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Publications (169)
The properties of a number of data-rich methods that are widely used in macroeconomic forecasting are analyzed. In particular, this analysis focuses on principal components (PC) and Bayesian regressions, as well as a lesser known alternative, partial least squares (PLS) regression. In the latter method, linear, orthogonal combinations of a large nu...
We address the issue of parameter dimensionality reduction in Vector Autoregressive models (VARs) for many variables by imposing specific reduced rank restrictions on the coefficient matrices that simplify the VARs into Multivariate Autoregressive Index (MAI) models. We derive the Wold representation implied by the MAIs and show that it is closely...
The problem of model selection of a univariate long memory time series is investigated once a semi parametric estimator for the long memory parameter has been used. Standard information criteria are not consistent in this case. A Modified Information Criterion (MIC) that overcomes these difficulties is introduced and proofs that show its asymptotic...
This paper proposes a nonlinear panel data model which can endogenously generate both ‘weak’ and ‘strong’ cross-sectional dependence. The model’s distinguishing characteristic is that a given agent’s behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the...
This paper employes a parametric model of persistent (level) shifts in the conditional mean of stock market returns which are endogenously driven by large positive or negative return shocks. These shocks can be taken to reflect important market announcements, monetary policy regime changes and/or changes in business conditions affecting stock marke...
The paper addresses the issue of choice of bandwidth in the application of semi parametric estimation of the long memory parameter in a univariate time series process. The focus is on the properties of forecasts from the long memory model. A variety of cross-validation methods based on out of sample forecasting properties are proposed. These proced...
Recently, there has been considerable work on stochastic time-varying coefficient models as vehicles for modelling structural change in the macroeconomy with a focus on the estimation of the unobserved paths of random coefficient processes. The dominant estimation methods, in this context, are based on various filters, such as the Kalman filter, th...
This paper considers a multivariate system of fractionally integrated time series and investigates the most appropriate way for estimating Impulse Response (IR) coefficients and their associated confidence intervals. The paper extends the univariate analysis recently provided by Baillie and Kapetanios (2013), and uses a semi parametric, time domain...
This paper is concerned with the estimation and construction of confidence intervals for the impulse response function (IRF) from strongly persistent time series. A non-parametric, time domain estimator, based on an autoregressive (AR) approximation is shown to have good theoretical and small sample properties for the estimation of the IRF. An alte...
We examine how to forecast after a recent break, introducing a new approach, monitoring for change and then combining forecasts from a model using the full sample and another using post-break data. We compare this to some robust techniques: rolling regressions, forecast averaging over all possible windows and exponentially weighted forecasts. We ex...
There is a growing literature on the realized volatility (RV) forecasting of asset returns using high-frequency data. We explore the possibility of forecasting RV with factor analysis; once considering the significant jumps. A real high-frequency financial data application suggests that the factor based approach is of significant potential interest...
Evidence of lengthy half-lives for real exchange rates in the presence of a high degree of exchange rate volatility has been considered as one of the most puzzling empirical regularities in international macroeconomics. This paper suggests that the measure of half-life used in the literature might be problematic and proposes alternative measures wi...
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. The optimal shrinkage is chosen by maximizing t...
Factor-augmented regressions are often used as a parsimonious way of modeling a variable using information from a large data-set, through a few factors estimated from this data-set. But how does one determine the appropriate number of factors that are relevant for such a regression? Existing work has focused on criteria that can consistently estima...
This paper presents a new stochastic volatility model which allows for persistent shifts in volatility of stock market returns, referred to as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger than the values...
We consider time series forecasting in the presence of ongoing structural change where both the time series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially weighted moving averages, known to be robust to histo...
(DISCLAIMER: Not all mathematical symbols in the abstract will display properly -
please see the abstract in the pdf). An important issue in the analysis of cross-sectional dependence which has received renewed interest in the past few years is the need for a better understanding of the extent and nature of such cross dependencies. In this paper we...
This paper examines the macroeconomic impact of the first round of quantitative easing (QE) by the Bank of England which started in March 2009. Although Bank Rate, the UK policy rate, was reduced to ½%, effectively its lower bound, the Bank’s Monetary Policy Committee felt that additional measures were necessary to meet the inflation target in the...
