Econometrics for Modelling Climate Change

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

On the long-run dynamics of income and wealth inequality

We analyse top income and wealth shares data, by conducting a robust estimation of trends, tests for structural breaks, and tests for determining persistence. We include Anglo-Saxon countries, continental Europe and Asian countries, grouped under different percentiles and deciles, spanning a period that is at least close to a century. We find that the top income shares for almost all countries are characterised by broken trends, or level shifts. The preponderance of trend breaks appears in the 1970s and 1980s where after a negative trend changes in magnitude or direction. Finally, shocks to the top income share data are not transitory, which have consequences for policy such as advocating redistributive measures.

ATOMIUM: A high-resolution view on the highly asymmetric wind of the AGB star π1Gruis

The nebular circumstellar environments of cool evolved stars are known to harbour a rich morphological complexity of gaseous structures on different length scales. A large part of these density structures are thought to be brought about by the interaction of the stellar wind with a close companion. The S-type asymptotic giant branch (AGB) star π1Gruis, which has a known companion at ∼440 au and is thought to harbour a second, closer-by (< 10 au) companion, was observed with the Atacama Large Millimeter/submillimeter Array as part of the ATOMIUM Large programme. In this work, the brightest CO, SiO, and HCN molecular line transitions are analysed. The continuum map shows two maxima, separated by 0.04″ (6 au). The CO data unambiguously reveal that π1Gru’s circumstellar environment harbours an inclined, radially outflowing, equatorial density enhancement. It contains a spiral structure at an angle of ∼38 ± 3° with the line-of-sight. The HCN emission in the inner wind reveals a clockwise spiral, with a dynamical crossing time of the spiral arms consistent with a companion at a distance of 0.04″ from the AGB star, which is in agreement with the position of the secondary continuum peak. The inner wind dynamics imply a large acceleration region, consistent with a beta-law power of ∼6. The CO emission suggests that the spiral is approximately Archimedean within 5″, beyond which this trend breaks down as the succession of the spiral arms becomes less periodic. The SiO emission at scales smaller than 0.5″ exhibits signatures of gas in rotation, which is found to fit the expected behaviour of gas in the wind-companion interaction zone. An investigation of SiO maser emission reveals what could be a stream of gas accelerating from the surface of the AGB star to the companion. Using these dynamics, we have tentatively derived an upper limit on the companion mass to be ∼1.1 M⊙.

Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications

Trend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one of the methodologies that can solve such a problem in practical computation times with a high level of accuracy and precision. In applications such as fault detection in optical fibers, the length N of the dataset to be processed by the algorithm, however, may render the total processing time impracticable, since there is a quadratic increase on the latter with respect to N. To overcome this problem, the herewith proposed profile-splitting methodology enables blocks of data to be processed simultaneously, with significant gains in processing time and comparable performance. A thorough analysis of the efficiency of the proposed methodology stipulates optimized parameters for individual hardware units implementing the profile-splitting. These results pave the way for high performance linearized Bregman iteration algorithm hardware implementations capable of efficiently dealing with large datasets.

STRUCTURAL BREAKS IN NATURAL GAS CONSUMPTION AND ECONOMIC GROWTH IN NIGERIA: EVIDENCE FROM NEW TIME SERIES TESTS THAT ALLOW FOR STRUCTURAL BREAKS

This paper analyzed the issue of structural breaks in natural gas consumption and economic growth in Nigeria. The newly residual augmented least squares (RALS-LM) unit root test with breaks also known as “RALS-LM test with trend breaks and non-normal errors” proposed by Meng-Lee-Payne (2017) and the new structural breaks testing proposed by Kejriwal–Perron (2010) are among the tools used for the investi-gation. Our empirical findings provide significant evidence that the series of natural gas consumption and economic growth are stationary with one or two trend breaks. Furthermore, the investigation identified significant incidences of structural breaks in the relationship between natural gas consumption and economic growth in 1990, 2004, 2009 and all the break dates were found to be significant. The evaluation of the sub-sample periods based on the break dates revealed that the first and second breaks are potential while the last is destructive. Moreover, the estimate of the long-run elasticity is significant where a 1% increase in natural gas consumption induces the growth of Nigerian economy by 0.15% and all the dummies that represent the breakpoints are also significant where the 2004 break had a bigger effect among other breaks. The implication of the results is that shocks in the series of natural gas consumption and economic growth in Nigeria have transitory effect, modeling the relationship between natural gas consumption and economic growth in Nigeria without taking structural breaks into consideration could produce biased and unreliable statistical results, and there is economically significant dependence of the Nigerian economy on natural gas consumption.

A parametric stationarity test with smooth breaks

We propose a test to investigate the stationarity null against the unit-root alternative where a Fourier component is employed to approximate nonlinear deterministic trend of unknown form. A parametric adjustment is also adopted to accommodate possible stationary error. The asymptotic distribution of the test under the null is derived, and the asymptotic critical values are tabulated. We also show that it is a consistent test. Even with small sample sizes often encountered in empirical applications, our parametric stationarity test employing Fourier term has good size and power properties when trend breaks are gradual. The validity of the Fisher hypothesis for 15 OECD countries is investigated to illustrate the usefulness of our test.

Vegetation dynamics and responses to climate anomalies in East Africa

Vegetation plays a key role in the global climate system via modification of the water and energy balance. Its coupling to climate is therefore important, particularly in the tropics where severe climate change impacts are expected. Consequently, understanding vegetation dynamics and response to present and projected climatic conditions for various land cover types in East Africa is vital. This study provides an assessment of the vegetation trends in East Africa using Leaf Area Index (LAI) time series for the period 1982 to 2011, regression analysis between LAI and Standardised Precipitation Evapotranspiration Index (SPEI), as well as analysis of the temporal non-stationarity in the LAI trends and vegetation response to climate. Our results show mean LAI over the region increased at a rate of about 4 × 10−3 units per year, while the rate of increase for annual mean temperature was 0.035 °C per year. Annual precipitation did not show significant trends. Trend breaks and variations in the stability of LAI time series anomalies significantly alter the LAI trends across the period of study. Drought and wetness conditions also show significant influence on the vegetation dynamics in the region. Given the potential impacts of climate change on vegetation productivity in this region, this study provides the much-needed reference point for the disentanglement of historical climatic- and human-induced vegetation dynamics. In addition, the results indicate key areas of interest for the assessment of potential impacts of vegetation dynamics on land surface water and energy balance in the region.

On Trend Breaks and Initial Condition in Unit Root Testing

Recent approaches in unit root testing have taken into account the influences of initial conditions and data trend breaks via pre-testing and union of rejection testing strategies. This paper reviews existing methods, extends the methods of (Harvey, D. I., S. J. Leybourne, and A. M. R. Taylor. 2012b. “Unit Root Testing under a Local Break in Trend.”

RALS-LM unit root test with trend breaks and non-normal errors: application to the Prebisch-Singer hypothesis

This study proposes a new unit root test that allows for structural breaks in both the intercept and the slope, and adopts the residual augmented least squares (RALS) procedure to gain improved power when the error term follows a non-normal distribution. The new test using the RALS procedure is more powerful than the usual LM test which does not incorporate information on non-normal errors. Our test is free of nuisance parameters that indicate the locations of structural break. It is also free of the spurious rejection problem. Thus, the rejection of the null hypothesis can be considered as more accurate evidence of stationarity. We apply the new test on the recently extended Grilli and Yang index of 24 commodity series from 1900 to 2007. Our empirical findings provide significant evidence that primary commodity prices are stationary with one or two trend breaks. However, compared with past studies, our findings provide even weaker evidence to support the Prebisch-Singer hypothesis.