Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them

This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007–08, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth, and inflation. In the context of unstable environments, I discuss how to assess models’ forecasting ability; how to robustify models’ estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models’ parameters are neither necessary nor sufficient to generate time variation in models’ forecasting performance: thus, one should not test for breaks in models’ parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models’ forecasting performance are more appropriate than traditional, average measures. (JEL C51, C53, E31, E32, E37, F37)

Time series forecasting using amplitude-frequency analysis of STL components

The problem of economic time series analysis and forecasting using amplitude-frequency analysis of STL decomposition is considered. An amplitude-phase operator was chosen as an apparatus for extraction the series harmonic components, the advantages of which (compared to the Fourier transform) are: calculations speed, result accuracy, simplicity and interpretability of software implementation. The forecast quality was carried out using the MAPE metric. Significantly higher prediction quality was achieved compared to Facebook Prophet forecasting package.

Reimagine LNG – An Overview of the LNG Market Potentially Leading to a Future AFLOAT

Objective / Scope LNG has proven its worth, to meet energy demands throughout the globe at scale, whilst providing the cleanest fossil fuel. To complement the emerging trend of energy transition all over the globe, LNG provides a robust solution for a potential future. This paper will describe the current state and outlook of the LNG market, rethinking of LNG contracts and the major drivers that could favor a Floating LNG facility as a market driver compared to land-based facilities. Methods, Procedures, Process With recent events which include the oil price slump, LNG supply glut and the ongoing COVID 19 pandemic, the imbalance in the LNG market is predicted to run with low market price that could last up to four more years. On one hand, low market price is putting a lot of pressure on suppliers but on the flip side, this can be a game changer for the consumers. Consumers can potentially exploit buyers' market by making the investments much stronger to strive towards a clean future. Conventionally LNG producers have been land-based until facilities like Golar LNG made historical success. The focus of the Floating LNG industry is now directed towards small and mid-scale production. With a constant demand from stakeholders to get facilities up and running in a short development schedule, Floating LNG can provide some compelling benefits when combined with the concept of an economic time chartering investment rather than a CAPEX investment. This leads to a shortened execution time from discovery to market and avoids the extensive and time-consuming permitting and land use issues that are typical of onshore projects. The main drivers / challenges for a Floating LNG Facility investment are · Location, associated country regulatory restrictions · Source of gas · Market demand · Technology based on capacity · Project financing Floating LNG can not only provide economic benefits for first use but could also provide a commercial route to easy re-deployment to new gas sources, wherever necessary and possible. The paper will include: · Reflection on the LNG market of the recent past · Impact of COVID 19 on LNG market globally and the projected trends by various analysts · Overview of LNG contract types · Technical and commercial Drivers of Floating LNG which will potentially influence the market Results, Observations, Conclusions The take-away from this paper is a deeper understanding of the following: · Current LNG market and outlook · Reimagine LNG Contracts · Re-explore Floating LNG drivers Novel / Additive Information While the COVID 19 has created one of the reasons for the major impact on the market, this paper will present more interesting facts on many other contributing reasons for the present market downturn. This will in turn give an in-depth understanding for reimagining the major three drivers of Floating LNG, potentially leading to a WIN-WIN solution. This will help to sustain a constant cash flow amongst both sellers and buyers.

Country macroeconomic indicators modeling features

Economic time series have repeatable or non-repeatable fluctuation. A pattern of a time series, which repeats at regular intervals every year, same direction, and similar magnitude is defined as seasonality. The seasonal component represents intra-year fluctuations that are more or less stable year after in a time series. Possible causes of these variations are a systematic and calendar related effects and include natural factors (for instance seasonalweather patterns), administrativemeasures (for example the starting and ending dates of the school year), social/cultural/religious traditions (fixed holidays such as Christmas), the length of the months (28, 29, 30 or 31 days) or quarters (90, 91 or 92 days).Analysts, economists, police makers use time series to make conclusions and decisions in respective area. They tray to identify important features of economic series such as short term changes, directions, turning points and consistency between other economic indicators. These points are usually in interest. Sometimes seasonal movements can make these features difficult to see and this type of analysis is not easy using raw time series data.Deterministic, TRAMO-SEATS and ARIMA-X-12 seasonal adjustment methods are analysed in this article. 1600 time serieswere simulated for solvingwhich seasonal adjustmentmethod is precise. TRAMOSEATS and ARIMA-X-12 both perform similarly for the simulated series. Econometric models of macroeconomic indicators of Lithuania reveal that modeling with seasonal adjusted data is more accurate.

TREND EXTRACTION FROM ECONOMIC TIME SERIES WITH MISSING OBSERVATIONS BY GENERALIZED HODRICK–PRESCOTT FILTERS

The Hodrick–Prescott (HP) filter has been a popular method of trend extraction from economic time series. However, it is impractical without modification if some observations are not available. This paper improves the HP filter so that it can be applied in such situations. More precisely, this paper introduces two alternative generalized HP filters that are applicable for this purpose. We provide their properties and a way of specifying those smoothing parameters that are required for their application. In addition, we numerically examine their performance. Finally, based on our analysis, we recommend one of them for applied studies.

Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

Measuring the Business Cycle Chronology with a Novel Business Cycle Indicator for Germany

This paper introduces a Business Cycle Indicator to compile a transparent and reliable chronology of past business cycle turning points for Germany. The Indicator is derived applying the statistical method of Principal Component Analysis, based on information from 20 economic time series. In this way, the Business Cycle Indicator grasps the development of the broader economic activity and has several advantages over a business cycle assessment based on quarterly series of Gross Domestic Product.

Distribution-Based Entropy Weighting Clustering of Skewed Time Series

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series have motivated the development of classes of distributions that can accommodate properties such as heavy tails and skewness. Thanks to its flexibility, the Skew Exponential Power Distribution (also called Skew Generalized Error Distribution) ensures a unified and general framework for clustering possibly skewed time series. This paper develop a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine all the parameter estimates to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is showed by means of an application to financial time series, showing also how the obtained clusters can be used to form portfolio of stocks.