Real-time nowcasting the US output gap: Singular spectrum analysis at work

Carvalho, Miguel de; Rua, António

doi:10.1016/j.ijforecast.2015.09.004

Cited by 40 publications

(32 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the SSA and robust SSA algorithms, there are two choices to be made by the researcher: (i) the window length L ; and (ii) the number of eigentriples used for reconstruction r . Three values of L were chosen for each time series, as defined in Table 2 —

, and

—being the

obtained from the periodogram, based on the largest cycle for each time series [ 37 ] (i.e., about one trimester for ADAM Strategy, one semester for Alaska Black, one year for APEX Long Biased, one quadrimeter for Brasil Capital, one quadrimeter for Gavea Macro, and one quadrimester for SPX Nimitz), and N being the time series length. The choice of the number of eigentriples used for reconstruction r , for each of the considered window lengths and each of the time series, was done by taking into consideration the the w-correlations among components [ 5 ].…”

Section: Resultsmentioning

confidence: 99%

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Rodrigues

Pimentel

Messala

et al. 2020

Entropy

View full text Add to dashboard Cite

Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noise-free time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and non-parametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models.

show abstract

, and

—being the

Section: Resultsmentioning

confidence: 99%

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Rodrigues

Pimentel

Messala

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…See also who consider a set of UK economic variables including GDP and industrial production whereas Papailias and Thomakos (2017) consider a set of US variables including GDP. Additionally, de Carvalho et al (2012) and de Carvalho and Rua (2017) resort to SSA to nowcast the US output gap, a first application of the ideas for mixed frequency SSA. SSA has also been used for forecasting tourism, in particular UK tourism income by Beneki et al (2012), US tourist arrivals in Hassani et al (2015) and European tourist arrivals by Hassani et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

Hassani

Rua

Silva

et al. 2019

International Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

The literature on mixed-frequency models is relatively recent and has found applications across economics and finance. The standard application in economics considers the use of (usually) monthly variables (e.g. industrial production) in predicting/fitting quarterly variables (e.g. real GDP). In this paper we propose a Multivariate Singular Spectrum Analysis (MSSA) based method for mixed frequency interpolation and forecasting, which can be used for any mixed frequency combination. The novelty of the proposed approach rests on the grounds of simplicity within the MSSA framework. We present our method using a combination of monthly and quarterly series and apply MSSA decomposition and reconstruction to obtain monthly estimates and forecasts for the quarterly series. Our empirical application shows that the suggested approach works well, as it offers forecasting improvements on a dataset of eleven developed countries over the last 50 years. The implications for mixed frequency modelling and forecasting, and useful extensions of this method, are also discussed. JEL: C18, C53, E17

show abstract

“…As a result, there are continuous attempts at developing the underlying theory of SSA and improving its forecasting methods. Whilst the review of all applications of SSA are beyond the scope of this paper, those interested are referred to [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Nowadays, there is a bulk of research on SSA to develop its theory and applications and few such examples can be found in [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Weighted Linear Recurrent Forecasting in Singular Spectrum Analysis

2019

View full text Add to dashboard Cite

Singular Spectrum Analysis (SSA) is an increasingly popular time series filtering and forecasting technique. Owing to its widespread applications in a variety of fields, there is a growing interest towards improving its forecasting capabilities. As such, this paper takes into consideration the Recurrent forecasting approach in SSA (SSA-R) and presents a new mechanism for improving the accuracy of forecasts attainable via this method. The proposed Recurrent SSA-R approach is referred to as Weighted SSA-R (W:SSA-R), and we propose using a weighting algorithm for weigthing the coefficients of the Linear Recurrent Relation (LRR). The performance of forecasts from the W:SSA-R approach are compared with forecasts from the established SSA-R approach. We exploit real data and various simulated time series for the comparison, so as to provide the reader with more conclusive findings. Our results confirm that the W:SSA-R approach can provide comparatively more accurate forecasts and is indeed a viable solution for improving forecasts by SSA.

show abstract

Real-time nowcasting the US output gap: Singular spectrum analysis at work

Cited by 40 publications

References 56 publications

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

Weighted Linear Recurrent Forecasting in Singular Spectrum Analysis

Contact Info

Product

Resources

About