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Signal extraction for nonstationary multivariate time series with illustrations for trend inflation
This paper advances the theory and methodology of signal extraction by introducing asymptotic and finite sample formulas for optimal estimators of signals in nonstationary multivariate time series. Previous literature has considered only univariate or stationary models. However, in current practice and research, econometricians, macroeconomists, and policy-makers often combine related series - that may have stochastic trends--to attain more informed assessments of basic signals like underlying inflation and business cycle components. Here, we use a very general model structure, of widespread ...
Continuous time extraction of a nonstationary signal with illustrations in continuous low-pass and band-pass filtering
This paper sets out the theoretical foundations for continuous-time signal extraction in econometrics. Continuous-time modeling gives an effective strategy for treating stock and flow data, irregularly spaced data, and changing frequency of observation. We rigorously derive the optimal continuous-lag filter when the signal component is nonstationary, and provide several illustrations, including a new class of continuous-lag Butterworth filters for trend and cycle estimation.
Improving real-time estimates of the output gap
This paper investigates strategies for real-time estimation of the output gap. First, I examine estimates from univariate models with stochastic cycles. This corresponds to the use of model-based band-pass filters in real-time, and I find that the turning points in real-time and final output gap series match more closely for higher order models and that the revisions properties and real-time accuracy are more favorable. Second, I investigate the use of capacity utilization as an auxiliary indicator to improve on output gap estimates in real-time. I find that this bivariate approach leads to ...