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In statistics, samples are drawn from a population in a data- generating process (DGP). Standard errors measure the uncer- tainty in sample estimates of population parameters. In sci- ence, evidence is generated to test hypotheses in an evidence- generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sam- ple. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

We consider models defined by a set of moment restrictions that may be subject to weak identification. We propose a testing procedure to assess whether instruments are ”too weak” for standard (Gaussian) asymptotic theory to be reliable. Since the validity of standard asymptotics for GMM rests upon a Taylor expansion of the first order conditions, we distinguish two cases: (i) models that are either linear or separable in the parameters of interest; (ii) general models that are neither linear nor separable. Our testing procedure is similar in both cases, but our null hypothesis of weak identification for a nonlinear model is broader than the popular one. Our test is straightforward to apply and allows to test the null hypothesis of weak identification of specific subvectors without assuming identification of the components not under test. In the linear case, it can be seen as a generalization of the popular first-stage F-test but allows us to fix its shortcomings in case of heteroskedasticity. In simulations, our test is well behaved when compared to contenders, both in terms of size and power. In particular, the focus on subvectors allows us to have power to reject the null of weak identification on some components of interest. This observation may explain why, when applied to the estimation of the Elasticity of Intertemporal Substitution, our test is the only one to find matching results for every country under the two symmetric popular specifications: the intercept parameter is always found strongly identified, whereas the slope parameter is always found weakly identified.\ud

A test is derived for short-memory correlation in the conditional variance of strictly positive, skewed data. The test is quasi-locally most powerful (QLMP) under the assumption of conditionally gamma data. Analytical asymptotic relative efficiency calculations show that an alternative test, based on the first-order autocorrelation coefficient of the squared data, has negligible relative power to detect correlation in the conditional variance. Finite sample simulation results con.rm the poor performance of the squares-based test for fixed alternatives, as well as demonstrating the poor performance of the test based on the first-order autocorrelation coefficient of the raw (levels) data. Robustness of the QLMP test, both to misspecification of the conditional distribution and misspecification of the dynamics is also demonstrated using simulation. The test is illustrated using financial trade durations data.

This paper proposes a new test for simultaneous intraday jumps in a panel of high frequency financial data. We utilize intraday first-high-low-last values of asset prices to construct estimates for the cross-variation of returns in a large panel of high frequency financial data, and then employ these estimates to provide a first-high-low-last price based test statistic to detect common large discrete movements (co-jumps). We study the finite sample behavior of our first-high-low-last price based test using Monte Carlo simulation, and find that it is more powerful than the Bollerslev et al (2008) return-based co-jump test. When applied to a panel of high frequency data from the Chinese mainland stock market, our first-high-low-last price based test identifies more common jumps than the return-based test in this emerging market.

This paper considers a general model specification test for nonlinear multivariate cointegrating regressions where the regressor consists of a univariate integrated time series and a vector of stationary time series. The regressors and the errors are generated from the same innovations, so that the model accommodates endogeneity. A new and simple test is proposed, and the resulting asymptotic theory is established. The test statistic is constructed based on a natural distance function between a nonparametric estimate and a smoothed parametric counterpart. The asymptotic distribution of the test statistic under the parametric specification is proportional to that of a local-time random variable with a known distribution. In addition, the finite sample performance of the proposed test is evaluated using both simulated and real data examples.

In the absence of uniformly most powerful (UMP) tests or uniformly most powerful invariant (UMPI) TESTS, King (1987c) suggested the use of Point Optimal (PO) tests, which are most powerful at a chosen point under the alternative hypothesis. This paper surveys the literature and major developments on point optimal testing since 1987 and suggests some areas for future research. Topics include tests for which all nuisance parameters have been eliminated and dealing with nuisance parameters via (i) a weighted average of p values, (ii) approximate point optimal tests, (iii) plugging in estimated parameter values, (iv) using asymptotics and (v) integration. Progress on using point-optimal testing principles for two-sided testing and multi-dimensional alternatives is also reviewed. The paper concludes with thoughts on how best to deal with nuisance parameters under both the null and alternative hypotheses, as well as the development of a new class of point optimal test for multi-dimensional testing.

