| 1. |
- Ankargren, Sebastian, et al.
(författare)
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Simulation smoothing for nowcasting with large mixed-frequency VARs
- 2021
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062. ; 19, s. 97-113
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Tidskriftsartikel (refereegranskat)abstract
- There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macro-economic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows. We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.
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| 2. |
- Cantoni, Eva, et al.
(författare)
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Semiparametric inference with missing data : robustness to outliers and model misspecification
- 2020
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062. ; 16, s. 108-120
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Tidskriftsartikel (refereegranskat)abstract
- Classical semiparametric inference with missing outcome data is not robust to contamination of the observed data and a single observation can have arbitrarily large influence on estimation of a parameter of interest. This sensitivity is exacerbated when inverse probability weighting methods are used, which may overweight contaminated observations. Inverse probability weighted, double robust and outcome regression estimators of location and scale parameters are introduced, which are robust to contamination in the sense that their influence function is bounded. Asymptotic properties are deduced and finite sample behaviour studied. Simulated experiments show that contamination can be more serious a threat to the quality of inference than model misspecification. An interesting aspect of the results is that the auxiliary outcome model used to adjust for ignorable missingness by some of the estimators, is also useful to protect against contamination. Both adjustment to ignorable missingness and protection against contamination are achieved through weighting schemes. A case study illustrates how the resulting weights can be studied to gain insights on how the two different weighting schemes interact.
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| 3. |
- He, Changli, et al.
(författare)
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The shifting seasonal mean autoregressive model and seasonality in the Central England monthly temperature series, 1772–2016
- 2019
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Ingår i: Econometrics and Statistics. - : Elsevier BV. - 2452-3062. ; 12, s. 1-24
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Tidskriftsartikel (refereegranskat)abstract
- A new autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time is introduced. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed as in ARCH models. This variance is also allowed to be smoothly and deterministically time-varying. Under regularity conditions, consistency and asymptotic normality of the maximum likelihood estimators of parameters of this model is proved. The purpose of the model is to find out how the average monthly temperatures in the well-known central England temperature series have been varying during the period of more than 240 years. The main result is that warming has occurred but that there are notable differences between months. In particular, no warming is found for February, April, May and June.
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| 4. |
- Javed, Farrukh, 1984-, et al.
(författare)
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Edgeworth Expansions for Multivariate Random Sums
- 2024
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062. ; 31, s. 66-80
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Tidskriftsartikel (refereegranskat)abstract
- The sum of a random number of independent and identically distributed random vectors has a distribution which is not analytically tractable, in the general case. The problem has been addressed by means of asymptotic approximations embedding the number of summands in a stochastically increasing sequence. Another approach relies on fitting flexible and tractable parametric, multivariate distributions, as for example finite mixtures. Both approaches are investigated within the framework of Edgeworth expansions. A general formula for the fourth-order cumulants of the random sum of independent and identically distributed random vectors is derived and it is shown that the above mentioned asymptotic approach does not necessarily lead to valid asymptotic normal approximations. The problem is addressed by means of Edgeworth expansions. Both theoretical and empirical results suggest that mixtures of two multivariate normal distributions with proportional covariance matrices satisfactorily fit data generated from random sums where the counting random variable and the random summands are Poisson and multivariate skew-normal, respectively.
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| 5. |
- Källberg, David, 1982-, et al.
(författare)
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Large sample properties of entropy balancing estimators of average causal effects
- 2023
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062.
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Tidskriftsartikel (refereegranskat)abstract
- Weighting methods are used in observational studies to adjust for covariate imbalances between treatment and control groups. Entropy balancing (EB) is an alternative to inverse probability weighting with an estimated propensity score. The EB weights are constructed to satisfy balance constraints and optimized towards stability. Large sample properties of EB estimators of the average causal treatment effect, based on the Kullback-Leibler and quadratic Rényi relative entropies, are described. Additionally, estimators of their asymptotic variances are proposed. Even though the objective of EB is to reduce model dependence, the estimators are generally not consistent unless implicit parametric assumptions for the propensity score or conditional outcomes are met. The finite sample properties of the estimators are investigated through a simulation study. The average causal effect of smoking on blood lead levels is estimated using data from the National Health and Nutrition Examination Survey.
