Macroeconomic Forecasting With Mixed-Frequency Data

Clements, Michael P.; Galvão, Ana Beatriz

doi:10.1198/073500108000000015

Cited by 315 publications

(109 citation statements)

References 16 publications

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“…For instance, whenever the autoregressive terms in model (3) are present (p > 0), it was pointed out by Ghysels et al (2006b) that, in the general case, φ(L) = β(L)/α(B) will have seasonal patterns thus corresponding to some seasonal impact of explanatory variables on the dependent one in a pure distributed lag model (i.e., without autoregressive terms). To avoid such an effect whenever it is not (or is believed to be not) relevant, Clements and Galvão (2008) proposed to us a common factor restriction which can be formulated as a common polynomial restriction with a constraint on the polynomial β(L) to satisfy a factorization . Then, under the null hypothesis of ∃ γ ∈ R q such that f γ = β, it holds…”

Section: Specification Selection and Adequacy Testingmentioning

confidence: 99%

Mixed Frequency Data Sampling Regression Models: TheRPackagemidasr

Ghysels¹,

Kvedaras

Zemlys

2016

J. Stat. Soft.

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When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002). In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.

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Section: Specification Selection and Adequacy Testingmentioning

confidence: 99%

Mixed Frequency Data Sampling Regression Models: TheRPackagemidasr

Ghysels¹,

Kvedaras

Zemlys

2016

J. Stat. Soft.

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“…More recently, the mixed-frequency literature is receiving significant attention (see Mariano & Murasawa, 2004;Clements & Galvao, 2008;Schwaab et al, 2009;Aruoba, Diebold & Scotti, 2009;Hamilton, 2010). Theoretically, the state space framework is general enough to bridge any frequency mismatch.…”

Section: Mixed Frequency Varsmentioning

confidence: 99%

Bayesian Inference for the Mixed-Frequency VAR Model

Viefers

2011

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Unlike earlier studies, that used the pseuo-EM algorithm of Dempster, Laird & Rubin (1977) to estimate the model, this paper describes how to make use of recent advances in Bayesian inference on mixture models. This way, one is able to surmount some well-known issues connected to inference on mixture models, e.g. the label switching problem. The paper features a numerical simulation study to gauge the model performance in terms of convergence to true parameter values and a small empirical example involving US business cycles. Terms of use: Documents inJEL-Classification: C32, C38, E32, E37, E51

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“…model specifications. In particular, we consider the classical MIDAS model, introduced by Ghysels et al (2004), and the extended version by Clements and Galvão (2008), which includes an autoregressive component. These models rely on the exponential lag polynomials to exploit high-frequency information while at the same time being parsimonious.…”

Section: Introductionmentioning

confidence: 99%

“…We consider 6 monthly explanatory variables. Following Clements and Galvão (2008), we use industrial production, employment (nonfarm payrolls) and 3 capacity utilization. In addition, we include the Purchasing Managers Index (PMI), the Chicago Fed National Activity Index (CFNAI) and the Philadelphia Fed Business Outlook Survey for general business activity (PFBOS).…”

Section: Introductionmentioning

confidence: 99%

Density Forecasts with MIDAS Models

Aastveit¹,

Foroni²,

Ravazzolo

2014

SSRN Journal

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In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions with and without an autoregressive component.First, we compare the forecasting performance of the different MIDAS models in Monte Carlo simulation experiments. We find that the results in terms of point and density forecasts are coherent. Moreover, the results do not clearly indicate a superior performance of one of the models under scrutiny when the persistence of the low frequency variable is low.Some differences are instead more evident when the persistence is high, for which the AR-MIDAS and the AR-U-MIDAS produce better forecasts. Second, in an empirical exercise we evaluate density forecasts for quarterly US output growth, exploiting information from typical monthly series. We find that MIDAS models provide accurate and timely density forecasts.JEL codes: C10, C53, E37

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Macroeconomic Forecasting With Mixed-Frequency Data

Cited by 315 publications

References 16 publications

Mixed Frequency Data Sampling Regression Models: TheRPackagemidasr

Mixed Frequency Data Sampling Regression Models: TheRPackagemidasr

Bayesian Inference for the Mixed-Frequency VAR Model

Density Forecasts with MIDAS Models

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