Statistical models can play a crucial role in decision making. Traditional model validation tests typically make restrictive parametric assumptions about the model under the null and the alternative hypotheses. The majority of these tests examine one type of change at a time. This paper presents a method for determining whether new data continues to support the chosen model. We suggest using simulation and the kernel density estimator instead of assuming a parametric distribution for the data under the hull hypothesis. This leads to a more versatile testing procedure, one that can be applied to test different types of models and look for a variety of different types of divergences from the null hypothesis. Such a flexible testing procedure, in some cases, can also replace a range of tests that each test against particular alternative hypotheses. The procedure’s ability to recognize a change in the underlying model is demonstrated through AR(1) and linear models. We examine the power of our procedure to detect changes in the variance of the error term and the AR coefficient in the AR(1) model. In the linear model, we examine the performance of the procedure when there are changes in the error variance and error distribution, and when an economic cycle is introduced into the model. We find that the procedure has correct empirical size and high power to recognize the changes in the data generating process after 10 to 15 new observations, depending on the type and extent of the change.

Estimation of unknown parameters and functions involved in complex nonlinear econometric models is a very important issue. Existing estimation methods include generalised method of moments (GMM) by Hansen (1982) and others, efficient method of moments (EMM) by Gallant and Tauchen (1997), Markov chain Monte Carlo (MCMC) method by Chernozhukov and Hong (2003), and nonparametric simulated maximum likelihood estimation (NSMLE) method by Creel and Kristensen (2011), and Kristensen and Shin (2012). Except the NSMLE method, other existing methods do not provide closed-form solutions. This paper proposes non- and semi-parametric based closed-form approximations to the estimation and computation of posterior means involved in complex nonlinear econometric models. We first consider the case where the samples can be independently drawn from both the likelihood function and the prior density. The samples and observations are then used to nonparametrically estimate posterior mean functions. The estimation method is also applied to estimate the posterior mean of the parameter-of-interest on a summary statistic. Both the asymptotic theory and the finite sample study show that the nonparametric estimate of this posterior mean is superior to existing estimates, including the conventional sample mean.

In this paper we propose a new methodology for selecting the window length in Singular Spectral Analysis in which the window length is determined from the data prior to the commencement of modeling. The selection procedure is based on statistical tests designed to test the convergence of the autocovariance function. A classical time series portmanteau type statistic and two test statistics derived using a conditional moment principle are considered. The first two are applicable to short-memory processes, and the third is applicable to both short- and long-memory processes. We derive the asymptotic distribution of the statistics under fairly general regularity conditions and show that the criteria will identify true convergence with a finite window length with probability one as the sample size increases. Results obtained using Monte-Carlo simulation indicate the relevance of the asymptotic theory, even in relatively small samples, and that the conditional moment tests will choose a window length consistent with the Whitney embedding theorem. Application to observations on the Southern Oscillation Index shows how observed experimental behaviour can be reflected in features seen with real world data sets.

This paper investigates empirically the Balassa-Samuelson hypothesis (BSH) using annual data over 1970-2008 from 33 countries grouped into developed and developing countries. The innovative feature of our study is that we introduce a new approach for classifying traded and nontraded industries. Our proposed approach allows for country specific heterogeneity over each industry, and changes in classifications of industries across periods. We apply panel cointegration tests with group-mean panel dynamic ordinary least squares (DOLS) estimators suggested in Pedroni (2000, 2001) to examine the BSH. We also use persistence profiles in order to investigate the speed of adjustment to equilibrium in response to shocks on cointegrating relations, and employ them as a complementary tool for checking whether the long run relationships obtained from the tests are actually valid. Our main finding is that there is not enough evidence in favour of the BSH nor in favour of purchasing power parity (PPP) in either developed or developing countries. In developed countries, there is strong evidence that higher growth countries will experience real exchange rate appreciation. However, this relation does not provide evidence in favour of the BSH. We actually find opposing evidence that as productivity growth in traded goods relative to that in nontraded goods increases, the real exchange rated tends to depreciate. For developing countries, our results support the BSH. However, the speed of reversion to equilibrium is very slow. We also find moderate evidence that comparatively rapidly growing developing countries will experience real exchange rate depreciation.

We test for bias and efficiency of the ECB inflation forecasts using a confidential dataset of ECB macroeconomic quarterly projections. We investigate whether the properties of the forecasts depend on the level of inflation, by distinguishing whether the inflation observed by the ECB at the time of forecasting is above or below the target. The forecasts are unbiased and efficient on average, however there is evidence of state dependence. In particular, the ECB tends to overpredict (underpredict) inflation at intermediate forecast horizons when inflation is below (above) target. The magnitude of the bias is larger when inflation is above the target. These results hold even after accounting for errors in the external assumptions. We also find evidence of inefficiency, in the form of underreaction to news, but only when inflation is above the target. Our findings bear important implications for the ECB forecasting process and ultimately for its communication strategy.

