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Nonlinear time series modelling: an introduction
Potter, Simon M. (1999)
Recent developments in nonlinear time series modelling are reviewed. Three main types of nonlinear models are discussed: Markov Switching, Threshold Autoregression and Smooth Transition Autoregression. Classical and Bayesian estimation techniques are described for each model. Parametric tests for nonlinearity are reviewed with examples from the three types of models. Finally, forecasting and impulse response analysis is developed.
Staff Reports , Paper 87

Working Paper
Some Monte Carlo results on nonparametric changepoint tests
Bryden, Edward J.; Carlson, John B.; Craig, Ben R. (1995)
An examination of the small-sample properties of nonparametric changepoint tests using Monte Carlo analysis to investigate the probabilities of false-positive tests under alternative assumptions about the time-series properties of the underlying process. ; An analysis of whether depositor preference legislation reduced the FDIC's failed-bank resolution costs in 1984-92, and whether nondepositors' responses may have partially undone the intended benefits of such legislation.
Working Papers (Old Series) , Paper 9517

Working Paper
A comparison of two approaches to measuring common and idiosyncratic components in sets of time series variables
Clark, Todd E. (1994)
Research Working Paper , Paper 94-04

Report
Dynamic factor models with time-varying parameters: measuring changes in international business cycles
Otrok, Christopher; Del Negro, Marco (2008)
We develop a dynamic factor model with time-varying factor loadings and stochastic volatility in both the latent factors and idiosyncratic components. We employ this new measurement tool to study the evolution of international business cycles in the post-Bretton Woods period, using a panel of output growth rates for nineteen countries. We find 1) statistical evidence of a decline in volatility for most countries, with the timing, magnitude, and source (international or domestic) of the decline differing across countries; 2) some evidence of a decline in business cycle synchronization for ...
Staff Reports , Paper 326

Working Paper
Efficient tests for autoregressive unit roots in panel data
Bowman, David (1999)
In this paper the class of admissable tests for unit roots in panel data sets of autoregressive, Gaussian time series will be partially characterized. Using this characterization, several recently suggested tests are shown to be inadmissable. Since the sufficient statistic for this testing problem is multidimensional, there is no uniformly most powerful test; however, in light of the inadmissability result, a new test is proposed that appears to do well relative to existing tests. The test is parameterized in a way that allows the choice of different directional deviations from the null ...
International Finance Discussion Papers , Paper 646

Working Paper
Testing for cointegration when some of the cointegrating vectors are known
Watson, Mark W.; Horvath, Michael T. K. (1993)
Working Paper Series, Macroeconomic Issues , Paper 93-15

Working Paper
Econometric modeling of exchange rate volatility and jumps
Neely, Christopher J.; Erdemlioglu, Deniz; Laurent, Sebastien (2012)
This chapter reviews the rapid advances in foreign exchange volatility modeling made in the last three decades. Academic researchers have sought to fit the three major characteristics of foreign exchange volatility: intraday periodicity, autocorrelation and discontinuities in prices. Early research modeled the autocorrelation in daily and weekly squared foreign exchange returns with ARCH/GARCH models. Increased computing power and availability of high-frequency data allowed later researchers to improve volatility and jumps estimates. Researchers also found it useful to incorporate information ...
Working Papers , Paper 2012-008

Working Paper
GARCH-based identification of triangular systems with an application to the CAPM: still living with the roll critique
Prono, Todd (2006)
This paper presents a new method for identifying triangular systems of time-series data. Identification is the product of a bivariate GARCH process. Relative to the literature on GARCH-based identification, this method distinguishes itself both by allowing for a time-varying covariance and by not requiring a complete estimation of the GARCH parameters. Estimation follows OLS and standard univariate GARCH and ARMA techniques, or GMM. A Monte Carlo study of the GMM estimator is provided. The identification method is then applied in testing a conditional version of the CAPM. ; Quantitative ...
Working Papers , Paper 07-1

Working Paper
Cointegration and transformed series
Hallman, Jeffrey J. (1990)
An explanation of how to use nonparametric techniques to search for and test possible cointegrating transformations of time series.
Working Papers (Old Series) , Paper 9014

Working Paper
Trends and random walks in macroeconomic time series: a re-examination
Rudebusch, Glenn D. (1990)
Working Paper Series / Economic Activity Section , Paper 105

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