A Monte Carlo Analysis of the Fisher Randomization Technique: Reviving Randomization for Experimental Economists

Data created in a controlled laboratory setting are a relatively new phenomenon to economists. Traditional data analysis methods using either parametric or nonparametric tests are not necessarily the best option available to economists analyzing laboratory data. In 1935, Fisher proposed the randomization technique as an alternative data analysis method when examining treatment effects. The observed data are used to create a test statistic. Then treatment labels are shuffled across the data and the test statistic is recalculated. The original statistic can be ranked against all possible test statistics that can be generated by these data, and a p-value can be obtained. A Monte Carlo analysis of t-test, the Mann-Whitney U-test, and the exact randomization t-test is conducted. The exact randomization t-test compares favorably to the other two tests both in terms of size and power. Given the limited distributional assumptions necessary for implementation of the exact randomization test, these results suggest that experimental economists should consider using the exact randomization test more often. This revised version was published online in August 2006 with corrections to the Cover Date.     

Household characteristics and consumption behaviour: A nonparametric approach

In this paper we apply nonparametric methods in order to discuss some empirical aspects of household consumption behaviour. First, we study the differences in the consumption behaviour between household types. We find that, except for food, there are no clear significant differences. Secondly, we derive the functional form for the food Engel curve, using specification tests consistent in the direction of nonparametric alternatives. Finally, we use this specification to discuss the misleading conclusions that could be reached from a mechanic interpretation of the rejection of Hausman's test, when applied to test the exogeneity of expenditure. The data is obtained from the Spanish Expenditure Survey 1980–81 and 1990–91.     

Economic development and CO2 emissions: A nonparametric panel approach

We examine the empirical relation between CO₂ emissions per capita and GDP per capita during the period 1960–1996, using a panel of 100 countries. Relying on the nonparametric poolability test of Baltagi et al. [Baltagi, B.H., Hidalgo, J., Li, Q., 1996. A nonparametric test for poolability using panel data, Journal of Econometrics 75, 345–367], we find evidence of structural stability of the relationship. We then specify a nonparametric panel data model with country-specific effects. Estimation results show that this relationship is upward sloping. Nonparametric specification tests do not reject monotonicity but do reject the polynomial functional form which leads to the environmental Kuznets curve in several studies.     

Nonlinear approaches in testing PPP: Evidence from Southern African development community

In this paper two nonlinearity tests are employed: the nonparametric test developed by Brock, Dechert and Scheinkman — known as the BDS test and the Fourier stationarity test. The BDS non-linearity test detects whether the independent and identically distribute (iid) assumption of the time series used in the analysis holds true or not while the Fourier approximation mimics a wide variety of breaks and other types of nonlinearities. Both tests confirm the non-linear nature of real exchange series in the Southern African Development Community (SADC). The result from the Fourier stationary test further provides strong evidence of an optimal currency area (OCA) among the 11 SADC countries included in this study.     

Testing for a unit root under the alternative hypothesis of ARIMA (0, 2, 1)

Showing a dual relationship between ARIMA (0,?2,?1) with parameter θ?=??1 and the random walk, a new alternative hypothesis in the form of ARIMA (0,?2,?1) is established in this article for evaluating unit root tests. The power of four methods of testing for a unit root is investigated under the new alternative, using Monte Carlo simulations. The first method testing θ?=??1 in second differences and using a new set of critical values suggested by the two authors in finite samples, is the most appropriate from the integration order point of view. The other three methods refer to tests based on t and?φ?statistics introduced by Dickey and Fuller, as well as, the nonparametric Phillips–Perron test. Additionally, for cases where for the first method a low power is met, we studied the validity of prediction interval for a future value of ARIMA (0,?2,?1) with θ close but greater of ?1, using the prediction equation and the error variance of the random walk. Keeping the forecasting horizon short, the coverage of the interval ranged at expected levels, but its average half-length ranged up to four times more than its true value.     

BOOTSTRAPPING COVARIATE UNIT ROOT TESTS: AN APPLICATION TO INFLATION RATES

This paper proposes a bootstrap procedure for the covariate point optimal tests (CP_T) of Elliott and Jansson. Although the covariate tests enjoy large power gains over the traditional univariate unit root tests, our simulations show that they still suffer from severe size distortions at finite samples. Through simulations, we demonstrate the superiority of the bootstrap procedure in the sense that it can yield desirable size and power properties for the CP_T tests when the Akaike's information criterion is used. Moreover, we show the empirical relevance of the bootstrap tests by applying them to inflation in the G‐10 countries, and then obtain strong evidence against the unit root hypothesis for most countries at the 5% significance level.     

Nonparametric neural network modeling of hedonic prices in the housing market

This article addresses the contribution to hedonic modeling of a nonparametric approach based on artificial neural network (ANN) regressions. ANNs provide consistent estimates for the hedonic price of each attribute and permit a number of hypotheses on the hedonic price relationship to be tested nonparametrically. In particular, we exploit results by Stinchcombe and White (Econom Theory 14:295?C324, 1998) in order to carry out misspecification testing in linear and semiloglinear hedonic models. The same approach directly applies to testing misspecification of any parametric specification for the hedonic relationship. A nonparametric significance test for the variables in the hedonic model is also proposed. The test extends the approach developed by Racine (J Bus Econ Stat 15(3):369?C378, 1997) in kernel-based nonparametric testing to ANN-based inference. The finite sample performance of the proposed tests is analyzed through Monte Carlo experiments, and simulation-based algorithms for computation of the null distribution of the tests are proposed. Then, the performance of three classes of regression models??linear, semi-log, and ANNs??applied to hedonic price modeling in a Spanish regional housing market is compared. Our results indicate the presence of nonlinear behavior, as predicted by economic theory, with the ANN-based tests detecting statistically significant evidence of misspecification??both in the linear and the semilog specifications??and ANN regressions providing moderate improvement of predictive performance.     

TESTING STRICT STATIONARITY WITH APPLICATIONS TO MACROECONOMIC TIME SERIES

We propose a model‐free test for strict stationarity. The idea is to estimate a nonparametric time‐varying characteristic function and compare it with the empirical characteristic function based on the whole sample. We also propose several derivative tests to check time‐invariant moments, weak stationarity, and pth order stationarity. Monte Carlo studies demonstrate excellent power of our tests. We apply our tests to various macroeconomic time series and find overwhelming evidence against strict and weak stationarity for both level and first‐differenced series. This suggests that the conventional time series econometric modeling strategies may have room to be improved by accommodating these time‐varying features.     

Temporal and Spatial Homogeneity in Air Pollutants Panel EKC Estimations

Although panel data have been used intensively by a wealth of studies investigating the GDP-pollution relationship, the poolability assumption used to model these data is almost never addressed. This paper applies a strategy to test the poolability assumption with methods robust to functional misspecification. Nonparametric poolability tests are performed to check the temporal and spatial homogeneity of the panel and their results are compared with the conventional F-tests for a balanced panel of 48 Spanish provinces on four air pollutant emissions (CH₄, CO, CO₂ and NMVOC) over the 1990–2002 period. We show that temporal homogeneity may allow the pooling of the data and drive to well-defined nonparametric and parametric cross-sectional U-inverted shapes for all air pollutants. However, the presence of spatial heterogeneity makes this shape compatible with different time-series patterns in every province—mainly increasing or decreasing depending on the pollutant. These results highlight the extreme sensitivity of the income-pollution relationship to region- or country-specific factors.      

PREDICTING STOCK RETURNS AND VOLATILITY WITH INVESTOR SENTIMENT INDICES: A RECONSIDERATION USING A NONPARAMETRIC CAUSALITY‐IN‐QUANTILES TEST

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S^BW and S^PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only S^PLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality‐in‐quantiles model of Balcilar et al., (forthcoming), in fact, both S^BW and S^PLS can predict stock returns and its volatility, with S^PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S^BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.     

The role of financial sector competition for monetary policy

Abstract In this paper, we examine the impact of competition in the banking industry on financial market activity. In particular, we explore this issue in a setting where banks simultaneously insure individuals against liquidity risk and offer loans to promote intertemporal consumption smoothing. In addition, spatial separation and private information generate a transactions role for money. Interestingly, we demonstrate that the industrial organization of the financial system bears significant implications for the effects of monetary policy. Under perfect competition, higher rates of money growth lead to lower interest rates and a higher volume of lending activity. In contrast, in a monopoly banking sector, money growth restricts the availability of funds and raises the cost of borrowing.     

