Econometric modeling of locational behavior

1989 ◽  
Vol 18 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Daniel McFadden
Keyword(s):  
Econometric Modeling ◽  
Locational Behavior

scholarly journals A DIALOGUE CONCERNING A NEW INSTRUMENT FOR ECONOMETRIC MODELING

2005 ◽  
Vol 21 (01) ◽  
Author(s):  
Clive W.J. Granger ◽  
David F. Hendry
Keyword(s):  
Econometric Modeling ◽  
New Instrument

Engineering‐Econometric Modeling of Energy Demand

1992 ◽  
Vol 118 (2) ◽  
pp. 109-121 ◽  
Author(s):  
Fabrizio Carlevaro ◽  
Jean‐Luc Bertholet ◽  
Jean‐Paul Chaze ◽  
Patrick Taffé
Keyword(s):  
Energy Demand ◽  
Econometric Modeling

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

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.

Current Development Trends in the Digital Currency Market

2021 ◽  
Vol 27 (7) ◽  
pp. 504-511
Author(s):  
E. A. Sintsova ◽  
E. A. Vitsko
Keyword(s):  
Financial Stability ◽  
Econometric Modeling ◽  
Modeling Tools ◽  
Digital Currency ◽  
Currency Market ◽  
Russian Economy ◽  
Current Trends ◽  
General Scientific ◽  
Modern Context ◽  
Current Stage

Aim. The presented study aims to analyze the development of the digital currency market, investigate trends for expanding the use of its tools, identify the peculiarities of the current stage of digital currency use, and consider the mechanism of introducing central bank digital currencies (CBDCs).Tasks. The authors specify the role and content of the digital currency market and its tools in the modern Russian economy; examine the formation and development of the cryptocurrency market from the perspective of introducing the “digital ruble”; identify regulatory prerequisites that hinder the development of the digital currency market; describe current trends and the mechanism of organizing the introduction of CBDCs.Methods. This article reflects a comprehensive approach to assessing the effectiveness of the use of digital currency market tools based on the use of economic-statistical and general scientific dialectical methods as well as the laws and principles of formal logic. The conducted studies and recommendations are based on statistics provided by CoinMarketCap. In particular, the methodological basis includes econometric modeling tools used to assess the cryptocurrency market in order to identify its characteristic traits and features.Results. Under modern conditions, the digital currency market is considered to be one of the main transformational elements of the digital economy. The authors focus on the prerequisites for the development and implementation of the domestic digital currency as an instrument of the national monetary policy and for ensuring the financial stability of the economy as a whole. This hypothesis is confirmed by the analysis and study of the global economic situation in the international digital currency market as well as the peculiarities of the functioning of its key components.Conclusions. In the modern context, it is important to have a theoretical and practical understanding of the conditions for the functioning of the digital currency market in the national economy and to find a comprehensive solution to issues associated with expanding the use of its tools for the development of the payment system and the formation of a favorable competitive environment.

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