Skip to main content
Erschienen in:

2023 | OriginalPaper | Buchkapitel

1. Single-Equation Econometric Model

verfasst von : Jerzy Witold Wiśniewski

Erschienen in: Forecasting from Multi-equation Econometric Micromodels

Verlag: Springer Nature Switzerland

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The first chapter is devoted to econometric models in the form of one stochastic equation. Linear, product, and limited dependent variable models were presented. In this chapter all stages of building a single-equation econometric model are discussed. These are: specification, estimation of parameters, verification, and exploitation possibilities. Basic methods of estimation of structural parameters and parameters of the stochastic structure of the model are presented. Basic statistical measures determining the quality of an empirical econometric model are also discussed.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Fußnoten
1
The nature of the category represented by the dependent variable assigns the model to a specific discipline. A dependent variable representing a demographic category, for instance, makes the model demometric; if the dependent variable is sociological in character—the model is sociometric; when the dependent variable represents a psychological category—the model is psychometric.
 
2
Structural parameter αj(j = 1, …, k) indicates an increase in the value of observation xtj by one unit, which, assuming immutability of other explanatory variables (the ceteris paribus principle), changes the size of yt by αj units.
 
3
Explanatory variables of discrete nature (discrete variables) should be included in the model only exponentially, as it is difficult, in power series terms, to assign economic interpretation to structural parameters.
 
4
The statistical data collected must be of appropriate quality, i.e., it should be comparable, with no gaps in the statistical series, both in the interior and on the edges thereof. Any minor data deficiencies can be filled using statistical techniques (interpolation, extrapolation). The statistical material should be free of any statistical bias.
 
5
For models consisting of multiple equations.
 
6
The estimator ought to be selected to contain the necessary statistical properties, i.e., consistency, unbiasedness, efficiency, and sufficiency. Details on that are presented in Sect. 1.3.
 
7
Global and specific goodness-of-fit measures.
 
8
Cf. Wiśniewski (1986), subchapter 1.5. This concept means that an economic variable, which—from research perspective—best reflects the economic category constituting the subject of an empirical verification, is called an equivalent variable. Cf. works Wiśniewski (2013), subchapter 1.3.
 
9
Including the amount of tax on goods and services.
 
10
The term estimator precision is more appropriate, in terms of estimator properties, than the phrase estimator efficiency. Efficiency is usually associated with effectiveness, whereas this property refers to the estimates’ accuracy.
 
11
The Aitken’s method is recommended when the random component variations for different statistical observations are not equal, that is, equality (1.16) does not hold.
 
12
Random component autocorrelation is a model specification error, for it can result from: 1) an omission of an important, statistically significant explanatory variable in the empirical model, which results in a positive autocorrelation; 2) a defective analytical form of the empirical model, resulting in a positive random component autocorrelation; 3) an excess of statistically insignificant variables in the empirical model, resulting in a negative random component autocorrelation.
 
13
It can be demonstrated that if first-order autocorrelation does not occur in the model, there is no autocorrelation of higher order either. Occurrence of first-order autocorrelation, however, indicates a model specification error, which necessitates model re-specification. Re-specification should be continued until the empirical model lacks first-order random component autocorrelation.
 
14
If DW(DW*) > du, there are no grounds for rejection of the null hypothesis (H0), which means that no random component autocorrelation occurrence can be inferred, with a risk of a first-type error (at significance level γ). If DW(DW*) < dl, hypothesis H0 is rejected in favor of an alternative one, by which first-order random component autocorrelation can be inferred. When dl ≤ DW(DW*) ≤ du, the test does not determine whether autocorrelation occurs or not. This indicates that the DW statistic has hit a region of test insensitivity, which is synonymous with necessary application of another test of random component autocorrelation, e.g., the Student’s t-test, to examine the autocorrelation coefficient.
 
15
The alternative hypothesis implies that the statistical test is a test with the so-called two-sided critical region.
 
16
Most commonly, a significance level of γ = 0.01 or γ = 0.05 is selected, which implies acceptance of 1% or 5% risk of Type I error.
 
17
Provided that all previous measures of the model’s goodness of fit are at a satisfactory level.
 
18
An extensive discussion of econometric model building for bound dependent variable transformation can be found in Wiśniewski (1986).
 
19
Cf. Goldberger (1972: 321).
 
Literatur
Zurück zum Zitat Goldberger AS (1972) Teoria ekonometrii. PWN, Warszawa Goldberger AS (1972) Teoria ekonometrii. PWN, Warszawa
Zurück zum Zitat Wiśniewski JW (1986) Ekonometryczne badanie zjawisk jakościowych. Studium metodologiczne. UMK, Toruń Wiśniewski JW (1986) Ekonometryczne badanie zjawisk jakościowych. Studium metodologiczne. UMK, Toruń
Zurück zum Zitat Wiśniewski JW (2013) Forecasting staffing decisions. Econometrics 1(39):22–29 Wiśniewski JW (2013) Forecasting staffing decisions. Econometrics 1(39):22–29
Metadaten
Titel
Single-Equation Econometric Model
verfasst von
Jerzy Witold Wiśniewski
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-031-27492-3_1

Premium Partner