The vector floor and ceiling model

This paper motivates and develops a nonlinear extension of the Vector Autoregressive model which we call the Vector Floor and Ceiling model. Bayesian and classical methods for estimation and testing are developed and compared in the context of an application involving U.S. macroeconomic data. In terms of statistical significance both classical and Bayesian methods indicate that the (Gaussian) linear model is inadequate. Using impulse response functions we investigate the economic significance of the statistical analysis. We find evidence of strong nonlinearities in the contemporaneous relationships between the variables and milder evidence of nonlinearity in the conditional mean

Time varying VARs with inequality restrictions

In many applications involving time-varying parameter VARs, it is desirable to restrict the VAR coe¢ cients at each point in time to be non-explosive. This is an example of a problem where inequality restrictions are imposed on states in a state space model. In this paper, we describe how existing MCMC algorithms for imposing such inequality restrictions can work poorly (or not at all) and suggest alternative algorithms which exhibit better performance. Furthermore, previous algorithms involve an approximation relating to a key integrating constant. Our algorithms are exact, not involving this approximation. In an application involving a commonly-used U.S. data set, we show how this approximation can be a poor one and present evidence that the algorithms proposed in this paper work well

Prior elicitation in multiple change-point models

This paper discusses Bayesian inference in change-point models. Existing approaches involve placing a (possibly hierarchical) prior over a known number of change-points. We show how two popular priors have some potentially undesirable properties (e.g. allocating excessive prior weight to change-points near the end of the sample) and discuss how these properties relate to imposing a fixed number of changepoints in-sample. We develop a new hierarchical approach which allows some of of change-points to occur out-of sample. We show that this prior has desirable properties and handles the case where the number of change-points is unknown. Our hierarchical approach can be shown to nest a wide variety of change-point models, from timevarying parameter models to those with few (or no) breaks. Since our prior is hierarchical, data-based learning about the parameter which controls this variety occurs

Are apparent findings of nonlinearity due to structural instability in economic time series?

Many modelling issues and policy debates in macroeconomics depend on whether macroeconomic times series are best characterized as linear or nonlinear. If departures from linearity exist, it is important to know whether these are endogenously generated (as in, e.g., a threshold autoregressive model) or whether they merely reflect changing structure over time. We advocate a Bayesian approach and show how such an approach can be implemented in practice. An empirical exercise involving several macroeconomic time series shows that apparent findings of threshold type nonlinearities could be due to structural instability

The Vector Floor and Ceiling Model

This paper motivates and develops a nonlinear extension of the Vector Autoregressive model which we call the Vector Floor and Ceiling model. Bayesian and classical methods for estimation and testing are developed and compared in the context of an application involving U.S. macroeconomic data. In terms of statistical significance both classical and Bayesian methods indicate that the (Gaussian) linear model is inadequate. Using impulse response functions we investigate the economic significance of the statistical analysis. We find evidence of strong nonlinearities in the contemporaneous relationships between the variables and milder evidence of nonlinearity in the conditional mean.Nonlinearity; Bayesian; Vector Autoregression

Prior Elicitation in Multiple Change-point Models

This paper discusses Bayesian inference in change-point models. The main existing approaches either attempt to be noninformative by using a Uniform prior over change-points or use an informative hierarchical prior. Both these approaches assume a known number ofchange-points. We show how they have some potentially undesirable properties and discuss how these properties relate to the imposition of a …xed number of changepoints. We develop a new Uniform prior which allows some of the change-points to occur out-of sample. This prior has desirable properties, can reasonably be interpreted as “noninformative” and handles the case where the number of change-points is unknown. We show how the general ideas of our approach can be extended to informative hierarchical priors. With arti…cial data and two empirical illustrations, we show how these di¤erent priors can have a substantial impact on estimation and prediction even with moderately large data sets.

Understanding Liquidity and Credit Risks in the Financial Crisis*

This paper develops a structured dynamic factor model for the spreads between London Interbank Offered Rate (LIBOR) and overnight index swap (OIS) rates for a panel of banks. Our model involves latent factors which relect liquidity and credit risk. Our empirical results show that surges in the short term LIBOR-OIS spreads during the 2007-2009 financial crisis were largely driven by liquidity risk. However, credit risk played a more significant role in the longer term (twelve-month) LIBOR-OIS spread. The liquidity risk factors are more volatile than the credit risk factor. Most of the familiar events in the financial crisis are linked more to movements in liquidity risk than credit risk.LIBOR-OIS spread, factor model, credit default swap, Bayesian

Real gates to virtual fields:Integrating online and offline ethnography in studying cannabis cultivation and reflections on the applicability of this approach in criminological ethnography more generally

This paper explores the interplay between online and offline approaches in criminological ethnography. Criminology has come to embrace online research: as well as offering numerous research benefits generic to the social sciences, the internet offers solutions to various problems specific to active offender research. Further, as many types of criminal or deviant behaviour increasingly have online aspects, so engaging in online research becomes both valid and vital to any meaningful ethnography. However, online approaches should be treated with caution: they are subject to their own limitations, and to rely on online methods as an alternative to traditional approaches can be as problematic as failing to embrace online research at all. Drawing on my experiences researching cannabis cultivation, I demonstrate some of the ways that offline and online methods complement one another. Online methods were useful in expanding my own study beyond the normal constraints of ethnography by generating a larger and more varied sample, and providing access to more data than traditional ethnographic approaches. They were also essential for exploring the various online aspects of cannabis cultivation. But offline methods proved invaluable in accessing and recruiting respondents online, and in providing the experience essential to participating in – and understanding – cultivation-related online interactions. Both approaches revealed findings not identified by the other, and research in each environment helped with understanding experiences and observations in the other. I argue that while there are clear strengths in online approaches to criminological ethnography, certain pitfalls arise when online techniques are used without employing face-to-face research as well. Triangulation of online and offline methods can enhance the understanding of many human behaviours, but may be particularly useful in overcoming the difficulties inherent in criminological ethnography. For many (although by no means all) criminological topics, online methods can usefully enhance, but not replace, traditional ethnographic techniques

Understanding Liquidity and Credit Risks in the Financial Crisis

This paper develops a structured dynamic factor model for the spreads between London Interbank Offered Rate (LIBOR) and overnight index swap (OIS) rates for a panel of banks. Our model involves latent factors which reflect liquidity and credit risk. Our empirical results show that surges in the short term LIBOR-OIS spreads during the 2007-2009 fi…nancial crisis were largely driven by liquidity risk. However, credit risk played a more signi…cant role in the longer term (twelve-month) LIBOR-OIS spread. The liquidity risk factors are more volatile than the credit risk factor. Most of the familiar events in the fi…nancial crisis are linked more to movements in liquidity risk than credit risk.