Determinants in the forecast of the gross national income of China and India from 1952 to 2015

In 2015, China and India's population represented approximately 35.74% of the total number of people living in the world. Due to the historical context and behavior of the most relevant indicators, this study proposes to utilize a wide variety of demographic, economic, and production indicators from 1952 to 2015 to assess their impact on the GNI in China and India. A comprehensive and new fangled modeling process with stepwise, regularization and distributed lag regression approaches is presented. Accordingly, theoretical results were corroborated through extensive diagnostic tests and an empirical check of the models' predictive capacity. The findings show that GNI in China is most influenced by variables such as reserves in foreign currency and the dependency ratio; whereas, variables of energy production and birth rate were generated for India. Therefore, it's the timing for China to relax the universal two-child policy. Due to the current value below the substitution rate, a gloomy outlook for China's future population and economy is predicted. Conversely, a positive outlook is forecasted for India, given the low price in the future of oil- India's primary raw material.     

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.     

Optimal allocation of direct and embodied arable land associated to urban economy: Understanding the options deriving from economic globalization

Land resources do not flow directly but can be allocated as “embodied land” in goods and services during economic globalization. The term “embodied arable land” can help link local land allocation strategies to the global and national supply chains and trade activities, and suggest new lens in optimizing arable land allocation. China is facing a serious arable land shortage, especially in municipal administrative areas. Based on the nested input-output analysis (Nested IOA), this work takes Shanghai as an example, exploring the allocation of direct and embodied arable land of an urban economy within the process of economic globalization. The amount of embodied arable land associated with Shanghai economy is 6.09 Mha, broken down into local arable land use (0.20 Mha), domestic inflows (3.20 Mha) and foreign inflows (2.69 Mha). This area supports local final demand (4.32 Mha), domestic outflows (1.24 Mha) and foreign outflows (0.53 Mha). Land-related imbalances emerge in the study, namely the economy’s demand versus the city’s size, the arable land demand versus supply, and embodied arable land inflows versus outflows. Regarding the role of the urban economy in allocation of global arable land under economic globalization, on the one hand, Shanghai has intensively involved with a large amount of embodied foreign arable land resources, and has been heavy dependent on foreign embodied arable land; On the other hand, there is still large potential for Shanghai to take the opportunity of economic globalization for an optimal allocation of direct and embodied arable land. Policy suggestions on taking the opportunity of economic globalization for an optimal allocation of direct and embodied arable land are put forward.     

Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

High-frequency credit spread information and macroeconomic forecast revision

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using a mixed data sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both the aggregate and individual forecast levels. The relationships are shown to be notably strong during ‘bad’ economic conditions, which suggests that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating an amplification effect of financial developments on macroeconomic aggregates. The forecasts do not incorporate all financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.     

A UNIFIED PRESENTATION OF COMPETITION ANALYSIS IN TWO-SIDED MARKETS

The goal of this paper is to present quantitative tools to manage competition policy analysis in two-sided platforms, based on a common framework for transaction and non-transaction platforms. We explore tools for relevant market definition [Critical Loss Analysis and a small but significant non-transitory increase in price (SSNIP) test], unilateral effects indicators [Upward Pricing Pressure (UPP) and Gross Upward Pricing Pressure Index (GUPPI)] and tests for exclusionary practices. We review dispersed results in the literature and fill the gaps where appropriate. We highlight the required changes from the usual one-sided market framework and tools. While discussions of antitrust tools can be found in specialized forums devoted to advances in competition policy analysis, we organize the material in an integrated framework.     

Do institutional investors drive corporate transparency regarding business contribution to the sustainable development goals?

Institutional investors show increasing interest in how companies align their corporate social responsibility strategies with the sustainable development goals (SDGs) proposed by the United Nations (UN). The information disclosed in this regard is essential to know and monitor business contribution to the 2030 Agenda. In this paper, we analyze the influence that institutional investors have on the adoption of the disclosure strategy established by UN and the Global Reporting Initiative (GRI)—GRI‐SDG Compass. The results obtained for a sample of 989 international companies, which prepare their sustainability reports following the GRI guidelines, show that ownership by foreign investors, pension funds, and “other” investors boosts the relevance of the information disclosed in relation to the 2030 Agenda. On the contrary, government, financial institutions, and cross holdings have no impact on the information systems developed.     

An N-dimensional generalization of the Amiti–Weinstein estimator

Amiti and Weinstein proposed an estimation framework to disentangle credit demand and supply shocks using matched bank-firm loan data. Here, we show that their estimator can be generalized to capture shocks arising in an arbitrary number of dimensions. Our algorithm permits empirical researchers to analyse multi-dimensional data sets using the Amiti–Weinstein framework. This may be beneficial both for studies on micro-level outcomes as well as for the literature on assessing the macroeconomic impact of idiosyncratic shocks. In an empirical application to a firm-product-country export data set, we highlight the usefulness of the generalized Amiti–Weinstein estimator, and we demonstrate the importance of considering additional dimensions when gauging the effect of granular shocks on aggregate fluctuations.     

A functional time series analysis of forward curves derived from commodity futures

We study forward curves formed from commodity futures prices listed on the Standard and Poor’s-Goldman Sachs Commodities Index (S&P GSCI) using recently developed tools in functional time series analysis. Functional tests for stationarity and serial correlation suggest that log-differenced forward curves may be generally considered as stationary and conditionally heteroscedastic sequences of functions. Several functional methods for forecasting forward curves that more accurately reflect the time to expiry of contracts are developed, and we found that these typically outperformed their multivariate counterparts, with the best among them using the method of predictive factors introduced by Kargin and Onatski (2008).     

Tests of Normality of Functional Data

The paper is concerned with testing normality in samples of curves and error curves estimated from functional regression models. We propose a general paradigm based on the application of multivariate normality tests to vectors of functional principal components scores. We examine finite sample performance of a number of such tests and select the best performing tests. We apply them to several extensively used functional data sets and determine which can be treated as normal, possibly after a suitable transformation. We also offer practical guidance on software implementations of all tests we study and develop large sample justification for tests based on sample skewness and kurtosis of functional principal component scores.