The paper presents a labour adjustment demand model for the private sector of Podkarpacki province in Poland. The model sets up short run and long run relationships between labour demand determinants through an error self correct process.

Due to the nature of this autoregressive distributed lags model(ADL)  and to very short period of the sample, traditional methods of parameter estimation are ineffective. Subsequently, we propose to apply the generalized maximum Tsallis entropy econometric approach –as a generalization of Jaynes-Shannon-Gibbs Information theoretic entropy formalism already applied to classical econometrics(Golan[and oth.],1996)-  to estimate the parameters of the model. Based upon multifractal dimensions,Tsallis non extensive entropy has proved to be useful in the case  of high frequency financial market analysis. We then extend the approach to classical econometrics case when small, lower frequency data sample is available. Such a sparse data  sample should display too tail queue Gaussian distribution.

Parameters estimation approach is presented and  related- inferential  information indexes proposed and computed. In particular, we note the value of  non extensive parameter q which varies in response to changes induced to the weighting parameter of criterion function of the model.

Tsallis entropy outputs are compared to those of Jaynes-Shannon-Gibbs approach and of classical LS method computations. Despite enough similar results, we conclude that non extensive entropy leads to a better in-sample prediction of the model  but seems to be, at least in the present study,  as instable as Jaynes-Shanon approach with respect to initial conditions. 

On economic side, above results show a slow adjusting mechanism of actual lever of employment to its long run targeted equilibrium owing to expected market perspectives.

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