Description Usage Arguments Details Value Note Author(s) References See Also Examples

In output based efficiency measurement, routine `nptestind`

perform test that radial (Debreu-Farrell) output-based measure of technical efficiency under chosen assumption about the technology and mix of outputs are independent. In input-based efficiency measurement, routine `nptestind`

perform test that radial (Debreu-Farrell) input-based measure of technical efficiency under chosen assumption about the technology and mix of inputs are independent. Testing is performed using bootstrap technique.

1 2 3 4 |

`formula` |
an object of class “formula” (or one that can be coerced to that class): a symbolic description of the model. The details of model specification are given under ‘Details’. |

`data` |
an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment ( |

`subset` |
an optional vector specifying a subset of observations for which technical efficiency is to be computed. |

`rts` |
character or numeric. string: first letter of the word “c” for constant, “n” for non-increasing, or “v” for variable returns to scale assumption. numeric: 3 for constant, 2 for non-increasing, or 1 for variable returns to scale assumption. |

`base` |
character or numeric. string: first letter of the word “o” for computing output-based or “i” for computing input-based technical efficiency measure. string: 2 for computing output-based or 1 for computing input-based technical efficiency measure |

`reps` |
specifies the number of bootstrap replications to be performed. The default is 999. The minimum is 100. Adequate estimates of confidence intervals using bias-corrected methods typically require 1,000 or more replications. |

`alpha` |
sets significance level; default is |

`dots` |
logical. Relevant if |

`print.level` |
numeric. 0 - nothing is printed; 1 - print summary of the model and data. 2 - print summary of technical efficiency measures. 3 - print estimation results observation by observation. Default is 1. |

In output based efficiency measurement, routine `nptestind`

perform test that radial (Debreu-Farrell) output-based measure of technical efficiency under chosen assumption about the technology and mix of outputs are independent. In input-based efficiency measurement, routine `nptestind`

perform test that radial (Debreu-Farrell) input-based measure of technical efficiency under chosen assumption about the technology and mix of inputs are independent.

Testing is performed using bootstrap technique (see Wilson, 2003).

Results can be summarized using `summary.npsf`

.

`nptestrts`

returns a list of class `npsf`

containing the following elements:

`K` |
numeric: number of data points. |

`M` |
numeric: number of outputs. |

`N` |
numeric: number of inputs. |

`rts` |
string: RTS assumption. |

`base` |
string: base for efficiency measurement. |

`reps` |
numeric: number of bootstrap replications. |

`alpha` |
numeric: significance level. |

`t4n` |
numeric: value of the T4n statistic. |

`pval` |
numeric: p-value of the test of independence. |

Results can be summarized using `summary.npsf`

.

Oleg Badunenko <oleg.badunenko@brunel.ac.uk>

Färe, R. and Lovell, C. A. K. (1978), Measuring the technical efficiency of production, *Journal of Economic Theory*, **19**, 150–162, doi: 10.1016/0022-0531(78)90060-1

Färe, R., Grosskopf, S. and Lovell, C. A. K. (1994), *Production Frontiers*, Cambridge U.K.: Cambridge University Press

Wilson P.W. (2003), Testing Independence in Models of Productive Efficiency, *Journal of Productivity Analysis*, **20**, 361–390, doi: 10.1023/A:1027355917855

`teradial`

, `tenonradial`

, `teradialbc`

, `tenonradialbc`

, `nptestrts`

, `sf`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ```
## Not run:
require( npsf )
# Prepare data and matrices
data( ccr81 )
head( ccr81 )
# Create some missing values
ccr81 [64, "x4"] <- NA # just to create missing
ccr81 [68, "y2"] <- NA # just to create missing
Y2 <- as.matrix( ccr81[ , c("y1", "y2", "y3"), drop = FALSE] )
X2 <- as.matrix( ccr81[ , c("x1", "x2", "x3", "x4", "x5"), drop = FALSE] )
# Perform nonparametric test that radial (Debreu-Farrell)
# output-based measure of technical efficiency under assumption of
# NIRS technology and mix of outputs are independent. Test is
# performed based on 999 replications at the 5
t1 <- nptestind ( y1 + y2 + y3 ~ x1 + x2 + x3 + x4 + x5,
data = ccr81, base = "o", rts = "n",
reps = 999, dots = TRUE)
# Really large data-set
data(usmanuf)
head(usmanuf)
nrow(usmanuf)
table(usmanuf$year)
# This will take some time depending on computer power
data(usmanuf)
head(usmanuf)
t2 <- nptestind ( Y ~ K + L + M, data = usmanuf,
subset = year >= 1999 & year <= 2000,
reps = 999, dots = TRUE, base = "i", rts = "v")
## End(Not run)
``` |

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