Time invariance of factor loadings is a standard assumption in the analysis of large factor models. Yet, this assumption may be restrictive unless parameter shifts are mild (i.e., local to zero). In this paper we develop a new testing procedure to detect big breaks in these loadings at either known or unknown dates. It relies upon testing for parameter breaks in a regression of one of the factors estimated by Principal Components analysis on the remaining estimated factors, where the number of factors is chosen according to Bai and Ng’s (2002) information criteria. The test fares well in terms of power relative to other recently proposed tests on this issue, and can be easily implemented to avoid forecasting failures in standard factor-augmented (FAR, FAVAR) models where the number of factors is a priori imposed on the basis of theoretical considerations.

This paper introduces a class of cointegration tests based on estimated low-pass and high-pass regression coefficients from the same wavelet transform of the original time series data. The procedure can be applied to test the null of cointegration in a n + k multivariate system with n cointegrating relationships without the need of either detrending nor differencing. The proposed non residual-based wavelet statistics are asymptotically distributed as standard chi-square with nk degrees of freedom regardless of deterministic terms or dynamic regressors, thus offering a simple way of testing for cointegration under the null without the need of special tables. Small sample quantiles for these wavelet statistics are obtained using Monte Carlo simulation in different situations including I(1) and higher order cointegration cases and it is shown that these wavelet tests exhibit appropriate size and good power when compared to other tests of the null of cointegration.

This paper provides a multivariate score-type test to distinguish between true and spurious long memory. The test is based on the weighted sum of the partial derivatives of the multivariate local Whittle likelihood function. This approach takes phase shifts in the multivariate spectrum into account. The resulting pivotal limiting distribution is independent of the dimension of the process, which makes it easy to apply in practice. We prove the consistency of our test against the alternative of random level shifts or monotonic trends. A Monte Carlo analysis shows good finite sample properties of the test in terms of size and power. Additionally, we apply our test to the log-absolute returns of the S\&P 500, DAX, FTSE, and the NIKKEI. The multivariate test gives formal evidence that these series are contaminated by level shifts.

The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained for a wide class of asymmetric GARCH models with exogenous covariates. The true value of the parameter is not restricted to belong to the interior of the parameter space, which allows us to derive tests for the significance of the parameters. In particular, the relevance of the exogenous variables can be assessed. The results are obtained without assuming that the innovations are independent, which allows conditioning on different information sets. Monte Carlo experiments and applications to financial series illustrate the asymptotic results. In particular, an empirical study demonstrates that the realized volatility is an helpful covariate for predicting squared returns, but does not constitute an ideal proxy of the volatility.

This paper weakens the size and moment conditions needed for typical block bootstrap methods (i.e., the moving blocks, circular blocks, and stationary bootstraps) to be valid for the sample mean of Near-Epoch-Dependent (NED) functions of mixing processes; they are consistent under the weakest conditions that ensure the original NED process obeys a central limit theorem (CLT), established by De Jong (1997, Econometric Theory 13(3), 353–367). In doing so, this paper extends De Jong’s method of proof, a blocking argument, to hold with random and unequal block lengths. This paper also proves that bootstrapped partial sums satisfy a functional CLT (FCLT) under the same conditions.

This paper presents Rtadf (right-tail augmented Dickey-Fuller), an EViews add-in that facilitates the performance of time series based tests that help detect and date-stamp asset price bubbles. The detection strategy is based on a right-tail variation of the standard augmented Dickey-Fuller (ADF) test where the alternative hypothesis is of a mildly explosive process. Rejection of the null in each of these tests may serve as empirical evidence for an asset price bubble. The add-in implements four types of tests: standard ADF, rolling window ADF, supremum ADF (SADF; Phillips, Wu, and Yu 2011) and generalized SADF (GSADF; Phillips, Shi, and Yu 2015). It calculates the test statistics for each of the above four tests, simulates the corresponding exact finite sample critical values and p values via Monte Carlo methods, under the assumption of Gaussian innovations, and produces a graphical display of the date stamping procedure.

This paper proposes new specification tests for conditional models with discrete responses, which are key to apply efficient maximum likelihood methods, to obtain consistent estimates of partial effects and to get appropriate predictions of the probability of future events. In particular, we test the static and dynamic ordered choice model specifications and can cover infinite support distributions for e.g. count data. The traditional approach for specification testing of discrete response models is based on probability integral transforms of a jittered discrete data which leads to continuous uniform i.i.d. series under the true conditional distribution. Then, standard specification testing techniques for continuous variables could be applied to the transformed series, but the extra randomness from jitters affects the power properties of these methods. We investigate in this paper an alternative transformation based only on original discrete data that avoids any randomization. We analyze the asymptotic properties of goodness-of-fit tests based on this new transformation and explore the properties in finite samples of a bootstrap algorithm to approximate the critical values of test statistics which are model and parameter dependent. We show analytically and in simulations that our approach dominates the methods based on randomization in terms of power. We apply the new tests to models of the monetary policy conducted by the Federal Reserve. (C) 2017 Elsevier B.V. All rights reserved. Support from the Ministerio Economia y Competitividad (Spain), grants ECO2012-31748, ECO2014-57007p, MDM 2014-0431, Comunidad de Madrid, grant S2015/HUM-3444, and Fundación Ramón Areces is gratefully acknowledged.

