Journal articles
Breitung J, Kripfganz S, Hayakawa K (2021). Bias-corrected method of moments estimators for dynamic panel data models.
Econometrics and Statistics,
24, 116-132.
Abstract:
Bias-corrected method of moments estimators for dynamic panel data models
A computationally simple bias correction for linear dynamic panel data models is proposed and its asymptotic properties are studied when the number of time periods is fixed or tends to infinity with the number of panel units. The approach can accommodate both fixed-effects and random-effects assumptions, heteroskedastic errors, as well as higher-order autoregressive models. Panel-corrected standard errors are proposed that allow for robust inference in dynamic models with cross-sectionally correlated errors. Monte Carlo experiments suggest that under the assumption of strictly exogenous regressors the bias-corrected method of moment estimator outperforms popular GMM estimators in terms of efficiency and correctly sized tests.
Abstract.
Kiviet JF, Kripfganz S (2021). Instrument approval by the Sargan test and its consequences for coefficient estimation.
Economics Letters,
205Abstract:
Instrument approval by the Sargan test and its consequences for coefficient estimation
Empirical econometric findings are often vindicated by supplementing them with the p-values of Sargan-Hansen tests for overidentifying restrictions, provided these exceed a chosen small nominal significance level. It is illustrated here that the probability that such tests reject instrument validity may often barely exceed small levels, even when instruments are seriously invalid, whereas even minor invalidity of instruments can severely undermine inference on regression coefficients by instrumental variable estimators. These uncomfortable patterns may be aggravated when particular valid or invalid instruments are relatively weak or strong.
Abstract.
Kripfganz S, Sarafidis V (2021). Instrumental-variable estimation of large-T panel-data models with common factors.
Stata Journal,
21(3), 659-686.
Abstract:
Instrumental-variable estimation of large-T panel-data models with common factors
In this article, we introduce the xtivdfreg command, which implements a general instrumental-variables (IV) approach for fitting panel-data models with many time-series observations, T, and unobserved common factors or interactive effects, as developed by Norkute et al. (2021, Journal of Econometrics 220: 416–446) and Cui et al. (2020a , ISER Discussion Paper 1101). The underlying idea of this approach is to project out the common factors from exogenous covariates using principal-components analysis and to run IV regression in both of two stages, using defactored covariates as instruments. The resulting two-stage IV estimator is valid for models with homogeneous or heterogeneous slope coefficients and has several advantages relative to existing popular approaches.
In addition, the xtivdfreg command extends the two-stage IV approach in two major ways. First, the algorithm accommodates estimation of unbalanced panels. Second, the algorithm permits a flexible specification of instruments.
We show that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular Stata ivregress command. Notably, unlike ivregress, xtivdfreg permits estimation of the two-way error-components paneldata model with heterogeneous slope coefficients.
Abstract.
Kripfganz S, Kiviet JF (2021). kinkyreg: Instrument-free inference for linear regression models with endogenous regressors.
Stata Journal,
21(3), 772-813.
Abstract:
kinkyreg: Instrument-free inference for linear regression models with endogenous regressors
In models with endogenous regressors, a standard regression approach is to exploit just-identifying or overidentifying orthogonality conditions by using instrumental variables. In just-identified models, the identifying orthogonality assumptions cannot be tested without the imposition of other nontestable assumptions. While formal testing of overidentifying restrictions is possible, its interpretation still hinges on the validity of an initial set of untestable just-identifying orthogonality conditions. We present the kinkyreg command for kinky least-squares inference, which adopts an alternative approach to identification. By exploiting nonorthogonality conditions in the form of bounds on the admissible degree of endogeneity, feasible test procedures can be constructed that do not require instrumental variables. The kinky least-squares confidence bands can be more informative than confidence intervals obtained from instrumental-variables estimation, especially when the instruments are weak. Moreover, the approach facilitates a sensitivity analysis for standard instrumental-variables inference. In particular, it allows the user to assess the validity of previously untestable just-identifying exclusion restrictions. Further instrument-free tests include linear hypotheses, functional form, heteroskedasticity, and serial correlation tests.
Abstract.
Kripfganz S, Schneider DC (2020). Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models.
Oxford Bulletin of Economics and Statistics,
82(6), 1456-1481.
Abstract:
Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models
We consider the popular ‘bounds test’ for the existence of a level relationship in conditional equilibrium correction models. By estimating response surface models based on about 95 billion simulated F-statistics and 57 billion t-statistics, we improve upon and substantially extend the set of available critical values, covering the full range of possible sample sizes and lag orders, and allowing for any number of long-run forcing variables. By computing approximate p-values, we find that the bounds test can be easily oversized by more than 5 percentage points in small samples when using asymptotic critical values.
Abstract.
Kripfganz S, Schwarz C (2019). Estimation of linear dynamic panel data models with time-invariant regressors.
Journal of Applied Econometrics,
34(4), 526-546.
Abstract:
Estimation of linear dynamic panel data models with time-invariant regressors
We present a sequential approach to estimating a dynamic Hausman‐Taylor model. We first estimate the coefficients of the time‐varying regressors and subsequently regress the first‐stage residuals on the time‐invariant regressors. In comparison to estimating all coefficients simultaneously, this two‐stage procedure is more robust against model misspecification, allows for a flexible choice of the first‐stage estimator, and enables simple testing of the overidentifying restrictions. For correct inference, we derive analytical standard error adjustments. We evaluate the finite‐sample properties with Monte Carlo simulations and apply the approach to a dynamic gravity equation for U.S. outward foreign direct investment.
Abstract.
Kripfganz S (2016). Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models.
Stata Journal,
16(4), 1013-1038.
Abstract:
Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models
In this article, I describe the xtdpdqml command for the quasi–maximum likelihood estimation of linear dynamic panel-data models when the time horizon is short and the number of cross-sectional units is large. Based on the theoretical groundwork by Bhargava and Sargan (1983, Econometrica 51: 1635–1659) and Hsiao, Pesaran, and Tahmiscioglu (2002, Journal of Econometrics 109: 107–150), the marginal distribution of the initial observations is modeled as a function of the observed variables to circumvent a short-T dynamic panel-data bias. Both random-effects and fixed-effects versions are available.
Abstract.
