Factor forecasts for the UK

Artis, Michael J.; Banerjee, Anindya; Marcellino, Massimiliano

doi:10.1002/for.957

Cited by 147 publications

(99 citation statements)

References 19 publications

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“…Recent forecasting studies using large-scale datasets and pseudo out-of-sample forecasting include: Artis et al (2002), Ng (2005, 2006), Forni et al (2005), and Stock and Watson (1999, 2005, 2006, 2012. Stock and Watson (2006) discuss in some detail the literature on the use of di¤usion indices for forecasting.…”

Section: Di¤usion Index Modelsmentioning

confidence: 99%

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Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence

Kim

Swanson

2010

SSRN Journal

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In this paper, we empirically assess the predictive accuracy of a large group of models that are speci…ed using principle components and other shrinkage techniques, including Bayesian model averaging and various bagging, boosting, least angle regression and related methods. Our results suggest that model averaging does not dominate other well designed prediction model speci…cation methods, and that using "hybrid"combination factor/shrinkage methods often yields superior predictions. More speci…cally, when using recursive estimation windows, which dominate other "windowing" approaches, "hybrid" models are mean square forecast error "best" around 1/3 of the time, when used to predict 11 key macroeconomic indicators at various forecast horizons. Baseline linear (factor) models also "win" around 1/3 of the time, as do model averaging methods. Interestingly, these broad …ndings change noticably when considering di¤erent sub-samples. For example, when used to predict only recessionary periods, "hybrid" models "win" in 7 of 11 cases, when condensing …ndings across all "windowing" approaches, estimation methods, and models, while model averaging does not "win" in a single case. However, in expansions, and during the 1990s, model averaging wins almost 1/2 of the time. Overall, combination factor/shrinkage methods "win" approximately 1/2 of the time in 4 of 6 di¤erent sample periods. Ancillary …ndings based on our forecasting experiments underscore the advantages of using recursive estimation strategies, and provide new evidence of the usefulness of yield and yield-spread variables in nonlinear prediction model speci…cation.

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Section: Di¤usion Index Modelsmentioning

confidence: 99%

“…The basic structure of the forecasting models examined is the same as that examined in Artis et al (2002), Bai and Ng (2002, 2006a,b, 2008, 2009), Boivin and Ng (2005) and Stock and Watson (2002, 2005, 2006, 2012. In particular, we consider models of the following generic form:…”

Section: Di¤usion Index Modelsmentioning

confidence: 99%

Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence

Kim

Swanson

2010

SSRN Journal

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“…Surveys with heavier empirical focus can be found in Breitung and Eickmeier (2005) and Reichlin (2003). Suffice it to say that factor models have been used in forecasting of the conditional mean by Stock and Watson (2002b), Cristadoro et al (2001), Artis et al (2005), Marcellino et al (2003), Schumacher (2005), Forni et al (2001), den Reijer (2005), and many others. Boivin and Ng (2005) compared the use of dynamic and static factors in forecasting.…”

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confidence: 99%

Large Dimensional Factor Analysis

Bai

2008

FNT in Econometrics

290

190

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Econometric analysis of large dimensional factor models has been a heavily researched topic in recent years. This review surveys the main theoretical results that relate to static factor models or dynamic factor models that can be cast in a static framework. Among the topics covered are how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy of observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, * We are grateful to an anonymous referee for careful reading and constructive comments. This survey was presented by the second author as an invited lecture at the 2007 annual meeting of the Society of Computational Economics in Montreal. Financial support from the National Science Foundation (SES055127 and SES-0549978) is gratefully acknowledged.and how to estimate panel cointegration models. The fundamental result that justifies these analyses is that the method of asymptotic principal components consistently estimates the true factor space. We use simulations to better understand the conditions that can affect the precision of the factor estimates.

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“…Recent forecasting studies using large-scale datasets and pseudo out-of-sample forecasting include: Armah and Swanson (2010a,b), Artis et al (2005), Ng (2005, 2006), Forni et al (2005), and Stock and Watson (1999, 2002a. Stock and Watson (2006) additionally discuss in some detail the literature on the use of di¤usion indices for forecasting.…”

Section: Di¤usion Index Modelsmentioning

confidence: 99%

“…One dimension reduction technique, involving the construction of di¤usion indices, has received considerable attention in the recent econometrics literature, particularly in the context of forecasting (see e.g. Armah and Swanson (2010a,b), Artis et al (2005), Bai and Ng (2002, 2006b, 2008, Ng (2005, 2006), Ding and Hwang (1999), Stock and Watson (2002a). Other recent important papers which extend the discussion in the above papers to vector and error-correction type models include Banerjee and Marcellino (2008), Dufour and Stevanovic (2010).…”

Section: Introductionmentioning

confidence: 99%

Mining Big Data Using Parsimonious Factor and Shrinkage Methods

Kim

Swanson

2013

SSRN Journal

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A number of recent studies have focused on the usefulness of factor models in the context of prediction using "big data" (see e.g., Bai and Ng (2008), Dufour and Stevanovic (2010), Forni et al. (2000, Kim and Swanson (2014), Stock and Watson (2002b, and the references cited therein). We add to this literature by analyzing the predictive bene…ts associated with the use of independent component analysis (ICA) and sparse principal component analysis (SPCA), coupled with a variety of other factor estimation and data shrinkage methods, including bagging, boosting, and the elastic net, among others. We carry out a forecasting "horse-race", involving the estimation of 28 di¤erent baseline model types, each constructed using a variety of speci…cation approaches, estimation approaches, and benchmark econometric models; and all used in the prediction of 11 key macroeconomic variables relevant for monetary policy assessment. In numerous instances we …nd that a variety of benchmark autoregressive models and model averaging methods are mean square forecast error (MSFE) dominated by more complicated nonlinear methods. For example, simple averaging methods are MSFE "best" in only 9 of 33 key cases considered. However, in order to "beat" model averaging methods, we must combine new factor estimation methods with interesting new forms of shrinkage. For example, SPCA yields MSFE-best prediction models in many cases, particularly when coupled with shrinkage. In summary, we present empirical results that provide strong new evidence of the usefulness of sophisticated factor based forecasting methods, and as a corollary, of the use of "big data" in macroeconometric forecasting.

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Factor forecasts for the UK

Cited by 147 publications

References 19 publications

Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence

Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence

Large Dimensional Factor Analysis

Mining Big Data Using Parsimonious Factor and Shrinkage Methods

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