nep-ecm New Economics Papers
on Econometrics
Issue of 2020‒06‒22
fourteen papers chosen by
Sune Karlsson
Örebro universitet

  1. Bootstrap Procedures for Detecting Multiple Persistence Shifts in Heteroskedastic Time Series By Mohitosh Kejriwal; Xuewen Yu; Pierre Perron
  2. Trigonometric Trend Regressions of Unknown Frequencies with Stationary or Integrated Noise By Pierre Perron; Mototsugu Shintaniz; Tomoyoshi Yabu
  3. A Negative Correlation Strategy for Bracketing in Difference-in-Differences with Application to the Effect of Voter Identification Laws on Voter Turnout By Ting Ye; Luke Keele; Raiden Hasegawa; Dylan S. Small
  4. New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence By Rustam Ibragimov; Rasmus Pedersen; Anton Skrobotov
  5. Continuous Record Asymptotics for Change-Point Models By Alessandro Casini; Pierre Perron
  6. Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter By Sander Barendse; Andrew J. Patton
  7. A Two Step Procedure for Testing Partial Parameter Stability in Cointegrated Regression Models By Mohitosh Kejriwal; Pierre Perron; Xuewen Yu
  8. On Quadratic Forms in Multivariate Generalized Hyperbolic Random Vectors∗ By S. Broda; Juan Carlos Arismendi-Zambrano
  9. The probability of a robust inference for internal validity and its applications in regression models By Tenglong Li; Kenneth A. Frank
  10. An alternative to synthetic control for models with many covariates under sparsity By Marianne Bl\'ehaut; Xavier D'Haultfoeuille; J\'er\'emy L'Hour; Alexandre B. Tsybakov
  11. Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN By Calypso Herrera; Florian Krach; Josef Teichmann
  12. Doubly Multiplicative Error Models with Long- and Short-run Components By Alessandra Amendola; Vincenzo Candila; Fabrizio Cipollini; Giampiero M. Gallo
  13. Financial frictions and the wealth distribution By Jesús Fernández-Villaverde; Samuel Hurtado; Galo Nuño
  14. Econometric Methods and Data Science Techniques: A Review of Two Strands of Literature and an Introduction to Hybrid Methods By Xie, Tian; Yu, Jun; Zeng, Tao

  1. By: Mohitosh Kejriwal (Purdue University); Xuewen Yu (Purdue University); Pierre Perron (Boston University)
    Abstract: This paper proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by nonstationary volatility. The assumed volatility process can accommodate discrete breaks, smooth transition variation as well as trending volatility. We develop wild bootstrap sup-Wald tests of the null hypothesis that the process is either stationary [I(0)] or has a unit root [I(1)] throughout the sample. We also propose a sequential procedure to estimate the number of persistence breaks based on ordering the regime-speciÖc bootstrap p-values. The asymptotic validity of the advocated procedures is established both under the null of stability and a variety of persistence change alternatives. A comparison with existing tests that assume homoskedasticity illustrates the finite sample improvements offered by our methods. An application to OECD ináation rates highlights the empirical relevance of the proposed approach and weakens the case for persistence change relative to existing procedures.
    Keywords: heteroskedasticity, multiple structural changes, sequential procedure, unit root, Wald tests, wild bootstrap
    JEL: C22
    Date: 2020–03
  2. By: Pierre Perron (Boston University); Mototsugu Shintaniz (The University of Tokyo); Tomoyoshi Yabu (Keio University)
    Abstract: We propose a new procedure to select the unknown frequencies of a trigonometric function, a problem Örst investigated by Anderson (1971) under the assumption of serially uncorrelated noise. We extend the analysis to general linear processes without the prior knowledge of a stationary or integrated model allowing the frequencies to be unknown. We provide a consistent model selection procedure. We Örst show that if we estimate a model with fewer frequencies than those in the correct model, the estimates converge to a subset of the frequencies in the correct model. This opens the way to a consistent model selection strategy based on a speciÖc to general procedure that tests whether additional frequencies are needed. This is achieved using tests based on the feasible ísuper e¢ cientî(under unit root noise) Generalized Least Squares estimator of Perron, Shintani and Yabu (2017) who assumed the frequencies to be known. We show that the limiting distributions of our test statistics are the same for both cases about the noise function. Simulation results conÖrm that our frequency selection procedure works well with sample sizes typically available in practice. We illustrate the usefulness of our method via applications to unemployment rates and global temperature series.
