nep-ecm New Economics Papers
on Econometrics
Issue of 2023‒06‒12
ten papers chosen by
Sune Karlsson
Örebro universitet

  1. Indirect Inference and Small Sample Bias - Some Recent Results By Meenagh, David; Minford, Patrick; Xu, Yongdeng
  2. Optimal tests following sequential experiments By Karun Adusumilli
  3. Monitoring multicountry macroeconomic risk By Dimitris Korobilis; Maximilian Schröder
  4. Distribution regression with sample selection and UK wage decomposition By Victor Chernozhukov; Ivan Fernandez-Val; Siyi Luo
  5. FIEGARCH, modulus asymmetric FILog-GARCH and trend-stationary dual long memory time series By Yuanhua Feng; Thomas Gries; Sebastian Letmathe
  6. Simple graphical criteria for selection bias in general-population and selected-sample treatment effects By Mathur, Maya B; Shpitser, Ilya
  7. Estimation of Nonlinear Exchange Rate Dynamics in Evolving Regimes By Jeffrey Frankel
  8. Are the Effects of Uncertainty Shocks Big or Small? By Piergiorgio Alessandri; Andrea Gazzani; Alejandro Vicondoa
  9. The Barycenter of the Distribution and Its Application to the Measurement of Inequality: The Balance of Inequality, the Gini Index, and the Lorenz Curve By Giorgio Di Maio
  10. Calibration of Local Volatility Models with Stochastic Interest Rates using Optimal Transport By Gregoire Loeper; Jan Obloj; Benjamin Joseph

