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Keywords:time series analysis 

Working Paper
Time-varying oil price volatility and macroeconomic aggregates

We illustrate the theoretical relation among output, consumption, investment, and oil price volatility in a real business-cycle model. The model incorporates demand for oil by a firm, as an intermediate input, and by a household, used in conjunction with a durable good. We estimate a stochastic volatility process for the real price of oil over the period 1986?2011 and utilize the estimated process in a nonlinear approximation of the model. For realistic calibrations, an increase in oil price volatility produces a temporary decrease in durable spending, while precautionary savings motives lead ...
Working Papers , Paper 1201

Working Paper
Financial stress index: a lens for supervising the financial system

This paper develops a new financial stress measure (Cleveland Financial Stress Index, CFSI) that considers the supervisory objective of identifying risks to the stability of the financial system. The index provides a continuous signal of financial stress and broad coverage of the areas that could indicate it. The construction methodology uses daily public market data collected from different sectors of financial markets. A unique feature of the index is that it employs a dynamic weighting method that captures the changing relative importance of the different sectors of the financial system. ...
Working Papers (Old Series) , Paper 12-37

Working Paper
The dynamics of economics functions: modelling and forecasting the yield curve

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or "signal") is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or "y-values") at the knots of the spline. The natural cubic spline provides flexible cross-sectional fit and results in a linear, state space model. This FSN model achieves dimension reduction, provides a coherent description of the observed ...
Working Papers , Paper 0804

Report
Generalized canonical regression

This paper introduces a generalized approach to canonical regression, in which a set of jointly dependent variables enters the left-hand side of the equation as a linear combination, formally like the linear combination of regressors in the right-hand side of the equation. Natural applications occur when the dependent variable is the sum of components that may optimally receive unequal weights or in time series models in which the appropriate timing of the dependent variable is not known a priori. The paper derives a quasi-maximum likelihood estimator as well as its asymptotic distribution ...
Staff Reports , Paper 288

Working Paper
Estimating multivariate ARIMA models: when is close not good enough?

The purpose of this study is to examine the forecasting abilities of the same multivariate autoregressive model estimated using two methods. The first method is the "exact method" used by the SCA System from Scientific Computing Associates. The second method is an approximation method as implemented in the MTS system by Automatic Forecasting Systems, Inc. ; The two methods were used to estimate a five-series multivariate autoregressive model for the Quenouille series on hog numbers, hog prices, corn prices, corn supply, and farm wage rates. The 82 observations were arbitrarily divided into ...
Working Papers (Old Series) , Paper 8711

Working Paper
A Gibbs simulator for restricted VAR models

Many economic applications call for simultaneous equations VAR modeling. We show that the existing importance sampler can be prohibitively inefficient for this type of models. We develop a Gibbs simulator that works for both simultaneous and recursive VAR models with a much broader range of linear restrictions than those in the existing literature. We show that the required computation is of an SUR type, and thus our method can be implemented cheaply even for large systems of multiple equations.
FRB Atlanta Working Paper , Paper 2000-3

Working Paper
The stochastic coefficients approach to econometric modeling, part II: description and motivation

Finance and Economics Discussion Series , Paper 30

Working Paper
Information-aggregation bias

Aggregation in the presence of data processing lags distorts the information content of data, violating orthogonality restrictions that hold at the individual level. Though the phenomenon is general, it is illustrated here for the life cycle-permanent model. Cross-section and pooled-panel data induce information-aggregation bias akin to that in aggregate time series. Calculations show that information-aggregation can seriously bias tests of the life cycle model on aggregate time series, cross-section, and pooled-panel data.
Working Paper , Paper 91-06

Working Paper
Forecasting an aggregate of cointegrated disaggregates

This study examines the problem of forecasting an aggregate of cointegrated disaggregates. It first establishes conditions under which forecasts of an aggregate variable obtained from a disaggregate VECM will be equal to those from an aggregate, univariate time series model, and develops a simple procedure for testing those conditions. The paper then uses Monte Carlo simulations and an empirical example to examine how analysis of forecasting an aggregate might be affected by a failure to correct for cointegration. The Monte Carlo and empirical analyses indicate the effects of ignoring ...
Research Working Paper , Paper 95-13

Conference Paper
Economic and financial data as nonlinear processes

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Diebold, Francis X. 11 items

Bagshaw, Michael L. 9 items

Potter, Simon M. 6 items

Dueker, Michael J. 5 items

Estrella, Arturo 5 items

Zha, Tao 5 items

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