In this study, we examine the validity of the PPP proposition for 28 European countries. For this purpose, we propose a new unit root test procedure that allows for both gradual structural breaks and asymmetric nonlinear adjustment towards the equilibrium level. Small-sample properties of the new tests are examined through Monte-Carlo simulations. The simulation results suggest that the new tests have satisfactory size and power properties. We then apply these new tests along with other unit root tests to examine stationarity properties of real exchange rate series of the sample countries. Our tests reject the null of unit root in more cases when compared to alternative tests. Overall, we find that the PPP proposition holds in majority of the European countries examined in this article.

This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure based on integrated squared distance is proposed. The statistic’s asymptotic distribu- tion under the MAR hypothesis is derived. We demonstrate that our results can be easily extended to a test of missing completely at random (MCAR) and miss- ing completely at random conditional on covariates X (MCAR(X)). A Monte Carlo study examines finite sample performance of our test statistic. An empirical illustration concerns pocket prescription drug spending with missing values; we reject MCAR but fail to reject MAR.

In this paper, we discuss the key conditions for the identification and estimation of the local average treatment effect (LATE, Imbens and Angrist, 1994): the valid instrument assumption (LI) and the monotonicity assumption (LM). We show that the joint assumptions of LI and LM have a testable implication that can be summarized by a sign restriction defined by a set of intersection bounds. We propose an easy-to-implement testing procedure that can be analyzed in the framework of Chernozhukov, Lee, and Rosen (2013) and implemented using the Stata package of Chernozhukov, Kim, Lee, and Rosen (2013). We apply the proposed tests to the "draft eligibility" instrument in Angrist (1991), the "college proximity" instrument in Card (1993) and the "same sex" instrument in Angrist and Evans (1998).

This article proposes new model checks for dynamic count models. Both portmanteau and omnibus-type tests for lack of residual autocorrelation are considered. The resulting test statistics are asymptotically pivotal when innovations are uncorrelated but possibly exhibit higher order serial dependence. Moreover, the tests are able to detect local alternatives converging to the null at the parametric rate T− 1/2, with T the sample size. The finite sample performance of the test statistics are examined by means of Monte Carlo experiments. Using a dataset on U.S. corporate bankruptcies, the proposed tests are applied to check if different risk models are correctly specified. Supplementary materials for this article are available online.

Estimators of average treatment effects under unconfounded treatment assignment are known to become rather imprecise if there is limited overlap in the covariate distributions between the treatment groups. But such limited overlap can also have a detrimental effect on inference, and lead for example to highly distorted confidence intervals. This paper shows that this is because the coverage error of traditional confidence intervals is not so much driven by the total sample size, but by the number of observations in the areas of limited overlap. At least some of these "local sample sizes" are often very small in applications, up to the point where distributional approximation derived from the Central Limit Theorem become unreliable. Building on this observation, the paper proposes two new robust confidence intervals that are extensions of classical approaches to small sample inference. It shows that these approaches are easy to implement, and have superior theoretical and practical properties relative to standard methods in empirically relevant settings. They should thus be useful for practitioners.

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to combine efficient parameter estimation, variable selection, and valid inference for time series processes. In an application to several constituents of the S&P 500 index it is shown that (i) the optimal significant lag structure is time-varying and subject to drastic regime shifts that seem to happen across assets simultaneously; (ii) in many cases the relevant information for prediction is included in the first 22 lags, corroborating previous results concerning the accuracy and the difficulty of outperforming out-of-sample the heterogeneous autoregressive (HAR) model; and (iii) some common features of the optimal lag structure can be identified across assets belonging to the same market segment or showing a similar beta with respect to the market index.

