Evaluation and Influencing Factors of Transportation Industry Energy Efficiency of Changjiang Economic Zone

Changjiang Economic Zone (CEZ) faces the urgent task to promote the energy conservation and emission reduction of the transportation industry. This study constructs an evaluation system for transportation industry energy efficiency (TIEE) and evaluates TIEEs of 11 CEZ provinces in 2000–2017, using the super-slack-based measure (Super-SBM) model containing undesired output. On this basis, the panel data model was adopted to explore the impactors of TIEE. The main results are as follows: CEZ provinces varied significantly in TIEE. In the sample period, Jiangsu, Jiangxi, Zhejiang, Sichuan, Shanghai, and Anhui achieved relatively satisfactory TIEEs; Hunan, Hubei, and Guizhou performed generally on TIEE, calling for some improvement; Chongqing and Yunnan did not perform well, leaving a huge room for improvement. Judging by TIEE trends in the lower reaches, middle reaches, and upper reaches, TIEE of the lower reaches exhibited a U-shaped trend (first decrease and then increase) and TIEEs of the middle reaches and upper reaches did not fluctuate significantly, except for a few years. There was a marked difference between the three regions in TIEE: TIEE in the lower reaches was much higher than that in the middle reaches and upper reaches. In addition, the panel data model demonstrates that TIEE is significantly promoted by economic growth and transportation structure, obviously suppressed by industrial structure, opening-up, and transportation infrastructure, and not clearly affected by government influence or environmental regulation.

Fundamental Responsiveness in European Electricity Prices

We estimate fundamental pricing relationships in selected European day-ahead electricity markets. Using a fractionally integrated panel data model with unobserved common effects, we quantify the responsiveness of hourly electricity prices to two fundamental leading indicators of day-ahead markets: the predicted load and renewable generation. The application of fractional cointegration analysis techniques gives further insight into the pricing mechanism of power delivery contracts, enabling us to measure the persistence of fundamental shocks.

Main Determinants Influencing Inward and Outward Foreign Direct Investments in Thailand: Comparisons and Vital Implications

Foreign direct investment (FDI) has played an important role in the evolution of globalization and is the cornerstone of industrial expansion and economic development. From 1988 to 1990 till now, Thailand has been one of the main destinations of FDI, namely inward FDI (IFDI). However, outward foreign direct investment (OFDI) increased rapidly from 2003 to 2011, and it continues to grow. Although initially a net importer, Thailand has transformed into a net exporter of direct investment in 2011. Since 2003, Thailand has entered a stage of re-emergence of OFDI, and this growth trend of OFDI will continue in the future. The purpose of this study is to investigate the main determinants of Thailand's IFDI and OFDI, and apply a panel data model to determine which determinants have a significant impact on Thailand's IFDI and OFDI. We considered the FDI flows between Thailand and its five FDI partners (Japan, Hong Kong, the Netherlands, Singapore and the United States) from 1997 to 2014, where IFDI was 1997-2014, and 2004-2014 was OFDI. Regarding the determinants of Thailand's IFDI and OFDI, the market size (relative per capita GDP), Thailand's openness, relative R&D intensity and bilateral trade agreements have a positive impact on FDI decisions for both IFDI and OFDI. Relative wages and geographic distance have opposite effects on Thailand's IFDI and OFDI. Specifically, our empirical results show that market size is the most important determinant of IFDI inflows into Thailand, while the most important determinant of Thai OFDI is bilateral trade agreements. JEL classification numbers: F14, F23, F43. Keywords: Foreign Direct Investment, Panel Data Model, Fixed Effects, Generalized Least Squares (GLS).

Robust Multicollinearity Diagnostic Measure For Fixed Effect Panel Data Model

It is now evident that high leverage points (HLPs) can induce the multicollinearity pattern of a data in fixed effect panel data model. Those observations that are responsible for this phenomenon are called high leverage collinearity-enhancing observations (HLCEO). The commonly used within group ordinary least squares (WOLS) estimator for estimating the parameters of fixed effect panel data model is easily affected by HLCEOs. In their presence, the WOLS estimates may produce large variances and this would lead to erroneous interpretation. Therefore, it is imperative to detect the multicollinearity which is caused by HLCEOs. The classical Variance Inflation Factor (CVIF) is the commonly used diagnostic method for detecting multicollinearity in panel data. However, it is not correctly diagnosed multicollinearity in the presence of HLCEOs. Hence, in this paper three new robust diagnostic methods of diagnosing multicollinearity in panel data are proposed, namely the RVIF (WGM-FIMGT), RVIF (WGM-DRGP) and RVIF (WMM) and compared their performances with the CVIF. The numerical evidences show that the CVIF incorrectly diagnosed multicollinearity but our proposed methods correctly diagnosed no multicollinearity in the presence of HLCEOs where RVIF (WGM-FIMGT) being the best method as it has the least computational running time.

How the COVID-19 Pandemic Affects Bank Risks and Returns: Evidence from EU Members in Central, Eastern, and Northern Europe

The purpose of this study was to examine banks’ strategic adjustments to the challenges brought about by the COVID-19 pandemic. It examines how deep and pressing the necessary transformations are, based on an analysis of the banking sectors of Central, Eastern, and Northern European countries (CENE): the Czech Republic, Hungary, Poland, Slovakia, Estonia, Latvia, and Lithuania. The main research question posed asks how the pandemic and the subsequent economic crisis have changed banks’ sources of profits and risks, forcing banks to speed up structural transformations. In particular, the study identified and verified the following hypotheses: that the initial impact of the COVID-19 pandemic on banks in the analyzed region was heterogeneous and that the pandemic has intensified the challenges of digitalization and forced banks to speed up the digital transformations of their business models. The methodology employed was the dynamic panel data model—generalized method of moment (GMM-SYS version), using an adjusted dataset from the BankFocus database for unconsolidated bank data for the 2016–2020 period. The econometric analysis was supplemented with a CENE bank survey, researching bank attitudes and the stage of digital transformation. The results of the survey revealed that the majority of the surveyed banks consider themselves digitalization leaders, with a clearly articulated and implemented digitalization strategy. The main finding of the study was that the digital focus may help large banks in CENE to address and offset problems revealed by the panel data model: that traditional sources of incomes, based on intermediation and interest-related incomes, no longer contribute positively to profitability but also to stability.