A Simple Test of the New Keynesian Phillips Curve

We propose a way to test the New Keynesian Phillips Curve (NKPC) without estimating the structural parameters governing the curve, i.e. price stickiness and firms' backwardness. Using this strategy we can test the NKPC avoiding the identification problems related to the GMM approach. We find that it does not exist a combination of the structural parameters which is consistent with US data. This result does not necessarily imply that the idea of a forward looking price setting behaviour should be entirely disregarded, as the rejection might be due to the failure of the joint hypothesis of rational expectations. Thus further research should be aimed at providing alternative models for agents' expectations.VARs, Inflation, Phillips curve

Forecasting the Yield Curve Using Priors from No Arbitrage Affine Term Structure Models

In this paper we propose a strategy for forecasting the term structure of interest rates which may produce significant gains in predictive accuracy. The key idea is to use the restrictions implied by Affine Term Structure Models (ATSM) on a vector autoregression (VAR) as prior information rather than imposing them dogmatically. This allows to account for possible model misspecification. We apply the method to a system of five US yields, and we find that the gains in predictive accuracy can be substantial. In particular, for horizons longer than 1-step ahead, our proposed method produces systematically better forecasts than those obtained by using a pure ATSM or an unrestricted VAR, and it also outperforms very competitive benchmarks as the Minnesota prior, the Diebold-Li (2006) model, and the random walk.Bayesian methods, Forecasting, Term structure

A Bayesian Framework for the Expectations Hypothesis. How to Extract Additional Information from the Term Structure of Interest Rates

Even if there is a fairly large evidence against the Expectations Hypothesis (EH) of the term structure of interest rates, there still seems to be an element of truth in the theory which may be exploited for forecasting and simulation. This paper formalizes this idea by proposing a way to use the EH without imposing it dogmatically. It does so by using a Bayesian framework such that the extent to which the EH is imposed on the data is under the control of the researcher. This allows to study a continuum of models ranging from one in which the EH holds exactly to one in which it does not hold at all. In between these two extremes, the EH features transitory deviations which may be explained by time varying (but stationary) term premia and errors in expectations. Once cast in this framework, the EH holds on average (i.e. after integrating out the effect of the transitory deviations) and can be safely and effectively used for forecasting and simulation.Bayesian VARs, Expectations theory, Term structure

Modelling gait abnormalities and bone deformities in children with cerebel palsy

Cerebral palsy (CP) is a neuromuscular disorder that affects the motor control of muscles. CP children exhibit abnormal walking patterns and frequently develop lower limb, long bone deformities. To improve functionality and guide orthopaedic treatments effectively, it is critical to elucidate the relationship existing between bone morphology and movement of the lower limbs CP children. The hypothesis of this study is that gait abnormalities result in bone deformities. The investigation of this complex relationship represents the core of this thesis. The examination of magnetic resonance images and gait analysis of healthy and CP children showed different development in femoral and tibial morphology and varied gait characteristics between them. Similarly, different correlations between bone morphology and gait characteristics resulted in healthy and CP children. Gait characteristics also varied between CP children. An objective and quantitative graphical classification method of CP gait patterns was developed. This classified the CP children in overlapping clusters according to their gait patterns, confirming the presence of multiple gait abnormalities on the same lower limb for CP children. With the intention to define the effect of the walking characteristics on the bone structure, femoral muscle and hip contact forces in healthy and CP children with different walking strategies were estimated by using inverse dynamic analysis. The different gait styles resulted in different loadings on the developing femur bone. These constituted the loading conditions for bone growth analysis. A three-dimensional finite element model for femoral growth was developed and mechanobiological theories applied in order to predict femur changes over time in healthy and CP children. The models predicted higher femoral anteversion and neck3 shaft angle formation in children with CP, emphasizing how different gait characteristics can influence bone morphology. This information has potential to explain and eventually prevent or treat the development of bone deformities in CP children

A Comparison of Methods for the Construction of Composite Coincident and Leading Indexes for the UK

In this paper we provide an overview of recent developments in the methodology for the construction of composite coincident and leading indexes, and apply them to the UK. In particular, we evaluate the relative merits of factor based models and Markov switching specifications for the construction of coincident and leading indexes. For the leading indexes we also evaluate the performance of probit models and pooling. The results indicate that alternative methods produce similar coincident indexes, while there are more marked di.erences in the leading indexes.Forecasting, Business cycles, Leading indicators, Coincident indicators, Turning points

Forecasting Large Datasets with Reduced Rank Multivariate Models

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance with the most promising existing alternatives, namely, factor models, large scale bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank bayesian VAR of Geweke (1996). As a result, we found that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast, and for key variables such as industrial production growth, inflation, and the federal funds rate.Bayesian VARs, Factor models, Forecasting, Reduced rank

Forecasting Government Bond Yields with Large Bayesian VARs

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. Focusing on the U.S., we provide an extensive study on the forecasting performance of our proposed model relative to most of the existing alternative speci.cations. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also evaluate the performance of the alternative forecasts when used within trading schemes or as a basis for portfolio allocation. We extensively check the robustness of our results via subsample analysis and via a data based Monte Carlo simulation. We .nd that: i) our proposed BVAR approach produces forecasts systematically more accurate than the random walk forecasts, though the gains are small; ii) some models beat the BVAR for a few selected maturities and forecast horizons, but they perform much worse than the BVAR in the remaining cases; iii) predictive gains with respect to the random walk have decreased over time; iv) di¤erent loss functions (i.e., "statistical" vs "economic") lead to di¤erent ranking of speci.c models; v) modelling time variation in term premia is important and useful for forecasting.Bayesian methods, Forecasting, Term Structure.

Forecasting Exchange Rates with a Large Bayesian VAR

Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange rates tend to co-move, the use of a large set of them can contain useful information for forecasting. In addition, we adopt a driftless random walk prior, so that cross-dynamics matter for forecasting only if there is strong evidence of them in the data. We produce forecasts for all the 33 exchange rates in the panel, and show that our model produces systematically better forecasts than a random walk for most of the countries, and at any forecast horizon, including at 1-step ahead.Exchange Rates, Forecasting, Bayesian VAR

A Shrinkage Instrumental Variable Estimator for Large Datasets

This paper proposes and discusses an instrumental variable estimator that can be of particular relevance when many instruments are available. Intuition and recent work (see, e.g., Hahn (2002)) suggest that parsimonious devices used in the construction of the final instruments, may provide effective estimation strategies. Shrinkage is a well known approach that promotes parsimony. We consider a new shrinkage 2SLS estimator. We derive a consistency result for this estimator under general conditions, and via Monte Carlo simulation show that this estimator has good potential for inference in small samples.Instrumental variable estimation, 2SLS, Shrinkage, Bayesian regression

Forecasting with Dynamic Models using Shrinkage-based Estimation

The paper provides a proof of consistency of the ridge estimator for regressions where the number of regressors tends to infinity. Such result is obtained without assuming a factor structure. A Monte Carlo study suggests that shrinkage autoregressive models can lead to very substantial advantages compared to standard autoregressive models. An empirical application focusing on forecasting inflation and GDP growth in a panel of countries confirms this finding.Shrinkage, Forecasting