Classification of research models in studies on innovation financing
| Study models | Econometric | Empirical | Statistical | Theoretical | Grand total |
|---|---|---|---|---|---|
| Autoregressive distributed lag (ARDL) model introduced by Pesaran et al. (2001) | 1 | 1 | |||
| Multiple discrete continuous extreme value (MDCEV) model | 1 | 1 | |||
| A two-step econometric procedure | 1 | 1 | |||
| Pearson correlation analysis | 2 | 2 | |||
| Difference-in-differences (D-i-D) analysis | 1 | 1 | |||
| Descriptive analysis | 4 | 4 | |||
| Factorial analysis | 1 | 1 | |||
| Multiple regression equations | 1 | 1 | |||
| Propensity score matching (PSM) and difference in differences (DID) | 1 | 1 | |||
| Two-step estimation with dynamic panel data (GMM) | 2 | 2 | |||
| Cross-sectional estimates of censored regression models with pooled data | 1 | 1 | |||
| Propensity score matching estimator (PSM) | 1 | 1 | |||
| Multilevel probabilistic model (MML) | 1 | 1 | |||
| Conceptual model and estimation models of fixed and random effects regression | 1 | 1 | |||
| Crepon, Duguet and Mairesse (CDM) model | 1 | 1 | |||
| Three-stage dynamic DDF-DEA model with financial regulation | 1 | 1 | |||
| Griliches R&D capital model and international collaborations | 2 | 2 | |||
| Difference in differences (DID) model | 1 | 1 | |||
| Structural equation model | 1 | 1 | |||
| Maximum likelihood model | 1 | 1 | |||
| Two-stage selection model and dynamic random effects probit | 1 | 1 | |||
| Signaling model based on maximum expected revenue for SMEs | 1 | 1 | |||
| Ordered logit model | 1 | 1 | |||
| Instrumental variable probit model | 2 | 2 | |||
| Theoretical model | 1 | 1 | |||
| Theoretical model, a variation of the model presented by Gorodnichenko and Schnitzer (2013) | 2 | 2 | |||
| Tobit model | 1 | 1 | |||
| Econometric regression models | 1 | 1 | |||
| Logistic and panel regression models | 1 | 1 | |||
| Multilevel and panel regression models | 1 | 1 | |||
| Binary and ordered logit models | 1 | 1 | |||
| Propensity score matching | 1 | 1 | |||
| Propensity score weighting and DR curve | 1 | 1 | |||
| Neural networks | 1 | 1 | |||
| Regression | 2 | 2 | |||
| Panel data regression | 1 | 1 | |||
| Logistic and panel regression | 1 | 1 | |||
| Logistic and probability regression | 1 | 1 | |||
| Logistic regressions, support vector machines, neural networks, random forests and gradient boosting machines | 1 | 1 | |||
| Grand total | 2 | 10 | 31 | 5 | 48 |
| Study models | Econometric | Empirical | Statistical | Theoretical | Grand total |
|---|---|---|---|---|---|
| Autoregressive distributed lag (ARDL) model introduced by | 1 | 1 | |||
| Multiple discrete continuous extreme value (MDCEV) model | 1 | 1 | |||
| A two-step econometric procedure | 1 | 1 | |||
| Pearson correlation analysis | 2 | 2 | |||
| Difference-in-differences (D-i-D) analysis | 1 | 1 | |||
| Descriptive analysis | 4 | 4 | |||
| Factorial analysis | 1 | 1 | |||
| Multiple regression equations | 1 | 1 | |||
| Propensity score matching (PSM) and difference in differences (DID) | 1 | 1 | |||
| Two-step estimation with dynamic panel data (GMM) | 2 | 2 | |||
| Cross-sectional estimates of censored regression models with pooled data | 1 | 1 | |||
| Propensity score matching estimator (PSM) | 1 | 1 | |||
| Multilevel probabilistic model (MML) | 1 | 1 | |||
| Conceptual model and estimation models of fixed and random effects regression | 1 | 1 | |||
| Crepon, Duguet and Mairesse (CDM) model | 1 | 1 | |||
| Three-stage dynamic DDF-DEA model with financial regulation | 1 | 1 | |||
| Griliches R&D capital model and international collaborations | 2 | 2 | |||
| Difference in differences (DID) model | 1 | 1 | |||
| Structural equation model | 1 | 1 | |||
| Maximum likelihood model | 1 | 1 | |||
| Two-stage selection model and dynamic random effects probit | 1 | 1 | |||
| Signaling model based on maximum expected revenue for SMEs | 1 | 1 | |||
| Ordered logit model | 1 | 1 | |||
| Instrumental variable probit model | 2 | 2 | |||
| Theoretical model | 1 | 1 | |||
| Theoretical model, a variation of the model presented by | 2 | 2 | |||
| Tobit model | 1 | 1 | |||
| Econometric regression models | 1 | 1 | |||
| Logistic and panel regression models | 1 | 1 | |||
| Multilevel and panel regression models | 1 | 1 | |||
| Binary and ordered logit models | 1 | 1 | |||
| Propensity score matching | 1 | 1 | |||
| Propensity score weighting and DR curve | 1 | 1 | |||
| Neural networks | 1 | 1 | |||
| Regression | 2 | 2 | |||
| Panel data regression | 1 | 1 | |||
| Logistic and panel regression | 1 | 1 | |||
| Logistic and probability regression | 1 | 1 | |||
| Logistic regressions, support vector machines, neural networks, random forests and gradient boosting machines | 1 | 1 | |||
Note(s): This table categorizes the research models used in studies on innovation financing, displaying the distribution across econometric, empirical, statistical and theoretical approaches. The models are further identified by specific methodologies, highlighting their relevance and application in understanding the dynamics of innovation financing across various studies
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