We propose various specification tests for Hawkes models based on the Lagrange Multiplier (LM) principle. Hawkes models can be used to model the occurrence of extreme events in financial markets. Our specific testing focus is on extending a univariate model to a multivariate model, that is, we examine whether there is a conditional dependence between extreme events in markets. Simulations show that the test has good size and power, in particular for sample sizes that are typically encountered in practice. Applying the specification test for dependence to U.S. stocks, bonds, and exchange rate data, we find strong evidence for cross-excitation within segments as well as between segments, which cannot simply be explained by volatility spillovers. Therefore, we recommend that univariate Hawkes models be extended to account for the cross-triggering phenomenon. (JEL: C12, C22, C32, C52)

This paper deals with three aspects of spectacular oil price episodes such as the one witnessed in 2008. First, the concept of temporary explosiveness is proposed as an empirical method for capturing this type of behavior. The application of a recently proposed recursive unit root test shows that phases of explosive behavior occurred in 1990/1991, 2005/2006, and 2007/2008. Second, the underlying causes of the observed behavior are discussed. The prevailing opinion in the literature is that fundamental factors are the main explanation for the 2007/2008 oil price hike, but that in 1990/1991, speculative demand shocks also played a role. Third, it is shown that temporary oil price hikes influence economic decisions that are based on oil price information. For this purpose, a real options model on the oil field development decision is reconsidered. The mechanism behind this is an increase in the profitability of the oil field development project. In sum, the key contributions of this paper are to highlight a new empirical feature in oil prices and to show that economic effects of speculative demand shocks can emerge that to date appear to have been overlooked.

In this paper, we performed an analysis of the time series of the indicator by effect – GD P with the indicator by effort – external debt, great relevance macroeconomic indicators. This study is based on analyses and comparative statistical correlations about external debt, research analysis will be based on secondary data, based on information provided by the Romanian Statistical Yearbook published by the National Institute of Statistics and other publications of public institutions Romania. The originality of this work lies in combining research methods and statistical techniques used for time series that are total research for characterizing macroeconomic variables such as economic growth and external debt.

The exponential GARCH (EGARCH) model introduced by Nelson (1991) is a popular model for discrete time volatility since it allows for asymmetric effects and naturally ensures positivity even when including exogenous variables. Estimation and inference are usually done via maximum likelihood. Although some progress has been made recently, a complete distribution theory of MLE for EGARCH models is still missing. Furthermore, the estimation procedure itself may be highly sensitive to starting values, the choice of numerical optimization algorithm, etc. We present an alternative estimator that is available in a simple closed form and which could be used, for example, as starting values for MLE. The estimator of the dynamic parameter is independent of the innovation distribution. For the other parameters we assume that the innovation distribution belongs to the class of Generalized Error Distributions (GED), profiling out its parameter in the estimation procedure. We discuss the properties of the proposed estimator and illustrate its performance in a simulation study and an empirical example.

Elliott and Muller (EM) (2007) provide a method for constructing a confidence set for the structural break date by inverting a variant of the locally best test statistic. Previous studies have shown that the EM method produces a set with an accurate coverage ratio even for a small break; however, the set is often overly lengthy. This study proposes a simple modification to rehabilitate their method through the long-run variance estimation. Following the literature, we provide an asymptotic justification for the improvement of the modified method over the original method under a nonlocal asymptotic framework. A Monte Carlo simulation shows that the modified method achieves a shorter confidence set than the EM method, especially when the break is large or the HAC correction is conducted. The modified method may exhibit minor errors in the coverage rate when the break is small; however, the coverage is more stable than alternative methods when the break is large. We apply our method to a level shift ...

Dependent censoring arises in biomedical studies when the survival outcome of interest is censored by competing risks. In survival data with microarray gene expressions, gene selection based on the univariate Cox regression analyses has been used extensively in medical research, which however, is only valid under the independent censoring assumption. In this paper, we first consider a copula-based framework to investigate the bias caused by dependent censoring on gene selection. Then, we utilize the copula-based dependence model to develop an alternative gene selection procedure. Simulations show that the proposed procedure adjusts for the effect of dependent censoring and thus outperforms the existing method when dependent censoring is indeed present. The non-small-cell lung cancer data are analyzed to demonstrate the usefulness of our proposal. We implemented the proposed method in an R “compound.Cox” package.

This paper is concerned with testing rationality restrictions using quantile regression methods. Specifically, we consider negative semidefiniteness of the Slutsky matrix, arguably the core restriction implied by utility maximization. We consider a heterogeneous population characterized by a system of nonseparable structural equations with infinite dimensional unobservable. To analyze the economic restriction, we employ quantile regression methods because they allow us to utilize the entire distribution of the data. Difficulties arise because the restriction involves several equations, while the quantile is a univariate concept. We establish that we may test the economic restriction by considering quantiles of linear combinations of the dependent variable. For this hypothesis we develop a new empirical process based test that applies kernel quantile estimators, and derive its large sample behavior. We investigate the performance of the test in a simulation study. Finally, we apply all concepts to Canadian individual data, and show that rationality is an acceptable description of actual individual behavior.

