Exchange Rate Predictability in a Changing World

An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time Varying Parameters (TVP) estimated by Bayesian methods. In core out-of-sample results, we improve upon a random walk benchmark for at least half, and for as many as eight out of ten, of the currencies considered. This contrasts with a constant parameter Taylor rule model that yields a more limited improvement upon the benchmark. In further results, Purchasing Power Parity and Uncovered Interest Rate Parity TVP models beat a random walk benchmark, implying our methods have some generality in exchange rate prediction.

Data-based priors for vector autoregressions with drifting coefficients

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

Measuring the Euro-Dollar Permanent Equilibrium Exchange Rate using the Unobserved Components Model

This paper employs an unobserved component model that incorporates a set of economic fundamentals to obtain the Euro-Dollar permanent equilibrium exchange rates (PEER) for the period 1975Q1 to 2008Q4. The results show that for most of the sample period, the Euro-Dollar exchange rate closely followed the values implied by the PEER. The only significant deviations from the PEER occurred in the years immediately before and after the introduction of the single European currency. The forecasting exercise shows that incorporating economic fundamentals provides a better long-run exchange rate forecasting performance than a random walk process.

On the Sources of Uncertainty in Exchange Rate Predictability

We analyse the role of time-variation in coefficients and other sources of uncertainty in exchange rate forecasting regressions. Our techniques incorporate the notion that the relevant set of predictors and their corresponding weights, change over time. We find that predictive models which allow for sudden rather than smooth, changes in coefficients significantly beat the random walk benchmark in out-of-sample forecasting exercise. Using innovative variance decomposition scheme, we identify uncertainty in coefficients' estimation and uncertainty about the precise degree of coefficients' variability, as the main factors hindering models' forecasting performance. The uncertainty regarding the choice of the predictor is small.

The Committee on Climate Change: A policy analysis

Domestic action on climate change is increasingly important in the light of the difficulties with international agreements and requires a combination of solutions, in terms of institutions and policy instruments. One way of achieving government carbon policy goals may be the creation of an independent body to advise, set or monitor policy. This paper critically assesses the Committee on Climate Change (CCC), which was created in 2008 as an independent body to help move the UK towards a low carbon economy. We look at the motivation for its creation in terms of: information provision, advice, monitoring, or policy delegation. In particular we consider its ability to overcome a time inconsistency problem by comparing and contrasting it with another independent body, the Monetary Policy Committee of the Bank of England. In practice the Committee on Climate Change appears to be the ‘inverse’ of the Monetary Policy Committee, in that it advises on what the policy goal should be rather than being responsible for achieving it. The CCC incorporates both advisory and monitoring functions to inform government and achieve a credible carbon policy over a long time frame. This is a similar framework to that adopted by Stern (2006), but the CCC operates on a continuing basis. We therefore believe the CCC is best viewed as a "Rolling Stern plus" body. There are also concerns as to how binding the budgets actually are and how the budgets interact with other energy policy goals and instruments, such as Renewable Obligation Contracts and the EU Emissions Trading Scheme. The CCC could potentially be reformed to include: an explicit information provision role; consumption-based accounting of emissions and control of a policy instrument such as a balanced-budget carbon tax.

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty

This paper extends the Nelson-Siegel linear factor model by developing a flexible macro-finance framework for modeling and forecasting the term structure of US interest rates. Our approach is robust to parameter uncertainty and structural change, as we consider instabilities in parameters and volatilities, and our model averaging method allows for investors' model uncertainty over time. Our time-varying parameter Nelson-Siegel Dynamic Model Averaging (NS-DMA) predicts yields better than standard benchmarks and successfully captures plausible time-varying term premia in real time. The proposed model has significant in-sample and out-of-sample predictability for excess bond returns, and the predictability is of economic value.

Prior selection for panel vector autoregressions

There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage in large dimensions, but also allow for soft clustering of variables or countries which are homogeneous. I discuss the implications of these new priors for modelling interdependencies and heterogeneities among different countries in a panel VAR setting. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains of the new priors compared to existing popular priors for VARs and PVARs.

Fractional Integration of the Price-Dividend Ratio in a Present-Value Model of Stock Prices

We re-examine the dynamics of returns and dividend growth within the present-value framework of stock prices. We find that the finite sample order of integration of returns is approximately equal to the order of integration of the first-differenced price-dividend ratio. As such, the traditional return forecasting regressions based on the price-dividend ratio are invalid. Moreover, the nonstationary long memory behaviour of the price-dividend ratio induces antipersistence in returns. This suggests that expected returns should be modelled as an AFIRMA process and we show this improves the forecast ability of the present-value model in-sample and out-of-sample.

Separation of free amino acids in human plasma by capillary electrophoresis with laser induced fluorescence: potential for emergency diagnosis of inborn errors of metabolism.

Free amino acids (AAs) in human plasma are derivatized with 3-(4-carboxybenzoyl)quinoline-2-carboxaldehyde (CBQCA) and analyzed by capillary electrophoresis (CE) with laser induced fluorescence (LIF) detection. The labeling procedure is significantly improved over results reported previously. Derivatization can be completed in 40 min, with concentrations as low as 4 x 10(-8) M successfully labeled in favourable cases. Twenty-nine AAs (including 2 internal standards) are identified and can be reproducibly separated in 70 min. Migration time RSD values for 23 of these AAs were calculated and found in the range from 0.5 to 4%. The rapid derivatization procedure and the resolution obtained in the separation are sufficient for a semi-quantitative, emergency diagnosis of several inborn errors of metabolism (IEM). Amino acid profiles for both normal donor plasma samples and plasma samples of patients suffering from phenylketonuria, tyrosinemia, maple syrup urinary disease, hyperornithinemia, and citrullinemia are studied.

Forecast of acute respiratory infections: expected nonepidemic mobidity in Cuba

A forecast of nonepidemic morbidity due to acute respiratory infections were carry out by using time series analysis. The data consisted of the weekly reports of medical patient consultation from ambulatory facilities from the whole country. A version of regression model was fitted to the data. Using this approach, we were able to detect the starting data of the epidemic under routine surveillance conditions for various age groups. It will be necessary to improve the data reporting system in order to introduce these procedures at the local health center level, as well as on the provincial level.

Prediction of the economic cost of individual long-term care in the Spanish population

Pensions together with savings and investments during active life are key elements of retirement planning. Motivation for personal choices about the standard of living, bequest and the replacement ratio of pension with respect to last salary income must be considered. This research contributes to the financial planning by helping to quantify long-term care economic needs. We estimate life expectancy from retirement age onwards. The economic cost of care per unit of service is linked to the expected time of needed care and the intensity of required services. The expected individual cost of long-term care from an onset of dependence is estimated separately for men and women. Assumptions on the mortality of the dependent people compared to the general population are introduced. Parameters defining eligibility for various forms of coverage by the universal public social care of the welfare system are addressed. The impact of the intensity of social services on individual predictions is assessed, and a partial coverage by standard private insurance products is also explored. Data were collected by the Spanish Institute of Statistics in two surveys conducted on the general Spanish population in 1999 and in 2008. Official mortality records and life table trends were used to create realistic scenarios for longevity. We find empirical evidence that the public long-term care system in Spain effectively mitigates the risk of incurring huge lifetime costs. We also find that the most vulnerable categories are citizens with moderate disabilities that do not qualify to obtain public social care support. In the Spanish case, the trends between 1999 and 2008 need to be further explored.

Multivariate contemporaneous-threshold autoregressive models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific innovation covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.