    Keywords: Cyclical trends, median-unbiased estimator, nonlinear trends, supere¢ cient estimator, unit root
    JEL: C22
    Date: 2020–01
  3. By: Ting Ye; Luke Keele; Raiden Hasegawa; Dylan S. Small
    Abstract: The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID exploits a before and after comparison of the treated and control units to remove the bias due to time-invariant unmeasured confounders under the parallel trends assumption. Estimates from DID, however, will be biased if the outcomes for the treated and control units evolve differently in the absence of treatment, namely if the parallel trends assumption is violated. We propose a general identification strategy that leverages two groups of control units whose outcomes relative to the treated units exhibit a negative correlation, and achieves partial identification of the average treatment effect for the treated. The identified set is of a union bounds form that previously developed partial identification inference methods do not apply to. We develop a novel bootstrap method to construct valid confidence intervals for the identified set and parameter of interest when the identified set is of a union bounds form, and we establish the theoretical properties. We develop a simple falsification test and sensitivity analysis. We apply the proposed strategy for bracketing to an application on the effect of voter identification laws in Georgia and Indiana on turnout and find evidence that the laws increased turnout rates.
    Date: 2020–06
  4. By: Rustam Ibragimov; Rasmus Pedersen; Anton Skrobotov
    Abstract: Many key variables in finance, economics and risk management, including financial returns and foreign exchange rates, exhibit nonlinear dependence, heterogeneity and heavy-tailedness of some usually largely unknown type. The presence of non-linear dependence (usually modelled using GARCH-type dynamics) and heavy-tailedness may make problematic the analysis of (non-)efficiency, volatility clustering and predictive regressions in economic and financial markets using traditional approaches that appeal to asymptotic normality of sample autocorrelation functions (ACFs) of returns and their squares. The paper presents several new approaches to deal with the above problems. We provide the results that motivate the use of measures of market (non-)efficiency, volatility clustering and nonlinear dependence based on (small) powers of absolute returns and their signed versions. The paper provides asymptotic theory for sample analogues of the above measures in the case of general time series, including GARCH-type processes. It further develops new approaches to robust inference on them in the case of general GARCH-type processes exhibiting heavy-tailedness properties. The approaches are based on robust inference methods exploiting conservativeness properties of t-statistics Ibragimov and Muller (2010,2016) and several new results on their applicability in the settings considered. In the approaches, estimates of parameters of interest are computed for groups of data and the inference is based on t-statistics in resulting group estimates. This results in valid robust inference under a wide range of heterogeneity and dependence assumptions satisfied in financial and economic markets. Numerical results and empirical applications confirm advantages of the new approaches over existing ones and their wide applicability.
    Date: 2020–06
  5. By: Alessandro Casini (University of Rome Tor Vergata); Pierre Perron (Boston University)
    Abstract: For a partial structural change in a linear regression model with a single break, we develop a continuous record asymptotic framework to build inference methods for the break date. We have T observations with a sampling frequency h over a fixed time horizon [0, N] , and let T → ∞ with h ↓ 0 while keeping the time span N fixed. We impose very mild regularity conditions on an underlying continuous-time model assumed to generate the data. We consider the least-squares estimate of the break date and establish consistency and convergence rate. We provide a limit theory for shrinking magnitudes of shifts and locally increasing variances. The asymptotic distribution corresponds to the location of the extremum of a function of the quadratic variation of the regressors and of a Gaussian centered martingale process over a certain time interval. We can account for the asymmetric informational content provided by the pre- and post-break regimes and show how the location of the break and shift magnitude are key ingredients in shaping the distribution. We consider a feasible version based on plug-in estimates, which provides a very good approximation to the finite sample distribution. We use the concept of Highest Density Region to construct confidence sets. Overall, our method is reliable and delivers accurate coverage probabilities and relatively short average length of the confidence sets. Importantly, it does so irrespective of the size of the break.
