Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

Forecasting Inflation in China

This paper forecasts inflation in China over a 12-month horizon. The analysis runs 15 alternative models and finds that only those considering many predictors via a principal component display a better relative forecasting performance than the univariate benchmark.inflation forecasting; data-rich environment; principal components; China

Structural Time Series Models and the Kalman Filter: a concise review

The continued increase in availability of economic data in recent years and, more impor- tantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci?cations we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman ?lter algorithm is described taking into account its di¤erent stages, from initialisation to parameter?s estimation. JEL codes: C10, C22, C32

Filtering and Smoothing with Score-Driven Models

We propose a methodology for filtering, smoothing and assessing parameter and filtering uncertainty in misspecified score-driven models. Our technique is based on a general representation of the well-known Kalman filter and smoother recursions for linear Gaussian models in terms of the score of the conditional log-likelihood. We prove that, when data are generated by a nonlinear non-Gaussian state-space model, the proposed methodology results from a first-order expansion of the true observation density around the optimal filter. The error made by such approximation is assessed analytically. As shown in extensive Monte Carlo analyses, our methodology performs very similarly to exact simulation-based methods, while remaining computationally extremely simple. We illustrate empirically the advantages in employing score-driven models as misspecified filters rather than purely predictive processes.Comment: 33 pages, 5 figures, 6 table

Do so-called multivariate filters have better revision properties? An empirical analysis

The output gap plays a crucial role in thinking and actions of many central banks but real time measurements undergo substantial revisions as more data become available (Orphanides (2001), Orphanides and van Norden (forthcoming)). Some central banks augment, such as the Bank of Canada and the Reserve Bank of New Zealand, the Hodrick and Prescott (1997) filter with conditioning structural information to mitigate the impact of revisions to the output gap estimates. In this paper, we use a state space Kalman filter framework to examine whether the augmented (so-called “multivariate filtersâ€) achieve this objective. We find that the multivariate filters are no better than the Hodrick-Prescott filter for real-time NZ data. The addition of structural equations increase the number of signal equations, but at the same time adds more unobserved trend/equilibrium variables to the system. We find that how these additional trends/equilibrium values are treated matters a lot, and they increase the uncertainty around the estimates. In addition, the revisions from these models can be as large as a univariate Hodrick-Prescott filter.output gap, real time, multivariate filters

Instability in U.S. inflation: 1967-2005

Maintaining stables prices and keeping inflation in check have become key policy objectives of the Federal Reserve and other central banks. Evidence indicates that inflation has become less persistent and volatile since the early 1980s. Although economists have examined the implications for inflation modeling and forecasting, little information exists about whether changes or instabilities in inflation dynamics coincide with specific economic events such as oil price shocks or recessions. ; This article studies U.S. monthly inflation, inflation growth, and price level dynamics from January 1967 to September 2005. The author employs four price level measures—two versions of the monthly consumer price index and two versions of the monthly personal consumption expenditure deflator—with the goal of identifying possible instabilities in these dynamics. ; Autoregressive, moving average, and unobserved components models provide estimates on various aspects of inflation and price levels. Two rolling samples spanning the 1967–2005 period are constructed to uncover evidence about possible instability in mean inflation and the persistence and volatility of inflation and inflation growth. ; One way to summarize the empirical results is that this instability coincides with different economic events such as the oil price shocks of the 1970s or the end of the 1990–91 recession. An unresolved question is whether such changes are one-time events or can be expected to be repeated systematically in the future.Inflation (Finance)

International SVAR Factor Modelling

Models of Australia proxy international linkages using the US, despite Japan being an equivalent trading partner. This paper uses a Kahnan filter to extract US and Japanese reference cycles which are then used in an SVAR model of the Australian economy. The US and Japanese shocks are interpreted to be aggregate demand and interest rate shocks respectively. The results show that US shocks axe dominant for Australian outcomes, but the model is misspecified if Japan is excluded. The role of Japan is to dampen expansionary US shocks. Further, Australian monetary policy responds to domestic conditions, rather than international monetary policy.Structural VAR, latent factors, Kalman filter.

Why Are Beveridge-Nelson and Unobserved-Component Decompositions of GDP So Different?

This paper reconciles two widely-used decompositions of GDP into trend and cycle that yield starkly different results. Beveridge-Nelson (BN) implies that a stochastic trend accounts for most of the variation in output, while Unobserved-Components (UC) implies cyclical variation is dominant. Which is correct has broad implications for the relative importance of real versus nominal shocks. We show the difference arises from the restriction imposed in UC that trend and cycle innovations are uncorrelated. When this restriction is relaxed, the UC decomposition is identical to the BN decomposition. Furthermore, the zero correlation restriction can be rejected for U.S. quarterly GDP, with the estimated correlation being –0.9.