  1. By: Meenagh, David (Cardiff Business School); Minford, Patrick (Cardiff Business School); Xu, Yongdeng (Cardiff Business School)
    Abstract: Macroeconomic researchers use a variety of estimators to parameterise their models empirically. One such is FIML; another is a form of indirect inference we term “informal”under which data features are “targeted”by the model -i.e. parameters are chosen so that model-simulated features replicate the data features closely. In this paper we show, based on Monte Carlo experiments, that in the small samples prevalent in macro data, both these methods produce high bias, while formal indirect inference, in which the joint probability of the data- generated auxiliary model is maximised under the model simulated distribution, produces low bias. We also show that FII gets this low bias from its high power in rejecting misspecified models, which comes in turn from the fact that this distribution is restricted by the modelspeciÖed parameters, so sharply distinguishing it from rival misspecified models.
    Keywords: Moments, Indirect Inference
    JEL: C12 C32 C52
    Date: 2023–05
  2. By: Karun Adusumilli
    Abstract: Recent years have seen tremendous advances in the theory and application of sequential experiments. While these experiments are not always designed with hypothesis testing in mind, researchers may still be interested in performing tests after the experiment is completed. The purpose of this paper is to aid in the development of optimal tests for sequential experiments by analyzing their asymptotic properties. Our key finding is that the asymptotic power function of any test can be matched by a test in a limit experiment where a Gaussian process is observed for each treatment, and inference is made for the drifts of these processes. This result has important implications, including a powerful sufficiency result: any candidate test only needs to rely on a fixed set of statistics, regardless of the type of sequential experiment. These statistics are the number of times each treatment has been sampled by the end of the experiment, along with final value of the score (for parametric models) or efficient influence function (for non-parametric models) process for each treatment. We then characterize asymptotically optimal tests under various restrictions such as unbiasedness, \alpha-spending constraints etc. Finally, we apply our our results to three key classes of sequential experiments: costly sampling, group sequential trials, and bandit experiments, and show how optimal inference can be conducted in these scenarios.
    Date: 2023–04
  3. By: Dimitris Korobilis (University of Glasgow, UK; Rimini Centre for Economic Analysis); Maximilian Schröder (BI Norwegian Business School, Norway; Norges Bank, Norway)
    Abstract: We propose a multicountry quantile factor augmented vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the effects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implement joint risk scenario analysis.
    Keywords: quantile VAR, MCMC, variational Bayes, dynamic factor model
    JEL: C11 C32 E31 E32 E37 E66
    Date: 2023–05
  4. By: Victor Chernozhukov; Ivan Fernandez-Val; Siyi Luo
    Abstract: We develop a distribution regression model under endogenous sample selection. This model is a semi-parametric generalization of the Heckman selection model. It accommodates much richer effects of the covariates on outcome distribution and patterns of heterogeneity in the selection process, and allows for drastic departures from the Gaussian error structure, while maintaining the same level tractability as the classical model. The model applies to continuous, discrete and mixed outcomes. We provide identification, estimation, and inference methods, and apply them to obtain wage decomposition for the UK. Here we decompose the difference between the male and female wage distributions into composition, wage structure, selection structure, and selection sorting effects. After controlling for endogenous employment selection, we still find substantial gender wage gap – ranging from 21% to 40% throughout the (latent) offered wage distribution that is not explained by composition. We also uncover positive sorting for single men and negative sorting for married women that accounts for a substantive fraction of the gender wage gap at the top of the distribution.
    Date: 2023–04–26
  5. By: Yuanhua Feng (Paderborn University); Thomas Gries (Paderborn University); Sebastian Letmathe (Paderborn University)
    Abstract: A novel long memory volatility model MAFILog-GARCH (modulus asymmetric FILog-GARCH) is introduced, which has some advantages compared to the FIEGARCH. A general dual long memory FARIMA with them as error processes is defined. Moreover, a trend-stationary dual long memory model is proposed. The FIEGARCH and MAFILog-GARCH are first applied to returns of eight top US firms. It is found that their practical performances are comparable. Both are superior to the FIGARCH and FILog-GARCH. Further application provides evidence of trend-stationary dual long memory time series in different fields.
    Keywords: Modulus asymmetric FILog-GARCH, FIEGARCH, dual long memory, trend-stationary dual long memory, implementation in R
    Date: 2023–05
  6. By: Mathur, Maya B; Shpitser, Ilya
    Abstract: When analyzing a non-representative sample selected from a general population, estimated average treatment effects (ATEs) can be biased relative to not only the causal ATE for the general population, but also relative to the ATE for the selected sample. When the treatment could affect selection, different individuals comprise the selected sample when the treatment is hypothetically set to different levels. Thus, defining estimands of interest in the selected sample and establishing identification criteria has been challenging. We consider ATEs in the general population and the selected sample as well the net treatment difference, which compares average potential outcomes between the counterfactual selected samples in a world with all members of the general population treated versus a world with no members treated. We provide graphical criteria for each estimand to be nonparametrically identified, which are easily assessed using a standard single-world intervention graph. Others decomposed bias relative to the general-population ATE into: (1) bias in the ATE for the (factual) selected sample; and (2) bias due to effect heterogeneity by selection status. We provide an alternative two-way decomposition using the net treatment difference, allowing each source of bias to be assessed unambiguously in a graph, even when the treatment affects selection.
    Date: 2023–04–24
  7. By: Jeffrey Frankel
    Abstract: This paper develops a new econometric framework to estimate and classify exchange rate regimes. They are classified into four distinct categories: fixed exchange rates, BBC (band, basket and crawl), managed floating, and freely floating. The procedure captures the patterns of exchange rate dynamics and the interventions by authorities under each of the regimes. We pay particular attention to the BBC and offer a new approach to parameter estimation by utilizing a three-regime Threshold Auto Regressive (TAR) model to reveal the nonlinear nature of exchange rate dynamics. We further extend our benchmark framework to allow the evolution of exchange rate regimes over time by adopting the minimum description length (MDL) principle, to overcome the challenge of simultaneous two-dimensional inference of nonlinearity in the state dimension and structural breaks in the time dimension. We apply our framework to 26 countries. The results suggest that exchange rate dynamics under different regimes are well captured by our new framework.
    Keywords: Exchange rate regime, MDL, Minimum Description Length, structural breaks, TAR, Threshold Autoregression
    JEL: F33
    Date: 2023–03
  8. By: Piergiorgio Alessandri (Bank of Italy); Andrea Gazzani (Bank of Italy); Alejandro Vicondoa (Pontificia Universidad Católica de Chile)
    Abstract: Previous works have reached widely divergent conclusions on the macroeconomic relevance of uncertainty shocks. We show that this disagreement reflects identification problems linked to the use of financial data in low-frequency VAR models. To bypass this difficulty, we identify uncertainty shocks using daily data and use their monthly averages as instruments in VARs. This novel identification approach captures within-month interactions between uncertainty and asset prices, providing a full picture of the pivotal role of financial markets in propagating uncertainty to the real economy. Once these interactions are accounted for, thedisagreement disappears: uncertainty shocks have a small but significant impact on economic activity across specifications and identification schemes.
    Keywords: uncertainty shocks; financial shocks; structural vector autoregression; high-frequency identification; external instruments
    JEL: E32 C32 C36
    Date: 2023–05
  9. By: Giorgio Di Maio
    Abstract: This paper introduces in statistics the notion of the barycenter of the distribution of a non-negative random variable Y with a positive finite mean μY and the quantile function Q(x). The barycenter is denoted by μX and defined as the expected value of the random variable X having the probability density function fX(x) = Q(x)/μY. For continuous populations, the Gini index is 2μX − 1, i.e., the normalization of the barycenter, which is in the range [0, 1/2], the concentration area is μX − 1/2, and the Gini’s mean difference is 4μY (μX − 1/2). The same barycenter-based formulae hold for normalized discrete populations. The introduction of the barycenter allows for new economic, geometrical, physical, and statistical interpretations of these measures. For income distributions, the barycenter represents the expected recipient of one unit of income, as if the stochastic process that leads to the distribution of the total income among the population was observable as it unfolds. The barycenter splits the population into two groups, which can be considered as “the winners” and “the losers” in the income distribution, or “the rich” and “the poor”. We provide examples of application to thirty theoretical distributions and an empirical application with the estimation of personal income inequality in Luxembourg Income Study Database’s countries. We conclude that the barycenter is a new measure of the location or central tendency of distributions, which may have wide applications in both economics and statistics.
    Date: 2022–03
  10. By: Gregoire Loeper; Jan Obloj; Benjamin Joseph
    Abstract: We develop a non-parametric, optimal transport driven, calibration methodology for local volatility models with stochastic interest rate. The method finds a fully calibrated model which is the closest to a given reference model. We establish a general duality result which allows to solve the problem via optimising over solutions to a non-linear HJB equation. We then apply the method to a sequential calibration setup: we assume that an interest rate model is given and is calibrated to the observed term structure in the market. We then seek to calibrate a stock price local volatility model with volatility coefficient depending on time, the underlying and the short rate process, and driven by a Brownian motion which can be correlated with the randomness driving the rates process. The local volatility model is calibrated to a finite number of European options prices via a convex optimisation problem derived from the PDE formulation of semimartingale optimal transport. Our methodology is analogous to Guo, Loeper, and Wang, 2022 and Guo, Loeper, Obloj, et al., 2022a but features a novel element of solving for discounted densities, or sub-probability measures. We present numerical experiments and test the effectiveness of the proposed methodology.
    Date: 2023–04

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