Seasonal Unit Roots in Aggregate U.S. Data

Beaulieu, J. Joseph; Miron, Jeffrey A.

doi:10.3386/t0126

Cited by 47 publications

(68 citation statements)

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“…To corroborate this conclusion we apply the HEGY test. This test refers to the twelve π i coefficients following in the notation of Beaulieu and Miron (1993) and the critical values were also taken from Rodrigues and Osborne (1997). At the above mentioned significance level, we get similar results than the ones obtained with OCSB: the need for seasonal differencing is strongly rejected (by an F 1,12 test on the null: π i , i=1,..,12, are zero), but regular differencing is required in all the cases (by a t-test on the null that π 1 is zero).…”

Section: Trend and Seasonal Factorsmentioning

confidence: 99%

“…The tests were performed using the log transformation of the data and their results can be found on the website cited in the introduction. We applied the Osborn et al (1988) tests, hereafter OCSB, and Hylleberg et al (1990) test as extended in Beaulieu and Miron (1993), hereafter HEGY. Using the terminology employed in the first paper, I(r,s) -where r and s can take values one or zero-means that the data needs r regular differences and s annual differences in order to be stationary.…”

Section: Trend and Seasonal Factorsmentioning

confidence: 99%

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Forecasting aggregates and disaggregates with common features

Espasa

Mayo-Burgos

2013

International Journal of Forecasting

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This paper focuses on providing consistent forecasts for an aggregate economic indicator, such as a consumer price index, and all its components. The procedure developed is a disaggregated approach based on single components, which take into account the stable features as common trend and common serial correlation that some components share. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of this classification is a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We apply the procedure to forecast inflation in the Euro Area, the UK and the US. Our forecasts of the aggregate and other indirect forecasts. This paper focuses on providing consistent forecasts for an aggregate economic indicator, such as a consumer price index, and all its components. The procedure developed is a disaggregated approach based on single-equation models for the take into account the stable features as common trend and common serial correlation that some components share. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of s a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We apply the procedure to forecast inflation in the Euro Area, the UK and the US. Our forecasts are significantly more accurate than a direct forecast of the aggregate and other indirect forecasts. Forecasting aggregates and disaggregates with common featuresThis paper focuses on providing consistent forecasts for an aggregate economic indicator, such as a consumer price index, and all its components. The procedure equation models for the take into account the stable features as common trend and common serial correlation that some components share. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of s a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We apply the procedure to forecast inflation in the Euro Area, are significantly more accurate than a direct forecast

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Section: Trend and Seasonal Factorsmentioning

confidence: 99%

Section: Trend and Seasonal Factorsmentioning

confidence: 99%

Forecasting aggregates and disaggregates with common features

Espasa

Mayo-Burgos

2013

International Journal of Forecasting

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“…Como un bien para lo cual que para ser tratadas en el análisis deben cumplir tres propiedades: 1) ser continuas (es decir, no tener saltos inexplicables), 2) depender de los precios relativos y no de los absolutos, y 3) cumplir la llamada Ley de Walras, según la cual los mercados están vinculados a través de las restricciones presupuestarias de los agentes. [17] 2. Metodología El indicador más comúnmente utilizado para medir la generación de bienes y servicios tangibles es el Producto Interno Bruto (PIB).…”

Section: Perspectiva De La Valoraciónunclassified

Modelo Econométrico del sector de la construcción en Ecuador / Econometric model of the ecuadorian construction sector

Regalado¹,

Quintero²,

Miranda³

2015

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“…To determine k, the order of lag augmentation in (4), RA uses the general-tospeci¯c data dependent method recommended by Ng and Perron (1995), setting k max = 12 [i.e., 3 years of lags, as in Beaulieu and Miron (1993)] and sequentially testing (down) the signi¯cance of the last lag | based on its t-ratio and using ® = 0:05 and the standard normal table | until a rejection is found (that is, the usual t-sig procedure). Checking also for residual autocorrelation at the selected lag, RA¯nds k = 0 and no symptoms of this problem using the Breusch-Godfrey (Lagrange multiplier) tests for orders 1 and 4, the corresponding p-values, BG1(p) and BG4(p) being 0.497 and 0.377, respectively.…”

Section: A Tale Of Three Econometriciansmentioning

confidence: 99%

The order of integration for quarterly macroeconomic time series: A simple testing strategy

Lopes

2003

Empirical Economics

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Besides introducing a simple and intuitive de¯nition for the order of integration of quarterly time series, this paper also presents a simple testing strategy to determine that order for the case of macroeconomic data.A simulation study shows that much more attention should be devoted to the practical issue of selecting the maximum admissible order of integration. In fact, it is shown that when that order is too high, one may get (spurious) evidence for an excessive number of unit roots, resulting in an overdi®erenced series.

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Seasonal Unit Roots in Aggregate U.S. Data

Cited by 47 publications

References 0 publications

Forecasting aggregates and disaggregates with common features

Forecasting aggregates and disaggregates with common features

Modelo Econométrico del sector de la construcción en Ecuador / Econometric model of the ecuadorian construction sector

The order of integration for quarterly macroeconomic time series: A simple testing strategy

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