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on Transition Economics |
| By: | Inaki Veruete Villegas (Charles University, Institute of Economic Studies at Faculty of Social Sciences & The Environment Center, Czech Republic & BETA, CNRS, University of Strasbourg); Milan Scasny (Charles University, Institute of Economic Studies at Faculty of Social Sciences and The Environment Center, Czech Republic.) |
| Abstract: | The current geopolitical landscape, exemplified by the Russian invasion of Ukraine, has heightened concerns about energy security. This study delves into the nexus of energy security and natural gas utilization in the Czech Republic, offering a thorough analysis amid these turbulent times. Despite the fact that the environment/energy-extended input-output models have been significantly improved, they still fail to fully capture a sector’s role in an economic system characterized as a network of sectors as they primarily analyze sectors as both ends of the supply chain, ignoring a significant role of transmission sectors. We overcome this gap by applying a multidimensional approach to scrutinize the energy supply chain in order to assess the repercussions of heightened natural gas prices post-Russian invasion. Specifically, we combine domestic energy input-output demand and price models to assess the economic impacts under constrained alternative energy scenarios, particularly relevant given the challenges of replacing Russian gas. Additionally, leveraging network analysis techniques —node and edge betweenness centrality—and the hypothetical extraction method are used to identify critically important structural elements within the country’s natural gas consumption chain. While the former pinpoints vital transmission sectors based on gas flow, the latter gauges sectoral significance by simulating complete disconnections, without being influenced by the number of times the sector appears in the supply chain path. Last, we develop a complete map of the embodied energy flows. Structural Path Analysis traces intermediate product flows, enabling the quantification of embodied energy across the supply chain and its representation as a tree-like structure. Our findings reveal significant implications of natural gas price fluctuations on key manufacturing industries, notably those engaged in international trade which are vulnerable to energy supply and price disruptions. We emphasize the critical role of sectors providing essential household goods and services, like energy, food, and transportation. Strategic interventions may be necessary to safeguard domestic demand and the competitive edge of vital sectors like automotive. As energy security remains a dynamic and evolving challenge, our research contributes significantly to the ongoing discourse on energy resilience, particularly for countries dependent on energy imports. Despite the fact our study is applied to the energy field, this framework is useful to analyze the footprint of any inputs, including usage of critical materials, environmental inputs, or emissions, which face similar complexities. |
| Keywords: | Energy-Extended Input-Output Aanalysis; Energy Supply Chain; Natural Gas Footprint; Embodied Energy; Betwenness Centrality; Hypothetical Extraction; Structural Path Analysis; Input-Output Price Model |
| JEL: | C67 Q43 H56 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2024_19&r=tra |
| By: | Marandici, Ion |
| Abstract: | This article examines the relationship between nomenklatura membership, wealth accumulation and political ties across the post-Soviet region from the 1990s up to the mid-2010s. It introduces the Post-Soviet Oligarchs (PSO) dataset, containing the sociodemographic characteristics of the super-rich across the former Soviet republics. While the article finds partial support in favour of the nomenklatura capitalism hypothesis, statistical analysis also points to distinct regional patterns of wealth and political inequality. Thus, the most extensive overlap of wealth and power is observed in the authoritarian regimes of Central Asia and the South Caucasus, where ties to the Soviet regime facilitated the exertion of political influence after 1991, enabling in turn wealth accumulation. By contrast, in democratising contexts, the connections between politicians and super-rich point to a mutually dependent relationship between the economic and political realms, with wealth featuring as a major power resource. |
| Keywords: | oligarchs; nomenklatura capitalism; wealthy elites; post-Soviet region; democracy; wealth inequality; authoritarianism; economic transition; billionaires |
| JEL: | N00 O1 P2 P20 P26 P31 P36 P48 P52 Y10 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120709&r=tra |
| By: | Krzysztof Drachal (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | This study examines the application of Bayesian Symbolic Regression (BSR) for in-sample modelling of various commodities spot prices. The studied method is a novel one, and it shows promising potential as a forecasting tool. Additionally, BSR offers capabilities for handling variable selection (feature selection) challenges in econometric modeling. The focus of the presented research is to analyze the suitable selection of the initial parameters for BSR in the context of modelling commodities spot prices. Generally, it is a challenge for (conventional) symbolic regression to properly specify the set of operators (functions). Here, the analysis is primarily focused on specific time-series, making the presented considerations especially tailored to time-series representing commodities markets. The analysis is done with an aim to assess the ability of BSR to fit the observed data effectively. The out-of-sample forecasting performance analysis is deferred for investigations elsewhere. Herein, the main objective is to analyze how the selection of initial parameters impacts the accuracy of the BSR model. Indeed, the already known simulations were based on synthetic data. Therefore, herein real-word data from commodities markets are used. The outcomes can be useful for researchers and practitioners further interested in econometric and financial applications of BSR. (Research funded by the grant of the National Science Centre, Poland, under the contract number DEC-2018/31/B/HS4/02021.) |
