ECONOMETRIC MODELING OF MICRO-ECONOMIC DEMAND FOR INSURANCE

2018 ◽  
Author(s):  
Tatiana Sinyavskaya
Keyword(s):  
Econometric Modeling ◽  
Demand For Insurance

On the Corporate Demand for Insurance

1982 ◽  
Vol 55 (2) ◽  
pp. 281 ◽  
Author(s):  
David Mayers ◽  
Clifford W. Smith, Jr.
Keyword(s):  
Demand For Insurance

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.

Sign in / Sign up

Export Citation Format

Share Document