ScholarGate
Assistent

Sammenlign metoder

Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.

Pesaran CD-test: Diagnostikk for tverrsnittsavhengighet i paneldata×Breusch-Godfrey LM-test for seriell korrelasjon×CIPS-testen×Frees' tverrsnittsavhengighetstest for paneldata×
FagfeltØkonometriØkonometriØkonometriØkonometri
FamilieHypothesis testRegression modelHypothesis testHypothesis test
Opprinnelsesår2021197820071995
OpphavspersonM. Hashem PesaranTrevor Breusch & Leslie GodfreyM. Hashem PesaranEdward Frees
TypeNon-parametric diagnostic testLagrange-multiplier test for serial correlationPanel unit-root test with cross-section dependenceNon-parametric panel diagnostic test
Opprinnelig kildePesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50. DOI ↗Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46(6), 1293–1301. DOI ↗Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312. DOI ↗Frees, E. W. (1995). Assessing cross-sectional correlation in panel data. Journal of Econometrics, 69(2), 393–414. DOI ↗
AliasCD Test, Cross-Sectional Dependence Test, Pesaran General CD Test, Kesitsel Bağımlılık TestiBG test, LM test for autocorrelation, Breusch-Godfrey serial correlation test, Breusch-Godfrey otokorelasyon testiPesaran CIPS Test, Cross-Sectionally Augmented IPS, Second-Generation Panel Unit-Root Test, CIPS Birim Kök TestiFrees CD Test, Frees Q-statistic Test, Cross-Sectional Dependence Test (Frees), Frees Bağımlılık Testi
Relaterte3333
SammendragThe Pesaran CD test is a general diagnostic procedure for detecting cross-sectional dependence in panel data models. Developed by M. Hashem Pesaran (2021), it is applicable to both balanced and unbalanced panels with large N and T, and retains validity under heterogeneous slope coefficients. The test is widely adopted in empirical economics, finance, and political economy as a prerequisite check before selecting appropriate estimators or unit-root tests for panel datasets.The Breusch-Godfrey test is a Lagrange-multiplier test for serial correlation in regression residuals, developed independently by Trevor Breusch (1978) and Leslie Godfrey (1978). Unlike the Durbin-Watson test, it detects autocorrelation up to any chosen order p, remains valid when the model includes lagged dependent variables, and produces a definite chi-square p-value rather than an inconclusive region — making it the modern standard for autocorrelation testing.The CIPS test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed for panels in which the cross-sectional units share unobserved common factors that induce cross-section dependence. By augmenting each individual ADF regression with cross-sectional averages and their lags, the CIPS test accounts for this dependence and produces reliable inference where first-generation tests such as the original IPS test break down. It is widely applied in macroeconomic and finance panels where shocks propagate across countries or regions.The Frees test, introduced by Edward Frees in 1995, is a non-parametric diagnostic procedure for detecting cross-sectional dependence in panel data. It is designed for settings where N (number of units) is large and T (time periods) is moderate, making it a standard pre-estimation check before applying panel regression methods that assume cross-sectional independence. Applied economists and social scientists routinely use it to verify whether units in the panel share common shocks or spatial linkages.
ScholarGateDatasett
  1. v1
  2. 1 Kilder
  3. PUBLISHED
  1. v1
  2. 2 Kilder
  3. PUBLISHED
  1. v1
  2. 1 Kilder
  3. PUBLISHED
  1. v1
  2. 1 Kilder
  3. PUBLISHED

Gå til søk Last ned lysbilder

ScholarGateSammenlign metoder: Pesaran CD Test · Breusch-Godfrey Test · CIPS Test · Frees Test. Hentet 2026-06-19 fra https://scholargate.app/no/compare