A comparison of complementary automatic modeling methods: RETINA and PcGets. The authors [Oxford Bull. Econom. Stat. 65, Suppl. 1, 821–838 (2003)] proposed an automatic predictive modeling tool called relevant transformation of the inputs network approach (RETINA). It is designed to embody flexibility (using nonlinear transformations of the predictors of interest), selective search within the range of possible models, control of collinearity, out-of-sample forecasting ability, and computational simplicity. Here they compare the characteristics of RETINA with PcGets, a well-known automatic modeling method proposed by D. Hendry. We point out similarities, differences, and complementarities of the two methods. In an example using U.S. telecommunications demand data they find that RETINA can improve both in- and out-of-sample over the usual linear regression model and over some models like PcGets. Thus, both methods are useful components of the modern applied econometric automated modeling tool chest.

References in zbMATH (referenced in 44 articles , 1 standard article )

Showing results 1 to 20 of 44.
Sorted by year (citations)

1 2 3 next

  1. Becker, William; Paruolo, Paolo; Saltelli, Andrea: Variable selection In regression models using global sensitivity analysis (2021)
  2. Galbraith, John W.; Zinde-Walsh, Victoria: Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects (2020)
  3. Daubechies, Ingrid (ed.); Kutyniok, Gitta (ed.); Rauhut, Holger (ed.); Strohmer, Thomas (ed.): Applied harmonic analysis and data processing. Abstracts from the workshop held March 25--31, 2018 (2018)
  4. Altansukh, Gantungalag; Becker, Ralf; Bratsiotis, George; Osborn, Denise R.: What is the globalisation of inflation? (2017)
  5. Castle, Jennifer L.: Sir Clive W. J. Granger model selection (2017)
  6. Hoeher, P. A.: OFDM data detection and channel estimation (2017)
  7. Groen, Jan J. J.; Kapetanios, George: Revisiting useful approaches to data-rich macroeconomic forecasting (2016)
  8. Johansen, Søren; Nielsen, Bent: Asymptotic theory of outlier detection algorithms for linear time series regression models (2016)
  9. Kapetanios, George; Marcellino, Massimiliano; Papailias, Fotis: Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods (2016)
  10. Bekaert, Geert; Hoerova, Marie: The VIX, the variance premium and stock market volatility (2014)
  11. Huang, Tao; Fildes, Robert; Soopramanien, Didier: The value of competitive information in forecasting FMCG retail product sales and the variable selection problem (2014) ioport
  12. Castle, Jennifer L.; Clements, Michael P.; Hendry, David F.: Forecasting by factors, by variables, by both or neither? (2013)
  13. Savin, Ivan; Winker, Peter: Lasso-type and heuristic strategies in model selection and forecasting (2013)
  14. Castle, Jennifer L.; Doornik, Jurgen A.; Hendry, David F.: Model selection when there are multiple breaks (2012)
  15. Gloria, Antoine: Numerical homogenization: survey, new results, and perspectives (2012)
  16. Savin, Ivan; Winker, Peter: Heuristic optimization methods for dynamic panel data model selection: application on the Russian innovative performance (2012)
  17. Caggiano, Giovanni; Kapetanios, George; Labhard, Vincent: Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK (2011)
  18. Barhoumi, Karim; Darné, Olivier; Ferrara, Laurent: Are disaggregate data useful for factor analysis in forecasting French GDP? (2010)
  19. Bollerslev, Tim (ed.); Russell, Jeffrey R. (ed.); Watson, Mark W. (ed.): Volatility and time series econometrics. Essays in honor of Robert F. Engle (2010)
  20. Hassler, Uwe: Testing regression coefficients after model selection through sign restrictions (2010)

1 2 3 next