This paper considers the effects on multi-step prediction of using semiparametric local Whittle estimators rather than MLE for long memory ARFIMA models. We consider various representations of the minimum MSE predictor with known parameters. We then conduct a detailed simulation study for when the true parameters are replaced with estimates. The pr...
This paper employes a parametric model of structural breaks in the mean of stock returns which allows them to be endogenously driven by large positive or negative stock market return shocks. These shocks can be taken to reflect important market announcements, monetary policy regime shifts and/or changes in business conditions which affect stock mar...
Factor based forecasting has been at the forefront of developments in the macro-econometric forecasting literature in the recent past. Despite the flurry of activity in the area, a number of specification issues such as the choice of the number of factors in the forecasting regression, the benefits of combining factor-based forecasts and the choice...
Interest in the interface of nonstationarity and nonlinearity has been increasing in the econometric literature. This paper provides a formal method of testing for nonstationary long memory against the alternative of a particular form of nonlinear ergodic processes; namely, exponential smooth transition autoregressive processes. In this regard, the...
Abstract We review the main New Keynesian inflation equations that have arisen as a result of aggregation from individual firms' price rigidities. We find that, on the whole, they cannot account for inflation persistence, a key feature of the empirical dynamics of inflation, and with important policy implications. The only exceptions seem to be whe...
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 for US time series with the most promising existing alternatives, namely, factor models, large-scale Bayesian VARs, and multivariate boo...
Using a model of deterministic structural change, we revisit several topics in inflation dynamics explored previously using stochastic, time - varying parameter models. We document significant reductions in inflation persistence and predictability. We estimate that changes in the volatility of shocks were decisive in accounting for the great modera...
We examine how to forecast after a recent break. We consider monitoring for change and then combining forecasts from models that do and do not use data before the change; and robust methods, namely rolling regressions, forecast averaging over different windows and exponentially weighted moving average (EWMA) forecasting. We derive analytical result...
We consider the issue of Block Bootstrap methods in processes that exhibit strong dependence. The main difficulty is to transform the series in such way that implementation of these techniques can provide an accurate approximation to the true distribution of the test statistic under consideration. The bootstrap algorithm we suggest consists of the...
The aim of this paper is to consider high dimensional multivariate realized volatility models for large dimensional datasets and also address the solution for noise problem coming out of volatility estimation in the presence of market microstructure effects. Standard models, where prices are contaminated with stochastically independent noise, are u...
The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently, work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work...
We examine how to forecast after a recent break. We consider monitoring for change and then combining forecasts from models that do and do not use data before the change; and robust methods, namely rolling regressions, forecast averaging over different windows and exponentially weighted moving average (EWMA) forecasting. We derive analytical result...
When forecasts are assessed by a general loss (cost-of-error) function, the optimal point forecast is, in general, not the conditional mean, and depends on the conditional volatility-which, for stock returns, is time-varying. In order to provide forecasts ...
The martingale difference restriction is an outcome of many theoretical analyses in economics and finance. A large body of econometric literature deals with tests of that restriction. We provide new tests based on radial basis function (RBF) neural networks. Our work is based on the test design of Blake and Kapetanios (2000, 2003a, 2003b). However,...
Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered...
The present paper suggests a new way to carry out IV estimation with many instruments. Our suggestion is to cross-sectionally average the instruments and use these averages as instruments. We provide a theoretical and Monte Carlo analysis of this approach.
We use the panel convergence methodology proposed by Phillips and Sul (Econometrica 2007) to test for integration in the European banking sector. To overcome the difficulties associated with the ‘law of one price’, we use the banking integration metric proposed by Gropp and Kashyap (NBER 2009), that is, equality in the return of assets of individua...
The use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity is analysed. In particular, the focus is on situations where many weak instruments exist and/or the factor structure is weak. Theoretical results, simulation experiments and empirical applications highlight the relevance of Factor-GMM es...
This paper introduces a new model of structural breaks in the coefficients of economic relationships which allows them to be driven by large past economic shocks. The breaks generated by these shocks can be taken to reflect stochastic changes in agents' decisions or beliefs triggered by extraordinary economic events. Our model specifies that both t...
