 함수를 갖는 확산과정모형에서 모수들이 경제상황에 따라...

Recently various exchange rate models capturing the dynamics during the transition from an exchange rate arrangement of floating rates into a currency union have been derived. Technically, these stochastic equilibrium models are diffusion processes which have to be estimated by discretely...

We define risk spillover as the dependence of a given asset variance on the past covariances and variances of other assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an international equity portfolio. According to the risk...

Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to ensure robustness and correct the downward bias of RS measure with an additive term. Moreover scaling factors are provided for different interquantile ranges to ensure unbiasedness....

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

We develop a method for directly modeling cointegrated multivariate time series that are observed in mixed frequencies. We regard lower-frequency data as regularly (or irregularly) missing and treat them with higher-frequency data by adopting a state-space model. This utilizes the structure of...

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...