Empirically Relevant Critical Values For Hypothesis Tests: A Bootstrap Approach (1998)
Tests of statistical hypotheses can be based on either of two critical values: the Type I critical value or the size-corrected critical value. The former usually depends on unknown population...
Real and Spurious Long Memory Properties of Stock Market Data (1997)
We test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure. Spurious results can be produced by nonstationarity and aggregation. We...
Bootstrapping the Box-Pierce Q test: A Robust Test of Uncorrelatedness (1970)
Joel L. Horowitz, I. N. Lobato, John C. Nankervis, N. E. Savin
This paper considers a test of the null hypothesis that the first K autocorrelations of a covariance stationary time series are zero in the presence of statistical dependence. The test is based on...
Real and Spurious Long Memory Properties of Stock Market Data
We test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure. Spurious results can be produced by nonstationarity and aggregation. We...
Power of Tests in Binary Response Models
Most hypotheses in binary response models are composite. The null hypothesis is usually that one or more slope coefficients are zero. Typically, the sequence of alternatives of interest is one in...
The Effect of Nuisance Parameters on the Power of LM Tests in Logit and Probit Models
In econometrics, most null hypotheses are composite, dividing the parameters into parameters of interest and nuisance parameters. The domain of the nuisance parameters can influence the...
A Spline Analysis of the Small Firm Effect: Does Size Really Matter?
Joel L. Horowitz, Tim Loughran, N. E. Savin
This paper uses average monthly returns and linear spline regressions to investigate the relation between expected return and firm size during 1980-1994. We find that the average monthly returns are...
Testing for Autocorrelation Using a Modified Box-Pierce Q Test.
Lobato, Ignacio, Nankervis, John C, Savin, N E
This article investigates the finite-sample performance of a modified Box-Pierce Q statistic (Q*) for testing that financial time series are uncorrelated without assuming statistical independence....
Bootstrapping the Box-Pierce Q test: A robust test of uncorrelatedness
Horowitz, Joel L., Lobato, I.N., Nankervis, John C., Savin, N.E.
This paper illustrates the pitfalls of the conventional heteroskedasticity and autocorrelation robust (HAR) Wald test and the advantages of new HAR tests developed by Kiefer and Vogelsang in 2005 and...
Testing the Semiparametric Box-Cox Model with Bootstrap
This paper considers tests of the transformation parameter of the Box-Cox model when the distribution of the error is unknown. Monte Carlo experiments are carried out to investigate the rejection...
Semiparametric Estimation of the Box-Cox Model Preliminary and Incomplete
This paper investigates the finite sample performance of three semiparametric estimators of the Box-Cox model. Two of the semiparametric estimators are the nonlinear two-stage least squares (NL2SLS)...
CAPM Reconsidered: A Robust Finite Sample Evaluation
Ravikumar, B., Ray, Surajit, Savin, N.E.
In this paper, the conventional test of the Sharpe-Lintner version of the Capital Asset Pricing Model (CAPM) are reconsidered. The CAPM is formulated as a Seemingly Unrelated Regression (SUR) system...
A Robust Test For Autocorrelation in the Presence of Statistical Dependence
Lobato, I.N., Nankervis, John C., Savin, N.E.
The problem addressed in this paper is to test the null hypothesis that a time series process is uncorrelated up to lag K in the presence of statistical dependence. We propose a robust test that is...
Surajit, R., Ravikumar, B., Savin, N.E.
In this paper, we examine the robust Wald test statistic for SUR systems with adding up restrictions where the same explanatory variables are present in all equations and where heteroskedasticity...
Learning in Sender-Receiver Games
Blume, A., DeJong, D.V., Neumann, G.R., Savin, N.E.
Stimulus-response (SR) and belief-based learning (BBL) models are estimated with experimental data from sender-receiver games and compared using the Davidson and MacKinnon P-test for non-nested...
Empirically Relevant Critical Values For Hypothesis Tests: The Bootstrap to the Rescue
Tests of statistical hypotheses can be based on either of two critical values: the Type I critical value or the size-corrected critical value. The former usually depends on unknown population...
The Power of Hessian and Outer Product Based Wald and LM Tests.
Parks, R.W., Savin, N.E., Wurtz, A.H.
Wald and Lagrange Multiplier (LM) tests can be based on three commonly used estimators of the information matrix : the expectation of the Hessian matric, the Hessian matrix without the expectation...
Testing that Stock Returns Are Uncorrelated Using A General Box-Pierce Q Test.
Nankervis, J.C., Savin, N.E., Lobato, I.
This paper investigates the problem of testing that stock returns are uncorrelated without assuming statistical independence. This paper presents a generalized Box-Pierce Q statistics, denoted by Q*,...
The Effect of Nuisance Parameters on the Power of LM Tests in Logit and Probit Models.