Most work in the area of nonlinear econometric modeling is based on a single equation and assumes exogeneity of the explanatory variables. Recently, work by Caner and Hansen (2004) and Psaradakis, Sola, and Spagnolo (2005) has considered the possibility of estimating nonlinear models by methods that take into account endogeneity but provide no test...
This paper proposes a new panel model of cross-sectional dependence. The model has a number of potential structural interpretations that relate to economic phenomena such as herding in financial markets. On an econometric level it provides a flexible approach to the modelling of interactions across panel units and can generate endogenous cross-sect...
Detection of structural change is a critical empirical activity, but continuous 'monitoring' of series, for structural changes in real time, raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes...
I study the dynamics of oil futures prices in the NYMEX using a large panel dataset that includes global macroeconomic indicators, financial market indices, quantities and prices of energy products. I extract common factors from the panel data series and estimate a Factor-Augmented Vector Autoregression for the maturity structure of oil futures pri...
This paper considers the stationarity properties of a variety of financial variables using statistical tests for strict stationarity. We find that there has been a gradual shift in unconditional variances for the variables examined during the 90's and 2000's and that this is the main cause of the widespread rejection of the strict stationarity null...
Structural change is a major source of forecast failure. Immediately after a break, forecasting problems are particularly severe due to a lack of information about the new data generation process. Techniques exist for monitoring for structural change in real time, but the optimal post-break strategy is unex-plored. We consider two approaches. First...
We suggest a way to perform parsimonious instrumental variables estimation in the presence of many, and potentially weak, instruments. In contrast to standard methods, our approach yields consistent estimates when the set of instrumental variables complies with a factor structure. In this sense, our method is equivalent to instrumental variables es...
Testing and estimating the rank of a matrix of estimated parameters is key in a large variety of econometric modelling scenarios. This article describes general methods to test for and estimate the rank of a matrix, and provides details on a variety of modelling scenarios in the econometrics literature where such methods are required. Four differen...
We compare the Bank of England's Inflation Report quarterly forecasts for growth and inflation to real-time benchmark forecasts. The results reveal the well-known difficulty of forecasting in a stable macroeconomic environment, and the Inflation Report forecasts of GDP growth are generally inferior to forecasts from linear and non-linear univariate...
Most macroeconomic data are uncertain - they are estimates rather than perfect measures of underlying economic variables. One symptom of that uncertainty is the propensity of statistical agencies to revise their estimates in the light of new information or methodological advances. This paper sets out an approach for extracting the signal from uncer...
In this paper we use principal components analysis to obtain vulnerability indicators able to predict financial turmoil. Probit modelling through principal components and also stochastic simulation of a Dynamic Factor model are used to produce the corresponding probability forecasts regarding the currency crisis events affecting a number of East As...
The problem of structural change justifiably attracts considerable attention in econometrics. A number of different paradigms have been adopted, ranging from structural breaks which are sudden and rare, to time-varying coefficient models, which exhibit structural change more frequently and continuously. This paper is concerned with parametric econo...
Models based on economic theory have serious problems 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 r...
This paper is motivated by recent evidence that many univariate economic and financial time series have both nonlinear and long memory characteristics. Hence, this paper considers a general nonlinear, smooth transition regime autoregression which is embedded within a strongly dependent, long memory process. A time domain MLE with simultaneous estim...
We extend GLS detrending procedure to testing for unit roots against STAR and SETAR alternatives. Monte Carlo simulations and applications to DM/Yen real exchange rates demonstrate that GLS detrending-based nonlinear unit root tests are more powerful than OLS detrending-based counterparts.
This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge regression and forecast combinations, which are widely used in macroeconomic forecasting, and compares these with a lesser known alternative method: partial least squares regression. Under the latter, linear, orthogonal combinations of a large number of...
Model averaging often improves forecast accuracy over individual forecasts. It may also be seen as a means of forecasting in data-rich environments. Bayesian model av- eraging methods have been widely advocated, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of information- theoretic model av...
The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest the use of the principal component methodology of Stock and Watson [Stock, J.H., Watson, M.W., 2002. Macroeconomic forecasting using diffusion indices. Journal of Business and Economic Statistics, 20, 147-162] for the stochastic...
We analyse the sustainability of government debt for Latin American and Caribbean countries employing unit-root tests with nonlinear alternative hypotheses and examine the robustness of our results against those from unit-root tests with breaks and threshold nonlinearities. We show that, in general support for sustainability substantially improves...