An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, including conditional and unconditional heteroscedasticity, along with conditions on regressors that allow dependence and do not require existence of a density. The conditions aim to accommodate various settings plausible in economic applications, and can apply also to panel, spatial and time series data. A mean square rate of convergence of nonparametric regression estimates is established followed by asymptotic normality of a quite general statistic. Data-driven studentizations that rely on single or double indices to order the data are justified. In a partially linear model setting, Monte Carlo investigation of finite sample properties and two empirical applications are carried out.

The behavior of the power function of autocorrelation tests such as the Durbin–Watson test in time series regressions or the Cliff-Ord test in spatial regression models has been intensively studied in the literature. When the correlation becomes strong, Krämer (1985, Journal of Econometrics 28, 363–370.) (for the Durbin–Watson test) and Krämer (2005, Journal of Statistical Planning and Inference, 128, 489–496) (for the Cliff-Ord test) have shown that power can be very low, in fact can converge to zero, under certain circumstances. Motivated by these results, Martellosio (2010, Econometric Theory, 26, 152–186) set out to build a general theory that would explain these findings. Unfortunately, Martellosio (2010) does not achieve this goal, as a substantial portion of his results and proofs suffer from nontrivial flaws. The present paper now builds a theory as envisioned in Martellosio (2010) in an even more general framework, covering general invariant tests of a hypothesis on the disturbance covariance matrix in a linear regression model. The general results are then specialized to testing for spatial correlation and to autocorrelation testing in time series regression models. We also characterize the situation where the null and the alternative hypothesis are indistinguishable by invariant tests.

Little attention has been paid to the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. We study several such tests in models estimated by instrumental variables (IV) and limited-information maximum likelihood (LIML). Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The distributions of the statistics are shown to have an ill-defined limit as the parameter that determines the strength of the instruments tends to zero and as the correlation between the disturbances of the structural and reduced-form equations tends to plus or minus one. Simulation experiments demonstrate that this makes it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are not too weak. We also study the power properties of the bootstrap tests.

This paper studies the determinants of software piracy losses along five major macro­economic dimensions: Technological, Educational, Institutional, Access to Information and Labor force. The study was conducted based on a large dataset available from 1995 to 2010 and comprising 81 countries. As for the Technological dimension, more patents by residents increases piracy losses while the effect of R&D is the opposite (decreases piracy losses). In terms of the Educational dimension, the results show that more spending on education increase the piracy losses but, at the same time, more schooling years have the contrary effect. Concerning the Institutional dimension, nations with less corruption have lower piracy levels. Regarding the Access to Information, it seems that access to Internet diminishes the losses while the share of Internet broadband subscriptions has no effect. The results also show that, regarding the Labor dimension, employment in services has a deterrent effect while labor force with higher education and youth unemployment increases piracy losses. First published online 20October 2015&nbsp

open 3 si Pubblicato come working paper: Geraci, Andrea ; Fabbri, Daniele ; Monfardini, Chiara (2014) Testing exogeneity of multinomial regressors in count data models: does two stage residual inclusion work? Bologna: Dipartimento di Scienze economiche DSE, p. 37. DOI 10.6092/unibo/amsacta/3937 . In: Quaderni - Working Paper DSE (921). ISSN 2282-6483. We study a simple exogeneity test in count data models with possibly endogenous multinomial treatment. The test is based on Two Stage Residual Inclusion (2SRI), an estimation method which has been proved to be consistent for a general class of nonlinear parametric models. Results from a broad set of simulation experiments provide novel evidence on important features of this approach. We find differences in the finite sample performance of various likelihood-based tests, analyze their robustness to misspecification arising from neglected over-dispersion or from incorrect specification of the first stage model, and uncover that standardizing the variance of the first stage residuals leads to better results. An original application to testing the endogeneity status of insurance in a model of healthcare demand corroborates our Monte Carlo findings. partially_open embargoed_20171108 Geraci, Andrea; Fabbri, Daniele; Monfardini, Chiara Geraci, Andrea; Fabbri, Daniele; Monfardini, Chiara

This paper studies estimation and specification testing in threshold regression with endogeneity. Three key results differ from those in regular models. First, both the threshold point and the threshold effect parameters are shown to be identified without the need for instrumentation. Second, in partially linear threshold models, both parametric and nonparametric components rely on the same data, which prima facie suggests identification failure. But, as shown here, the discontinuity structure of the threshold itself supplies identifying information for the parametric coefficients without the need for extra randomness in the regressors. Third, instrumentation plays different roles in the estimation of the system parameters, delivering identification for the structural coefficients in the usual way, but raising convergence rates for the threshold effect parameters and improving efficiency for the threshold point. Specification tests are developed to test for the presence of endogeneity and threshold effects without relying on instrumentation of the covariates. The threshold effect test extends conventional parametric structural change tests to the nonparametric case. A wild bootstrap procedure is suggested to deliver finite sample critical values for both tests. Simulation studies corroborate the theory and the asymptotics. An empirical application is conducted to explore the effects of 401(k) retirement programs on savings, illustrating the relevance of threshold models in treatment effects evaluation in the presence of endogeneity.

