Modeling Internet firm survival using Bayesian dynamic models with time-varying coefficients

We showcase a Bayesian dynamic analysis and apply it to a study on the impact of a set of industry, firm and e-commerce-related factors on Internet firm survival. Through the use of one age-based and another calendar time-based dynamic Bayesian model, we are able to examine how the impact of these factors changes over time. Our results are based on data from 115 publicly-traded Internet firms and suggest that Internet firm survival relates to different factors, such as the initial public offerings rate of Internet stocks in the market, financial capital and firm size at different stages in their lifetimes, whose influence may have changed over time.

[1]  S. Klepper.  Entry, Exit, Growth, and Innovation over the Product Life Cycle , 1996 .

[2]  Yuji Honjo,et al.  Business failure of new firms: an empirical analysis using a multiplicative hazards model , 2000 .

[3]  Thomas M. Springer,et al.  THE SURVIVAL OF INITIAL PUBLIC OFFERINGS IN THE AFTERMARKET , 1997 .

[4]  Donald B. Rubin,et al.  Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology , 2006 .

[5]  P. Holland.  Statistics and Causal Inference , 1985 .

[6]  N. Wermuth,et al.  Causality: a Statistical View , 2004 .

[7]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[8]  Andrew Whinston,et al.  Measuring the Internet Economy , 1999 .

[9]  Paul Newbold,et al.  Bankruptcy, Working Capital and Funds Flow Components , 1984 .

[10]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[11]  Adrian F. M. Smith,et al.  Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus , 1993 .

[12]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[13]  Joseph G. Ibrahim,et al.  Bayesian Survival Analysis , 2004 .

[14]  E. Brynjolfsson,et al.  Computing Productivity: Firm-Level Evidence , 2003 .

[15]  G. Day,et al.  Shakeouts in Digital Markets: Lessons from B2B Exchanges , 2003, IEEE Engineering Management Review.

[16]  A. Dempster.  Causality and statistics , 1990 .

[17]  Nanny Wermuth,et al.  Multivariate Dependencies: Models, Analysis and Interpretation , 1996 .

[18]  J M Violanti,et al.  Cellular phones and fatal traffic collisions. , 1998, Accident; analysis and prevention.

[19]  Xiaoyun Yu,et al.  Evidence of Information Spillovers in the Production of Investment Banking Services , 2001 .

[20]  Paul Newbold,et al.  Funds Flow Components, Financial Ratios, and Bankruptcy , 1987 .

[21]  David B. Audretsch,et al.  New-Firm Survival and the Technological Regime , 1991 .

[22]  D. Cox.  Causality : some statistical aspects , 1992 .

[23]  S. Klepper,et al.  Time Paths in the Diffusion of Product Innovations , 1982 .

[24]  G. Box.  Use and Abuse of Regression , 1966 .

[25]  David B. Audretsch,et al.  New Firm Survival: New Results Using a Hazard Function , 1995 .

[26]  A. L. Edwards,et al.  An introduction to linear regression and correlation. , 1985 .

[27]  Chap T. Le,et al.  Applied Survival Analysis , 1998 .

[28]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .