nep-ecm New Economics Papers
on Econometrics
Issue of 2023‒11‒06
twelve papers chosen by
Sune Karlsson, Örebro universitet

  1. Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models By Mirko Armillotta; Paolo Gorgi
  2. Cutting Feedback in Misspecified Copula Models By Michael Stanley Smith; Weichang Yu; David J. Nott; David Frazier
  3. Testing Identification Conditions of LATE in Fuzzy Regression Discontinuity Designs By Yu-Chin Hsu; Ji-Liang Shiu; Yuanyuan Wan
  4. Observation-Driven filters for Time-Series with Stochastic Trends and Mixed Causal Non-Causal Dynamics By Francisco Blasques; Siem Jan Koopman; Gabriele Mingoli
  5. Many (Weak) Judges in Judge-Leniency Designs By Jochmans, Koen
  6. A test on the location of tangency portfolio for small sample size and singular covariance matrix By Drin, Svitlana; Mazur, Stepan; Muhinyuza, Stanislas
  7. Embrace the Noise: It Is OK to Ignore Measurement Error in a Covariate, Sometimes By Dong, Hao; Millimet, Daniel L.
  8. Multiple change point detection under serial dependence: wild contrast maximisation and gappy Schwarz algorithm By Cho, Haeran; Fryzlewicz, Piotr
  9. Estimating Individual Responses when Tomorrow Matters By Stephane Bonhomme; Angela Denis
  10. Automated regime detection in multidimensional time series data using sliced Wasserstein k-means clustering By Qinmeng Luan; James Hamp
  11. Identification of systematic monetary policy By Hack, Lukas; Istrefi, Klodiana; Meier, Matthias
  12. Monetary policy shocks and exchange rate dynamics in small open economies By Madison Terrell; Qazi Haque; Jamie L. Cross; Firmin Doko Tchatoka

  1. By: Mirko Armillotta (Vrije Universiteit Amsterdam); Paolo Gorgi (Vrije Universiteit Amsterdam)
    Abstract: We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a parametric pseudo-variance that can contain parametric restrictions with respect to the conditional expectation. The specification of the pseudo-variance and the parametric restrictions follow naturally in observation-driven models with bounds in the support of the observable process, such as count processes and double-bounded time series. We derive the asymptotic properties of the estimators and a validity test for the parameter restrictions. We show that the results remain valid irrespective of the correct specification of the pseudo variance. The key advantage of the restricted estimators is that they can achieve higher efficiency compared to alternative quasi-likelihood methods that are available in the literature. Furthermore, the testing approach can be used to build specification tests for parametric time series models. We illustrate the practical use of the methodology in a simulation study and two empirical applications featuring integer-valued autoregressive processes, where assumptions on the dispersion of the thinning operator are formally tested, and autoregressions for double-bounded data with application to a realized correlation time series.
    Keywords: Double-bounded time series, integer-valued autoregressions, quasi-maximum likelihood.
    JEL: C32 C52 C58
    Date: 2023–10–12
  2. By: Michael Stanley Smith; Weichang Yu; David J. Nott; David Frazier
    Abstract: In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose conducting modified Bayesian inference by ``cutting feedback''. Cutting feedback limits the influence of potentially misspecified modules in posterior inference. We consider two types of cuts. The first limits the influence of a misspecified copula on inference for the marginals, which is a Bayesian analogue of the popular Inference for Margins (IFM) estimator. The second limits the influence of misspecified marginals on inference for the copula parameters by using a rank likelihood to define the cut model. We establish that if only one of the modules is misspecified, then the appropriate cut posterior gives accurate uncertainty quantification asymptotically for the parameters in the other module. Computation of the cut posteriors is difficult, and new variational inference methods to do so are proposed. The efficacy of the new methodology is demonstrated using both simulated data and a substantive multivariate time series application from macroeconomic forecasting. In the latter, cutting feedback from misspecified marginals to a 1096 dimension copula improves posterior inference and predictive accuracy greatly, compared to conventional Bayesian inference.
    Date: 2023–10
  3. By: Yu-Chin Hsu; Ji-Liang Shiu; Yuanyuan Wan
    Abstract: This paper derives testable implications of the identifying conditions for the local average treatment effect (LATE) in fuzzy regression discontinuity (FRD) designs. Building upon the seminal work of Horowitz and Manski (1995), we show that the testable implications of these identifying conditions are a finite number of inequality restrictions on the observed data distribution. We then propose a specification test for the testable implications and show that the proposed test controls the size and is asymptotically consistent. We apply our test to the FRD designs used in Miller, Pinto, and Vera-Hernandez (2013) for Columbia’s insurance subsidy program, in Angrist and Lavy (1999) for Israel’s class size effect, in Pop-Eleches and Urquiola (2013) for Romanian school effect, and in Battistin, Brugiavini, Rettore, and Weber (2009) for the retirement effect on consumption.
    Keywords: Fuzzy regression discontinuity design; Moment inequalities; Local continuity in means; Weighted bootstrap
    JEL: C12 C14 C15
    Date: 2023–10–19
  4. By: Francisco Blasques (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam); Gabriele Mingoli (Vrije Universiteit Amsterdam)
    Abstract: This paper proposes a novel time-series model with a non-stationary stochastic trend, locally explosive mixed causal non-causal dynamics and fat-tailed innovations. The model allows for a description of financial time-series that is consistent with financial theory, for a decomposition of the time-series in trend and bubble components, and for meaningful real-time forecasts during bubble episodes. We provide sufficient conditions for strong consistency and asymptotic normality of the maximum likelihood estimator. The model-based filter for extracting the trend and bubbles is shown to be invertible and the extracted components converge to the true trend and bubble paths. A Monte Carlo simulation study confirms the good finite sample properties. Finally, we consider an empirical study of Nickel monthly price series and global mean sea level data. We document the forecasting accuracy against competitive alternative methods and conclude that our model-based forecasts outperform all these alternatives.
    Keywords: observation-driven filter, non-stationary time-series, mixed causal non- causal models
    JEL: C22 C51 C53
    Date: 2023–10–12
  5. By: Jochmans, Koen
    Abstract: Judge-lenciency designs are very popular. Evaluating whether conventional inference procedures apply to it is not immediate. We frame such designs as an inference problem from grouped data in a setting with a growing number of groups and limited variation between groups. Such an asymptotic approximation is well suited for the data sets encountered in practice. The two-stage least-squares estimator should never be used. The jackknife instrumental-variable estimator can present a reliable tool for inference, provided that a non-standard asymptotic-variance estimator is used along with it. Conventional decision rules to gauge instrument strength are typically not valid in our setting. An alternative such decision rule is provided and is found to perform well.
    Keywords: bias; examiner design; fixed effects; inference; jackknife; weak instruments
    JEL: C23 C26
    Date: 2023–10–16
  6. By: Drin, Svitlana (Örebro University School of Business); Mazur, Stepan (Örebro University School of Business); Muhinyuza, Stanislas (School of Business and Economics Linnaeus University)
    Abstract: In this paper, we propose the test for the location of the tangency portfolio on the set of feasible portfolios when both the population and the sample covariance matrices of asset returns are singular. We derive the exact distribution of the test statistic under both the null and alternative hypotheses. Furthermore, we establish the high-dimensional asymptotic distribution of that test statistic when both the portfolio dimension and the sample size increase to infinity. We complement our theoretical findings by comparing the high-dimensional asymptotic test with an exact finite sample test in the numerical study. A good performance of the obtained results is documented.
    Keywords: Tangency portfolio; Hypothesis testing; Singular Wishart distribution; Singular covariance matrix; Moore-Penrose inverse; High-dimensional asymptotics.
    JEL: G11
    Date: 2023–10–18
  7. By: Dong, Hao (Southern Methodist University); Millimet, Daniel L. (Southern Methodist University)
    Abstract: In linear regression models, measurement error in a covariate causes Ordinary Least Squares (OLS) to be biased and inconsistent. Instrumental Variables (IV) is a common solution. While IV is also biased, it is consistent. Here, we undertake an asymptotic comparison of OLS and IV in the case where a covariate is mismeasured for [Nδ] of N observations with δ ∊ [0, 1]. We show that OLS is consistent for δ
    Keywords: errors-in-variables, measurement error, asymptotics
    JEL: C13 C26 C52
    Date: 2023–10
  8. By: Cho, Haeran; Fryzlewicz, Piotr
    Abstract: We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion-based model selection strategy termed gappy Schwarz algorithm. The former is well-suited to separating shifts in the mean from fluctuations due to serial correlations, while the latter simultaneously estimates the dependence structure and the number of change points without performing the difficult task of estimating the level of the noise as quantified e.g. by the long-run variance. We provide modular investigation into their theoretical properties and show that the combined methodology, named WCM.gSa, achieves consistency in estimating both the total number and the locations of the change points. The good performance of WCM.gSa is demonstrated via extensive simulation studies, and we further illustrate its usefulness by applying the methodology to London air quality data.
    Keywords: data segmentation; wild binary segmentation; information criterion; autoregressive time series; RPG-2019-390
    JEL: C1
    Date: 2023–09–18
  9. By: Stephane Bonhomme; Angela Denis
    Abstract: We propose a regression-based approach to estimate how individuals' expectations influence their responses to a counterfactual change. We provide conditions under which average partial effects based on regression estimates recover structural effects. We propose a practical three-step estimation method that relies on subjective beliefs data. We illustrate our approach in a model of consumption and saving, focusing on the impact of an income tax that not only changes current income but also affects beliefs about future income. By applying our approach to survey data from Italy, we find that considering individuals' beliefs matter for evaluating the impact of tax policies on consumption decisions.
    Date: 2023–10
  10. By: Qinmeng Luan; James Hamp
    Abstract: Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to identify regimes in time series data, and one-dimensional asset returns in particular. In this paper, we begin by studying in detail the behaviour of the Wasserstein k-means clustering algorithm applied to synthetic one-dimensional time series data. We study the dynamics of the algorithm and investigate how varying different hyperparameters impacts the performance of the clustering algorithm for different random initialisations. We compute simple metrics that we find are useful in identifying high-quality clusterings. Then, we extend the technique of Wasserstein k-means clustering to multidimensional time series data by approximating the multidimensional Wasserstein distance as a sliced Wasserstein distance, resulting in a method we call `sliced Wasserstein k-means (sWk-means) clustering'. We apply the sWk-means clustering method to the problem of automated regime detection in multidimensional time series data, using synthetic data to demonstrate the validity of the approach. Finally, we show that the sWk-means method is effective in identifying distinct market regimes in real multidimensional financial time series, using publicly available foreign exchange spot rate data as a case study. We conclude with remarks about some limitations of our approach and potential complementary or alternative approaches.
    Date: 2023–10
  11. By: Hack, Lukas; Istrefi, Klodiana; Meier, Matthias
    Abstract: We propose a novel identification design to estimate the causal effects of systematic monetary policy on the propagation of macroeconomic shocks. The design combines (i) a time-varying measure of systematic monetary policy based on the historical composition of hawks and doves in the Federal Open Market Committee (FOMC) with (ii) an instrument that leverages the mechanical FOMC rotation of voting rights. We apply our design to study the effects of government spending shocks. We find fiscal multipliers between two and three when the FOMC is dovish and below zero when it is hawkish. Narrative evidence from historical FOMC records corroborates our findings. JEL Classification: E32, E52, E62, E63, H56
    Keywords: FOMC, government spending, monetary policy, rotation
    Date: 2023–10
  12. By: Madison Terrell; Qazi Haque; Jamie L. Cross; Firmin Doko Tchatoka
    Abstract: This paper investigates the relationship between monetary policy shocks and real exchange rates in several small open economies. To that end, we develop a novel identification strategy for time-varying structural vector autoregressions with stochastic volatility. Our approach combines short-run and long-run restrictions to preserve the contemporaneous interaction between the interest rate and the exchange rate. Using this framework, we find that the volatility of monetary policy shocks has substantially decreased in all countries. This leads to a considerable reduction in the significance of policy shocks in explaining exchange rate and macroeconomic fluctuations since the 1990s. However, we find that the dynamic effects of the policy shocks have remained stable over time. Finally, while we do identify violations of uncovered interest parity (UIP) in some countries, we find no evidence of the ‘exchange rate puzzle’ or the ‘delayed overshooting puzzle’ in any country.
    Date: 2023–06

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