In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the sequence of bootstrap P values obtained by iterating the bootstrap. The main idea of the paper is that, if this sequence converges to a random variable that follows the uniform U(0; 1) distribution, then the bootstrap is valid. The idea is studied by making the model under test discrete and finite, so that it is characterised by a finite three-dimensional array of probabilities. This device, when available, renders bootstrap iteration to any desired order feasible. It is used for studying a unit-root test for a process driven by a stationary MA(1) process, where it is known that the unit-root test, even when bootstrapped, becomes quite unreliable when the MA(1) parameter is in the vicinity of -1. Iteration of the bootstrap P value to convergence achieves reliable inference except for a parameter value very close to -1. The paper then endeavours to see these specific results in a wider context, and tries to cast new light on where bootstrap theory may be going.

This paper studies the estimation of change point in panel models. We extend Bai (2010) and Feng, Kao and Lazarová (2009) to the case of stationary or nonstationary regressors and error term, and whether the change point is present or not. We prove consistency and derive the asymptotic distributions of the Ordinary Least Squares (OLS) and First Difference (FD) estimators. We find that the FD estimator is robust for all cases considered.

With Monte Carlo experiments on models in widespread use we examine the performance of indirect inference (II) tests of DSGE models in small samples. We compare these tests with ones based on direct inference (using the Likelihood Ratio, LR). We find that both these tests have power so that a substantially false model will tend to be rejected by both; but that the power of the II test is substantially greater, both because the LR is applied after re-estimation of the model error processes and because the II test uses the false model's own restricted distribution for the auxiliary model's coefficients. This greater power allows users to focus this test more narrowly on features of interest, trading off power against tractability.

The empirically most relevant stylized facts when it comes to modeling time varying financial volatility are the asymmetric response to return shocks and the long memory property. Up till now, these have largely been modeled in isolation though. To more flexibly capture asymmetry also with respect to the memory structure we introduce a new model and apply it to stock market index data. We find that, although the effect on volatility of negative return shocks is higher than for positive ones, the latter are more persistent and relatively quickly dominate negative ones.

This paper proposes a Cramer-von Mises (CM) test statistic to check the adequacy of weak ARMA models. Without posing a martingale difference assumption on the error terms, the asymptotic null distribution of the CM test is obtained by using the Hillbert space approach. Moreover, this CM test is consistent, and has nontrivial power against the local alternative of order $n^{-1/2}$. Due to the unknown dependence of error terms and the estimation effects, a new block-wise random weighting method is constructed to bootstrap the critical values of the test statistic. The new method is easy to implement and its validity is justified. The theory is illustrated by a small simulation study and an application to S\&P 500 stock index.

Summary The paper uses a random-weighting (RW) method to bootstrap the critical values for the Ljung–Box or Monti portmanteau tests and weighted Ljung–Box or Monti portmanteau tests in weak auto-regressive moving average models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power generalized auto-regressive conditional heteroscedasticity models. Simulation evidence indicates that the weighted portmanteau tests have a power advantage over other existing tests. A real example on the Standard and Poor's 500 index illustrates the merits of our testing procedure. As an extension, the blockwise RW method is also studied.

A dynamic panel data model is considered that contains possibly stochastic individual components and a common fractional stochastic time trend. We propose four different ways of coping with the individual effects so as to estimate the fractional parameter. Like models with autoregressive dynamics, ours nests a unit root, but unlike the nonstandard asymptotics in the autoregressive case, estimates of the fractional parameter can be asymptotically normal. Establishing this property is made difficult due to bias caused by the individual effects, or by the consequences of eliminating them, and requires the number of time series observations T to increase, while the cross-sectional size, N; can either remain fi…xed or increase with T: The biases in the central limit theorem are asymptotically negligible only under stringent conditions on the growth of N relative to T; but these can be relaxed by bias correction. For three of the estimates the biases depend only on the fractional parameter. In hypothesis testing, bias correction of the estimates is readily carried out. We evaluate the biases numerically for a range of T and parameter values, develop and justify feasible bias-corrected estimates, and briefly discuss implied but less effective corrections. A Monte Carlo study of …finite-sample performance is included.