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| 6. |
- Lee, Seong-ho, et al.
(författare)
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Covariate balancing for causal inference on categorical and continuous treatments
- 2022
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062.
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Tidskriftsartikel (refereegranskat)abstract
- Novel estimators of causal effects for categorical and continuous treatments are proposed by using an optimal covariate balancing strategy for inverse probability weighting. The resulting estimators are shown to be consistent and asymptotically normal for causal contrasts of interest, either when the model explaining the treatment assignment is correctly specified, or when the correct set of bases for the outcome models has been chosen and the assignment model is sufficiently rich. For the categorical treatment case, the estimator attains the semiparametric efficiency bound when all models are correctly specified. For the continuous case, the causal parameter of interest is a function of the treatment dose. The latter is not parametrized and the estimators proposed are shown to have bias and variance of the classical nonparametric rate. Asymptotic results are complemented with simulations illustrating the finite sample properties. A data analysis suggests a nonlinear effect of BMI on self-reported health decline among the elderly.
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| 7. |
- Norkutė, Milda, et al.
(författare)
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The factor analytical method for interactive effects dynamic panel models with moving average errors
- 2019
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Ingår i: Econometrics and Statistics. - : Elsevier BV. - 2452-3062. ; 11, s. 83-104
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Tidskriftsartikel (refereegranskat)abstract
- The estimation of dynamic panel data models with interactive effects and moving average errors is considered. This is accomplished by making an extension to the factor analytical (FA) estimator which was originally designed for dynamic panels with fixed effects only and serially uncorrelated errors. The results show that the additional allowances have no effect on the asymptotic properties of the FA estimator. In particular, the asymptotic distribution of the estimator is free of the otherwise so common bias problem, a result that is verified in small samples using Monte Carlo simulation.
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| 8. |
- Pya Arnqvist, Natalya, universitetslektor, et al.
(författare)
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Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers
- 2021
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062. ; 18, s. 89-105
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Tidskriftsartikel (refereegranskat)abstract
- One of the primary concerns of product quality control in the automotive industry is an automated detection of defects of small sizes on specular car body surfaces. A new statistical learning approach is presented for surface finish defect detection based on spline smoothing method for feature extraction and k-nearest neighbour probabilistic classifier. Since the surfaces are specular, structured lightning reflection technique is applied for image acquisition. Reduced rank cubic regression splines are used to smooth the pixel values while the effective degrees of freedom of the obtained smooths serve as components of the feature vector. A key advantage of the approach is that it allows reaching near zero misclassification error rate when applying standard learning classifiers. In addition, probability based performance evaluation metrics have been proposed as alternatives to the conventional metrics. The usage of those provides the means for uncertainty estimation of the predictive performance of a classifier. Experimental classification results on the images obtained from the pilot system located at Volvo GTO Cab plant in Umeå, Sweden, show that the proposed approach is much more efficient than the compared methods.
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| 9. |
- Qasim, Muhammad, et al.
(författare)
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Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity
- 2024
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062.
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Tidskriftsartikel (refereegranskat)abstract
- A Stein-type control function maximum likelihood (CFML) estimator is suggested for the probit model in the presence of endogeneity. This novel estimator combines the probit maximum likelihood and CFML estimators. The asymptotic distribution and risk function for the new estimator is derived. It is demonstrated that, subject to certain conditions of the shrinkage parameter, the asymptotic risk of the new estimator is strictly smaller than the risk of the CFML. Monte Carlo simulations illustrate the method's superiority in finite samples. The method is also applied to analyze the impact of managerial incentives on the use of foreign-exchange derivatives.
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