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TSP / What is TSP? / Features / Requirements

 

 

Benchmarks

 

Here are some standard benchmark datasets and models for testing the accuracy of TSP. For an updated and expanded set of benchmarks, please see this page: http://www.stanford.edu/~clint/bench , which includes a high accuracy Klein-I FIML benchmark, more AR(1) benchmarks, many ARIMA models (exact ML), GARCH(1,1), Probit, Logit, Poisson, Negative Binomial, Ordered Probit.

 

  • Longley, JASA (1967) -- linear regression

 

 

  • Grunfeld investment data -- analyzed in Theil (1971)
    • grunsur.tsp computes the following:
      • OLS
      • SUR - Seemingly Unrelated Regressions
      • iterated SUR
      • (iterated) SUR with cross-equation constraints

 

  • Bartlett pears data -- analyzed by Hildreth and Lu, and by Henshaw
    • pears.tsp computes the following:
    • OLS
    • Durbin-Watson statistic and its exact P-value
    • AR(1) conditional ML via grid search (Hildreth-Lu)
    • AR(1) conditional ML via full iteration (Cochran-Orcutt)
    • AR(1) exact ML via full iteration (Beach-MacKinnon)

 

 

  • National Institutes of Standards and Technology (NIST) "Statistical Reference Datasets"
    • NIST StRD web page  - contains many test problems and certified results for univariate statistics, anova, linear regression, and nonlinear regression.
    • Univariate statistics
      • Table of results for Sun 4 TSP obtains at least 8 correct digits for mean, standard deviation, and first order autocorrelation for 7 data series.
      • uni.tsp Code to run 7 series
    • ANOVA
    • Ordinary Least Squares
    • Nonlinear Least Squares

 

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