    Keywords: Asymptotic distribution, break date, change-point, highest density region, semimartingale
    JEL: C10 C12 C22
    Date: 2020–03
  6. By: Sander Barendse; Andrew J. Patton
    Abstract: We develop tests for out-of-sample forecast comparisons based on loss functions that contain shape parameters. Examples include comparisons using average utility across a range of values for the level of risk aversion, comparisons of forecast accuracy using characteristics of a portfolio return across a range of values for the portfolio weight vector, and comparisons using a recently-proposed “Murphy diagrams†for classes of consistent scoring rules. An extensive Monte Carlo study verifies that our tests have good size and power properties in realistic sample sizes, particularly when compared with existing methods which break down when then number of values considered for the shape parameter grows. We present three empirical illustrations of the new test.
    Keywords: Forecasting, model selection, out-of-sample testing, nuisance parameters
    JEL: C53 C52 C12
    Date: 2020–05–27
  7. By: Mohitosh Kejriwal (Purdue University); Pierre Perron (Boston University); Xuewen Yu (Purdue University)
    Abstract: Kejriwal and Perron (2010, KP) provided a comprehensive treatment for the problem of testing multiple structural changes in cointegrated regression models. A variety of models were considered depending on whether all regression coefficients are allowed to change (pure structural change) or a subset of the coefficients is held Öxed (partial structural change). In this note, we Örst show that the limit distributions of the test statistics in the latter case are not invariant to changes in the coe¢ cients not being tested; in fact, they diverge as the sample size increases. To address this issue, we propose a simple two step procedure to test for partial parameter stability. The Örst entails the application of a joint test of stability for all coe¢ cients as in KP. Upon a rejection, the second conducts a stability test on the subset of coe¢ cients of interest while allowing the other coe¢ cients to change at the estimated breakpoints. Its limit distribution is standard chi-square. The relevant asymptotic theory is provided along with simulations that illustrates the usefulness of the procedure in finite samples.
    Keywords: cointegration, partial structural change, break date, sup-Wald tests, joint hypothesis testing
    JEL: C22
    Date: 2020–02
  8. By: S. Broda (Department of Economics and Econometrics, University of Amsterdam); Juan Carlos Arismendi-Zambrano (Department of Economics, Finance and Accounting, Maynooth University & ICMA Centre, Henley Business School, University of Reading)
    Abstract: Countless test statistics can be written as quadratic forms in certain random vectors, or ratios thereof. Consequently, their distribution has received considerable attention in the literature. Except for a few special cases, no closed-form expression for the cdf exists, and one resorts to numerical methods. Traditionally the problem is analyzed under the assumption of joint Gaussianity; the algorithm that is usually employed is that of Imhof (1961). The present manuscript generalizes this result to the case of multivariate generalized hyperbolic random vectors. This flexible distribution nests, among others, the multivariate t, Laplace, and variance gamma distributions. An expression for the first partial moment is also obtained, which plays a vital role in financial risk management. The proof involves a generalization of the classic inversion formula due to Gil-Pelaez (1951). Two numerical applications are considered: first, the finite-sample distribution of the two stage least squares estimator of a structural parameter. Second, the Value at Risk and expected shortfall of a quadratic portfolio with heavy-tailed risk factors. An empirical application is examined, in which a portfolio of Dow Jones Industrial Index stock options is optimized with respect to its expected shortfall. The results demonstrate the benefits of the analytical expression.
    Keywords: Characteristic Function; Conditional Value at Risk; Expected Shortfall; Transform Inver-sion; Two Stage Least Squares.
    JEL: C10 C13 C14 C15 C18 C63 C65 G32
    Date: 2020
  9. By: Tenglong Li; Kenneth A. Frank
    Abstract: The internal validity of observational study is often subject to debate. In this study, we define the unobserved sample based on the counterfactuals and formalize its relationship with the null hypothesis statistical testing (NHST) for regression models. The probability of a robust inference for internal validity, i.e., the PIV, is the probability of rejecting the null hypothesis again based on the ideal sample which is defined as the combination of the observed and unobserved samples, provided the same null hypothesis has already been rejected for the observed sample. When the unconfoundedness assumption is dubious, one can bound the PIV of an inference based on bounded belief about the mean counterfactual outcomes, which is often needed in this case. Essentially, the PIV is statistical power of the NHST that is thought to be built on the ideal sample. We summarize the process of evaluating internal validity with the PIV into a six-step procedure and illustrate it with an empirical example (i.e., Hong and Raudenbush (2005)).