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 empir...
This paper aims to provide a brief and relatively non-technical overview of state-of-the-art forecasting with large data sets. We classify existing methods into four groups depending on whether data sets are used wholly or partly, whether a single model or multiple models are used and whether a small subset or the whole data set is being forecast....
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 a...
We explore this issue by estimating our RBC model on US and UK data.
In recent years, there has been increasing interest in forecasting methods that utilise large data sets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is a popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian mod...
In this paper we present a methodology for evaluating the forecasting ability of composite leading indicator variables of industrial economic activity. The new methodology highlights the risks of variable selection in a VAR framework. The methodology is applied to investigate the performance of the OECD composite leading indicator in forecasting in...
This paper is concerned with parametric econometric models whose coefficients change deterministically over time. We provide a new estimator for unconditional time varying variances in regression models. A small Monte Carlo study indicates that the method works reasonably well for moderately large sample sizes.
The persistence properties of economic time series have been a primary object of investigation in a variety of guises since the early days of econometrics. Recently, work on nonlinear modelling for time series has introduced the idea that persistence of a shock at a point in time may vary depending on the state of the process at that point in time....
The Bank of England has constructed a ‘suite of statistical forecasting models’ (the ‘Suite’) providing judgement-free statistical forecasts of inflation and output growth as inputs into the forecasting process, and to offer measures of relevant news in the data. The Suite focuses on combining in an optimal way a small number of forecasts generated...
The paper describes the challenges that uncertainty over the true value of key macroeconomic variables poses for policymakers and the way in which they may form and update their priors in light of a range of indicators. SpeciÂ…cally, it casts the data uncertainty challenge in state space form and illustrates - in this setting - how the policymakerÂ...
Recently, considerable emphasis has been placed on the problems arising out of cross-sectional dependence in panel unit root tests. This paper adopts the factor-based cross-sectional dependence paradigm of Bai and Ng (2005) but suggests alternative factor extraction methods. Some theoretical results for these methods are provided. Further, a detail...
A persistent question in the development of models for macroeconomic policy analysis has been the relative role of economic theory and evidence in their construction. This paper looks at some popular strategies that involve setting up a theoretical or conceptual model (CM) which is transformed to match the data and then made operational for policy...
Tests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejection of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little work on the extent of the effect of neglected nonlineari...
The question of variable selection in a regression model is a major open research topic in econometrics. Traditionally two broad classes of methods have been used. One is sequential testing and the other is information criteria. The advent of large datasets used by institutions such as central banks has exacerbated this model selection problem. A s...
This paper introduces bootstrap neural network pure significance tests for the no cointegration hypothesis against nonlinear cointegration alternatives. The theoretical properties of the tests are discussed and a Monte Carlo investigation of their small sample properties is undertaken.
Using a new methodology that allows nonlinearities, we find frequent support for external debt sustainability in a number of Latin American countries. Our findings reverse the results for several countries, obtained with traditional unit-root tests and present a richer framework for evaluating the external solvency of an economy. Our results also p...
Interest in the interface of nonstationarity and nonlinearity has been increasing in the econometric literature. The motivation for this development maybe be traced to the perceived possibility that processes following nonlinear models maybe mistakenly taken to be unit root or long-memory nonstationary. This paper considers the possibility that pro...
This article proposes pure significance tests for the absence of nonlinearity in cointegrating relationships. No assumption of the functional form of the nonlinearity is made. It is envisaged that the application of such tests could form the first step towards specifying a nonlinear cointegrating relationship for empirical modelling. The asymptotic...
This paper considers the problem of statistical inference in linear regression models whose stochastic regressors and errors may exhibit long-range dependence. A time-domain sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties o...
This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are derived. An application to nonparametric specificatio...
The martingale difference restriction is an outcome of many theoretical analyses in economics and finance. A large body of econometric literature deals with tests of that restriction. We provide new tests based on radial basis function neural networks. Our work is based on the test design of Blake and Kapetanios (2000, 2003a,b). However, unlike tha...
The investigation of the presence of structural change in economic and financial series is a major preoccupation in econometrics. A number of tests have been developed and used to explore the stationarity properties of various processes. Most of the focus has rested on the first two moments of a process thereby implying that these tests are tests o...