| Keywords: | Bayesian symbolic regression, Commodities, Genetic algorithms, Modelling, Symbolic regression, Time-series |
| JEL: | C32 C53 Q02 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iefpro:14116014&r=tra |
| By: | Kumar, Utkarsh; Ahmad, Wasim; Uddin, Gazi Salah |
| Abstract: | Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time-varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal-gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime-specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime-specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out-of-sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model. |
| Keywords: | time-varying parameters; BRICS; cointegration; exchange rate forecasting; Markov switching |
| JEL: | F3 G3 |
| Date: | 2024–04–09 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122816&r=tra |
| By: | Ádám Csápai (University of Economics in Bratislava) |
| Abstract: | We assess the forecasting performance of the selected machine learning methods. According to previous research, they can enhance short-term forecasting performance. We forecast industrial production, inflation and unemployment in Slovakia. We compare the forecasting performance of the models using the mean absolute error and root-mean-squared error. We forecast the variables using ensemble machine learning techniques, such as random forest, bagging and boosting. Additionally, we explore regularized least squares models, such as ridge regression, lasso regression, and elastic net models. Finally, we examine the forecasting performance of neural networks and compare the mean and trimmed mean of model forecasts with individual model performance. Our findings affirm that these methods can enhance forecast accuracy of short-term forecasts, particularly when a relatively large dataset is available. Individual machine learning models prove themselves to be even more accurate than the averages of model forecasts. |
| Keywords: | Economic forecasting, Slovakia, Ensemble machine learning, Regularized least squares, Neural networks |
| JEL: | C53 E37 E27 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iefpro:14115967&r=tra |
| By: | Tianlei Huang (Peterson Institute for International Economics) |
| Abstract: | Expansionary fiscal policy helped China's economy grow in 2020, a year in which most economies contracted because of the COVID-19 pandemic. Amid a broader pivot to policy and regulatory tightening, fiscal support was withdrawn in 2021. In 2022, government budget turned expansionary to ensure economic stability ahead of the Communist Party Congress, but the execution fell short and fiscal policy ended up being weaker than planned. A recurrent problem during the pandemic, however, was that local governments did not fully spend their budgets. Aside from the sharp drop in local governments' land sale revenue in 2022, which dragged down their spending, it was also caused by local governments' failure to fully utilize their special bond quotas approved by the central government for capital investment. China's fiscal policy during the COVID-19 pandemic highlights four issues with implications for fiscal policy making. First, the government needs to avoid projecting unrealistically high land sale revenue in its budgets. Second, it needs to reconsider its problematic use of local-government special bond as a major fiscal stimulus instrument. Third, it needs to make sure its deficit, growth, and inflation targets are consistent. Last, Beijing needs to be more tolerant of higher fiscal deficits, at a minimum ensuring that overall fiscal spending grows at least as rapidly as nominal output. |
| Keywords: | China, public finance, government budget, fiscal deficit |
| JEL: | H61 H62 H77 P35 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:iie:wpaper:wp24-7&r=tra |
| By: | Salzer, Tim |
| Abstract: | This chapter offers a concise overview of China's endeavors towards establishing a state-backed digital currency from the early 2000s to the present, culminating in the digital yuan. Drawing on the social scientific literature concerned with large technical systems, we assert two main arguments. First of all, while many commentators have considered that the new payment infrastructure could overhaul the existing institutional arrangements in the realm of payments and in particular weaken private financial entities, its evolution actually follows a much more incremental logic and relies on both private and public institutions. Secondly, many foreign observers have assumed that the digital yuan represents a long-planned attempt at challenging the international currency hierarchy and American international hegemony. Contrary to this line of thinking, we argue that initially, currency digitalization in the PRC was first and foremost motivated by domestic factors. The project assumed an openly international dimension only after other foreign countries began to initiate their own attempts at currency digitalization under the new slogan of developing "Central Bank Digital Currencies". |
| Date: | 2024–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:5yq4r&r=tra |
| By: | Nam, Pham Khanh (School of Economics, University of Economics HCMC); Man, Pham Nhu (The Joint Doctorate Programme, University of Economics HCMC and Erasmus University Rotterdam); Thuy, Truong Dang (School of Economics, University of Economics HCMC) |
| Abstract: | This study examines the marginal abatement costs (MACs) of three water pollutants (BOD, COD, and TSS) in the seafood processing industry in the Mekong River Delta of Vietnam. Using data on production activities and pollutant concentration, we estimate the MACs and analyze their relationship with firm characteristics. The results reveal significant heterogeneity in MACs, with younger firms, less labor-intensive firms, LLCs and joint-stock companies, firms located in seashore or riverside areas, and those with ISO or other certifications exhibiting lower MACs. These findings suggest that a uniform standard or environmental fee may not efficiently address pollutant reduction. Instead, a tradable permit system could be a more effective approach. |
| Keywords: | seafood; water pollutants; marginal abatement cost; directional distance function |
| JEL: | Q22 |
| Date: | 2023–09–23 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:gunefd:2023_015&r=tra |