    Date: 2020–05
  10. By: Marianne Bl\'ehaut; Xavier D'Haultfoeuille; J\'er\'emy L'Hour; Alexandre B. Tsybakov
    Abstract: The synthetic control method is a an econometric tool to evaluate causal effects when only one unit is treated. While initially aimed at evaluating the effect of large-scale macroeconomic changes with very few available control units, it has increasingly been used in place of more well-known microeconometric tools in a broad range of applications, but its properties in this context are unknown. This paper introduces an alternative to the synthetic control method, which is developed both in the usual asymptotic framework and in the high-dimensional scenario. We propose an estimator of average treatment effect that is doubly robust, consistent and asymptotically normal. It is also immunized against first-step selection mistakes. We illustrate these properties using Monte Carlo simulations and applications to both standard and potentially high-dimensional settings, and offer a comparison with the synthetic control method.
    Date: 2020–05
  11. By: Calypso Herrera; Florian Krach; Josef Teichmann
    Abstract: Continuous stochastic processes are widely used to model time series that exhibit a random behaviour. Predictions of the stochastic process can be computed by the conditional expectation given the current information. To this end, we introduce the controlled ODE-RNN that provides a data-driven approach to learn the conditional expectation of a stochastic process. Our approach extends the ODE-RNN framework which models the latent state of a recurrent neural network (RNN) between two observations with a neural ordinary differential equation (neural ODE). We show that controlled ODEs provide a general framework which can in particular describe the ODE-RNN, combining in a single equation the continuous neural ODE part with the jumps introduced by RNN. We demonstrate the predictive capabilities of this model by proving that, under some regularities assumptions, the output process converges to the conditional expectation process.
    Date: 2020–06
  12. By: Alessandra Amendola; Vincenzo Candila; Fabrizio Cipollini; Giampiero M. Gallo
    Abstract: We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating low-, respectively, high-frequency features in the data. We derive the theoretical properties of the Maximum Likelihood and Generalized Method of Moments estimators. Two such models are then proposed, the Component-MEM, which uses daily data for both components, and the MEM-MIDAS, which exploits the logic of MIxed-DAta Sampling (MIDAS). The empirical application involves the S&P 500, NASDAQ, FTSE 100 and Hang Seng indices: irrespective of the market, both DMEM's outperform the HAR and other relevant GARCH-type models.
    Date: 2020–06
  13. By: Jesús Fernández-Villaverde (University of Pennsylvania, NBER and CEPR); Samuel Hurtado (Banco de España); Galo Nuño (Banco de España)
    Abstract: We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an endogenous regime-switching process for output, the risk-free rate, excess returns, debt, and leverage. The regime-switching generates i) multimodal distributions of the variables above; ii) time-varying levels of volatility and skewness for the same variables; and iii) supercycles of borrowing and deleveraging. All of these are important properties of the data. In comparison, the representative household version of the model cannot generate any of these features. Methodologically, we discuss how nonlinear DSGE models with heterogeneous agents can be efficiently computed using machine learning and how they can be estimated with a likelihood function, using inference with diffusions.
    Keywords: heterogeneous agents, wealth distribution, financial frictions, continuoustime, machine learning, neural networks, structural estimation, likelihood function
    JEL: C45 C63 E32 E44 G01 G11
    Date: 2020–06
  14. By: Xie, Tian (Shanghai University of Finance and Economics); Yu, Jun (School of Economics, Singapore Management University); Zeng, Tao (Zhejiang University)
    Abstract: The data market has been growing at an exceptional pace. Consequently, more sophisticated strategies to conduct economic forecasts have been introduced with machine learning techniques. Does machine learning pose a threat to conventional econometric methods in terms of forecasting? Moreover, does machine learning present great opportunities to cross-fertilize the field of econometric forecasting? In this report, we develop a pedagogical framework that identifies complementarity and bridges between the two strands of literature. Existing econometric methods and machine learning techniques for economic forecasting are reviewed and compared. The advantages and disadvantages of these two classes of methods are discussed. A class of hybrid methods that combine conventional econometrics and machine learning are introduced. New directions for integrating the above two are suggested. The out-of-sample performance of alternatives is compared when they are employed to forecast the Chicago Board Options Exchange Volatility Index and the harmonized index of consumer prices for the euro area. In the first exercise, econometric methods seem to work better, whereas machine learning methods generally dominate in the second empirical application.
    Date: 2020–05–30

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