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 review the main New Keynesian inflation equations that have arisen as a result of aggregation from individual firms' price rigidities. We find that, on the whole, they cannot account for inflation persistence, a key feature of the empirical dynamics of inflation, and with important policy implications. The only exception seems to be when price s...
The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recent work by Pesaran (2006) suggests a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the imp...
Panel data sets have been increasingly used in economics to analyse complex economic phenomena. One of the attractions of panel data sets is the ability to use an extended data set to obtain information about parameters of interest which are assumed to have common values across panel units. However, the assumption of poolability has not been studie...
The estimation of structural dynamic factor models (DFMs) for large sets of variables is attracting considerable attention. In this paper we briefly review the underlying theory and then compare the impulse response functions resulting from two alternative estimation methods for the DFM. Finally, as an example, we reconsider the issue of the identi...
The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we...
This paper employs a new model of structural breaks developed by Kapetanios and Tzavalis (2004), which allows for structural coefficient changes to be triggered by large economic shocks, to investigate the pattern of instability of many US macroeconomic series, over time. This instability is associated with changes in business cycle conditions.
This note shows that regime switching nonlinear autoregressive models widely used in the time series literature can exhibit arbitrary degrees of long memory via appropriate definition of the model regimes.
This paper proposes a new testing procedure to detect the presence of a cointegrating relationship that follows a globally stationary smooth transition process. In the context of nonlinear smooth transition error correction models (ECMs) we provide two simple operational versions of the tests. First, we obtain the associated nonlinear ECM-based tes...
It is well known that instrumental variables (IV) estimation is sensitive to the choice of instruments both in small samples and asymptotically. Recently, Donald and Newey (2001) suggested a simple method for choosing the instrument set. The method in- volves minimising the approximate mean square error (MSE) of a given IV estimator where the MSE i...
Recently, there has been increasing interest in forecasting methods that utilise large data sets. We explore the possibility of forecasting with model averaging using the out-of-sample forecasting performance of various models in a frequentist setting, using the predictive likelihood. We apply our method to forecasting UK inflation and find that th...
This paper proposes a simple testing procedure to distinguish a unit root process from a globally stationary three-regime self-exciting threshold autoregressive process. Following the threshold cointegration literature we assume that the process follows the random walk in the corridor regime, and therefore we propose that the null of a unit root be...
This paper studies the properties of the sieve bootstrap for a class of linear processes which exhibit strong dependence. The sieve bootstrap scheme is based on residual resampling from autoregressive approximations the order of which increases slowly with the sample size. The first-order asymptotic validity of the sieve bootstrap is established in...
This paper presents a new model of stochastic volatility which allows for infrequent shifts in the mean of volatility, known as structural breaks. These are endogenously driven from large innovations in stock returns arriving in the market. The model has a number of interesting properties. Among them, it can allow for shifts in volatility which are...
This paper considers estimation and inference in some general non linear time series models which are embedded in a strongly dependent, long memory process. Some new results are provided on the properties of a time domain MLE for these models. The paper also includes a detailed simulation study which compares the time domain MLE with a two step est...
The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we...
A persistent question arising in the development of models for the analysis of macroeconomic policy has been the relative role of economic theory and evidence (data) in their construction. This paper looks at some strategies for transforming a Conceptual Model to become a Data-Adjusted Model, and how to adjust further to an Operational Model for po...
Standard measures of prices are often contaminated by transitory shocks. This has prompted economists to suggest the use of measures of underlying inflation to formulate monetary policy and assist in forecasting observed inflation. Recent work has concentrated on modelling large data sets using factor models. In this paper we estimate factors from...
This paper tests a version of the rational expectations hypothesis using ‘fixed-event’ inflation forecasts for the UK. Fixed-event forecasts consist of a panel of forecasts for a set of outturns of a series at varying horizons prior to each outturn. The forecasts are the prediction of fund managers surveyed by Merrill Lynch. Fixed-event forecasts a...
In this paper, we explore the consequences for forecasting of the following two facts: first, that over time statistics agencies revise and improve published data, so that observations on more recent events are those that are least well measured. Second, that economies are such that observations on the most recent events contain the largest signal...