In econometrics, most null hypotheses are composite, dividing the parameters into parameters of interest and nuisance parameters. The domain of the nuisance parameters can influence the...
Power of tests in Binary Response Models.
Most hypotheses in binary response models are composite. The null hypothesis is usually that one or more slope coefficients are zero. Typically, the sequence of alternatives of interest is one in...
Real and Spurious Long Memory Properties of Stock Market Data.
We test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure. Spurious results can be produced by nonstationarity and aggregation.
A Spline Analysis of the Small Firm Effect: Does Size Really Matter.
Savin, N.E., Loughran, T., Horowitz, J.L.J.
In this paper we investigate the relation between expected return and firm size. Starting with the pionnering work of Banz (1981) and Reinganum (1981), this area has been one of the most researched...
Confidence Intervals for the Sample Mean of Overdifferenced Data.
Kocherlakota, N.R., Savin, N.E.
econometrics
Using Macroeconomic Data to Measure Nonmarket Activity.
Kocherlakota, N., Ingram, B.F., Savin, N.E.
ECONOMIC ACTIVITY;METHODOLOGY
On the Inferior Power of Outer Product Wald and LM Tests.
information ; evaluation
Is the Minimum Chi-Square Estimator the Winner in Logit Regression?
econometrics ; economic models
Non-Invarience of the Wald Test: The Bootstrap to the Rescue.
economic models ; econometrics
Is the Minimum Chi-Squared Estimator the Winner in Logit Regression?
maximum likelihood ; economic models ; evaluation
Explaining Business Cycles : A Multiple Shock Approach.
Ingram, B.F., Kocherlakota, N.R., Savin, N.E.
productivity ; economic models
Multiple Optima and Asymptotic Approximations in the Partial Adjustment Model.
McManus, D.A., Nankervis, J.C., Savin, N.E.
statistics ; economic models ; probability
What in the Best Estimator of the ML Covariance in Linear Regression Models?
econometrics ; linear models ; information
Binary Response Models: Logits, Probits and Semiparametrics
A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit...
TESTING FOR ZERO AUTOCORRELATION IN THE PRESENCE OF STATISTICAL DEPENDENCE
Lobato, I.N., Nankervis, John C., Savin, N.E.
The problem addressed in this paper is to test the null hypothesis that a time series process is uncorrelated up to lag K in the presence of statistical dependence. We propose an extension of the Box...
Explaining business cycles: A multiple-shock approach
Ingram, Beth Fisher, Kocherlakota, Narayana R., Savin, N. E.
Comparing Covariance Matrix Estimators in Linear Regression Models.
economic models ; statistics ; maximum likelihood
What is the Best Estimator of the ML Covariance in Linear Regression Models?
information ; econometrics ; economic models
The Student's t Approximation in a Stationary First Order Autoregressive Model.
The exact distribution of the regression t statistic for testing the value of the AR parameter in a Gaussian first ord er autoregressive model is investigated by Monte Carlo methods. The S tudent's t...
The power problems of unit root test in time series with autoregressive errors
DeJong, David N., Nankervis, John C., Savin, N. E., Whiteman, Charles H.
Real and Spurious Long-Memory Properties of Stock-Market Data.
The authors test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure of I. Lobato and P. M. Robinson (1997). Spurious results can be...
The Level and Power of the Bootstrap t Test in the AR(1) Model with Trend.
This paper considers a first-order autoregressive model which may include an intercept and trend where the innovations are independently and identically distributed. The innovation distribution is...
Learning and communication in sender-receiver games: an econometric investigation
Andreas Blume, Douglas V. DeJong, George R. Neumann, N. E. Savin
This paper compares stimulus response (SR) and belief-based learning (BBL) using data from experiments with sender-receiver games. The environment, extensive form games played in a population...
Empirically Relevant Power Comparisons for Limited Dependent Variable Models
Most hypotheses in limited dependent variable (LDV) models are composite, meaning that the null hypothesis H0 does not completely specify the data generating process (DGP). In this case, the null...
Measuring the cyclical behavior of home production: a macroeconomic analysis
Beth F. Ingram, Narayana R. Kocherlakota, N.E. Savin
Much economic activity takes place within the home. Unfortunately, it is difficult to assess the cyclical properties of home production because the available data are too sporadic. Under the...
Comparing Covariance Matrix Estimators in Linear Regression Models.
economic models ; statistics ; maximum likelihood
What is the Best Estimator of the ML Covariance in Linear Regression Models?
information ; econometrics ; economic models
The Effect of Nuisance Parameters on Size and Power; LM Tests in Logit Models
In econometrics, most null hypotheses are composite, dividing the parameters into parameters of interest and nuisance parameters. Typically, a composite hypothesis can be tested using two or more...