This paper examines the connection between economic growth and stock market performance in the case of an emerging economy, namely Romania, by using quarterly financial data, during the period 2000-2013. This topic is widely studied in the financial literature and seeks to provide an answer for the following questions: does economic growth influences the capital market, does capital market influences economic growth, or there is no connection between these variables. I have analyzed the long term relationship between economic growth and stock market for Romania, by applying Johansen cointegration test, Granger causality and Gregory Hansen cointegration test, which allows the presence of the structural breaks in the time series. The empirical results obtained highlighted that portfolio investments have a positive impact on economic growth and the GDP growth engages in turn, a long term positive capital markets return. The main conclusion of this study is that in the case of Romanian economy, is a bi-directional link between the economic growth and the capital market performance.

Estimation of a sample quantile's variance requires estimation of the probability density at the quantile. The common quantile spacing method involves smoothing parameter m. When m, n → ∞ , the corresponding Studentized test statistic asymptotically follows a standard normal distribution. Holding m fixed asymptotically yields a nonstandard distribution dependent on m that contains the Edgeworth expansion term capturing the variance of the quantile spacing. Consequently, the fixed-m distribution is more accurate than the standard normal under both asymptotic frameworks. For the fixed-m test, I propose an m to maximize power subject to size control, as calculated via Edgeworth expansion. Compared with similar methods, the new method controls size better and maintains good or better power in simulations. Results for two-sample quantile treatment effect inference are given in parallel.

Having as subject the labor market in the context of the contemporary world’s problems, this paper aims to make a theoretical and practical presentation of these concepts, which are relevant for the national and international literature. The first part, dealing with the state-of-the-art of the domain, aims to present these concepts from the perspective of many specialists in this field. The practical part of this article consists in a research based on the presentation of the labor market under the impact of the contemporary world’s problems. To support our line of reasoning, in our scientific approach we used a research based on the analysis of secondary sources, and as quantitative methods, we used: the simple linear regression and the public opinion poll. Among the contemporary world’s issues analyzed using the quantitative methods were: juvenile delinquency, violence against other peoples and population’s poverty rate. The hypothesis of this paper starts from the premise according to which lack of education generates intensification of the contemporary world’s problems, especially juvenile delinquency and poverty rate, these two having an impact upon the employment rate of the labor market, because potentially active people are convicted when referring to people above 18 years old and in special educational institutions when referring to people under 18. The most relevant conclusions of the research highlight the fact that between the labor market and the contemporary world’s problems there is interdependence, and not giving the appropriate importance to the contemporary world’s issues will generate irremediable difficulties in society.

This paper studies the semiparametric binary response model with interval data investigated by Manski and Tamer (2002, MT). In this partially identified model, we propose a new estimator based on MT's modified maximum score (MMS) method by introducing density weights to the objective function, which allows us to develop asymptotic properties of the proposed set estimator for inference. We show that the density-weighted MMS estimator converges to the identified set at a nearly cube-root-n rate. Further, we propose an asymptotically valid inference procedure for the identified region based on subsampling. Monte Carlo experiments provide supports to our inference procedure.

This discussion paper led to a publication in the Journal of Business & Economic Statistics , 2015, 33(2), 241-254. We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists in estimating the parameters of the model under the null hypothesis of the cointegration rank r=1,2,…,p-1. This step provides consistent estimates of the order of fractional cointegration, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup-likelihood ratio test of no-cointegration on the estimated p-r common trends that are not cointegrated under the null. The order of fractional cointegration is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step to adapt to the representation of the common trends estimated in the first step. The critical values of the proposed tests depend only on the number of common trends under the null, p-r, and on the interval of the orders of fractional cointegration b allowed in the estimation, but not on the order of fractional cointegration of already identified relationships. Hence this reduces the set of simulations required to approximate the critical values, making this procedure convenient for practical purposes. In a Monte Carlo study we analyze the finite sample properties of our procedure and compare with alternative methods. We finally apply these methods to study the term structure of interest rates.

The main purpose of this study is to investigate the Long and short Run relationship between government revenue and government expenditure in Iran Country covering data 1978- 2012 with using An Auto Regressive Distributive Lag (ARDL) Approach. We investigated causality between revenue and government expenditure by using an application of Toda-Yamamoto approach. Autoregressive Distributeded Lag (ARDL) technique is used to describe both long run relationships and short run dynamic adjustments between government revenue and expenditure variables. The results of this paper support the Freidman (1978) hypothesis that government revenues cause expenditure and revenues have a positive causal impact on government expenditure.