Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Tverrsnittsfordelt lag× | Kryss-seksjonell NARDL× | Panel VARX× | |
|---|---|---|---|
| Fagfelt | Økonometri | Økonometri | Økonometri |
| Familie | Regression model | Regression model | Regression model |
| Opprinnelsesår≠ | 2001 | 2014 | 2013 |
| Opphavsperson≠ | Pesaran, Shin, and Smith | Yongcheol Shin and colleagues | Canova and Ciccarelli |
| Type≠ | Distributed lag model | Asymmetric panel model | Multi-equation panel model |
| Opprinnelig kilde≠ | Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships and dynamics. Journal of Applied Econometrics, 16(3), 289-326. DOI ↗ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a system of nonlinear autoregressive distributed lag equations. Econometric Reviews, 33(1), 56-87. link ↗ | Canova, F., & Ciccarelli, M. (2013). Panel vector autoregressive models: A survey. Advances in Econometrics, 32, 205-246. DOI ↗ |
| Alias | Panel distributed lag model | NARDL panel | Panel VAR-X |
| Relaterte | 3 | 3 | 3 |
| Sammendrag≠ | CS-DL (Cross-Sectional Distributed Lag) is a simplified dynamic panel model regressing outcomes on current and lagged explanatory variables without explicit autoregressive terms, while accounting for cross-sectional dependence. Built on Pesaran et al. (2001) and extended by Chudik et al. (2014), it estimates dynamic effects more parsimoniously than ARDL when autocorrelated lags are less critical. This approach is valuable for short-horizon effects and policy impact analysis. | CS-NARDL extends the nonlinear autoregressive distributed lag (NARDL) model to panel data, capturing asymmetric long-run and short-run relationships where positive and negative changes in explanatory variables have differential effects. Introduced by Shin et al. (2014) and adapted to panels, it allows studying how cross-sectional units respond differently to positive versus negative shocks while maintaining cointegrating relationships. This approach is essential for understanding economic asymmetries in commodity markets, monetary transmission, and labor markets. | Panel VARX extends vector autoregression to heterogeneous panels with exogenous variables, enabling simultaneous modeling of multiple endogenous variables alongside observed external factors across many units. Introduced by Holtz-Eakin et al. (1988) and advanced by Canova and Ciccarelli (2013), it captures dynamic relationships within units while allowing parameters to vary across units. This framework is essential for macroeconomic panels and understanding cross-unit heterogeneity in responses to common shocks. |
| ScholarGateDatasett ↗ |
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