Monetary policy and exchange rate dynamics in the Spanish economy

As the Spanish economy gets more integrated in international markets, the real exchange rate becomes a key determinant of the monetary transmission. In this paper we trace out the dynamic response of prices, output and the exchange rate following a monetary policy shock. We estimate a structural VAR model whose identification scheme is based on the long run properties common to a large class of models. The results suggest that a small model with efficient asset markets plus nominal inertia and long run monetary neutrality, captures the essential features of the monetary transmission mechanism in Spain. The interest rate shock is well identified and the exchange rate overshoots its long run value. There are no signs of liquidity puzzle nor of price puzzle or exchange rate puzzle either.     

Is the environmental performance of industrialized countries converging? A ‘SURE’ approach to testing for convergence

In this paper, we test for convergence in the environmental performance of a sample of OECD countries, with data ranging from 1971 to 2002. First, we use Data Envelopment Analysis (DEA) to compute two environmental performance indicators (EPIs) in the production theory framework. Second, we propose the use of a sequential multivariate approach to test for convergence in environmental performance. These tests allow us to reconcile the time series literature with the cross-sectional dimension, which is basic when testing for convergence in regional blocs. The SURE technique is used, which allows for the existence of correlations across the series without imposing a common speed of mean reversion. The empirical results show that the group of countries as a whole, as well as the majority of countries considered on an individual basis (results for some countries vary between EPIs), are catching-up with Switzerland (the benchmark country).     

Nonparametric Sharpe Ratio

Sharpe ratio is a widely accepted tool for comparing the portfolio performance. In this paper we have proposed a nonparametric measure of the Sharpe rule. The statistical properties of this nonparametric measure and the standard Sharpe ratio are then developed under both normality and non-normality of observations. Further thebias corrected measures are given. An empirical application is also provided.

     

TESTING FOR STATIONARITY IN HETEROGENEOUS PANEL DATA IN THE CASE OF MODEL MISSPECIFICATION

This paper investigates the performance of the tests proposed by Hadri and by Hadri and Larsson for testing for stationarity in heterogeneous panel data under model misspecification. The panel tests are based on the well known KPSS test (cf. Kwiatkowski et al.) which considers two models: stationarity around a deterministic level and stationarity around a deterministic trend. There is no study, as far as we know, on the statistical properties of the test when the wrong model is used. We also consider the case of the simultaneous presence of the two types of models in a panel. We employ two asymptotics: joint asymptotic, T, N →∞ simultaneously, and T fixed and N allowed to grow indefinitely. We use Monte Carlo experiments to investigate the effects of misspecification in sample sizes usually used in practice. The results indicate that the assumption that T is fixed rather than asymptotic leads to tests that have less size distortions, particularly for relatively small T with large N panels (micro‐panels) than the tests derived under the joint asymptotics. We also find that choosing a deterministic trend when a deterministic level is true does not significantly affect the properties of the test. But, choosing a deterministic level when a deterministic trend is true leads to extreme over‐rejections. Therefore, when unsure about which model has generated the data, it is suggested to use the model with a trend. We also propose a new statistic for testing for stationarity in mixed panel data where the mixture is known. The performance of this new test is very good for both cases of T asymptotic and T fixed. The statistic for T asymptotic is slightly undersized when T is very small (≤10).     

Trade and regional growth in Spain: panel cointegration in a small sample

In this article, we test for the existence of a relationship between per capita Gross Domestic Product (GDP) and trade, for 15 Spanish Autonomous Communities between 1988 and 2004, using a panel cointegration methodology. In particular, we implement several panel unit root tests (Maddala and Wu, 1999; Levin et al., 2002; Im et al., 2003) and panel cointegration tests (Pedroni, 1999, 2004), with a special attention to their behaviour in a small sample. We also develop a Seemingly Unrelated Regression Estimation (SURE) residual based test, in order to explicitly take into account the cross regional correlation pattern. Appropriate confidence intervals are estimated with a sieve bootstrap designed for our small time sample, preserving the dependence structure among cross sectional units. Our cointegration tests reject the existence of a significant relationship between GDP per capita and exports. However, we do find some evidence of a significant relationship between GDP per capita and imports or with total trade.     

An Experimental Study of Communication and Coordination in Noncooperative Games

This paper reports the results of an experiment designed to test the usefulness of alternative solution concepts to explain players' behavior in noncooperative games with preplay communication. In the experiment subjects communicate byplain conversationprior to playing a simple game. In this setting, we find that the presumption ofindividualisticandindependentbehavior underlying the concept of Nash equilibrium is inappropriate. Instead, we observe behavior to becoordinatedandcorrelated. Statistical tests reject Nash equilibrium as an explanation of observed play. The coalition proof correlated equilibrium of the game, however, explains the data when the possibility of errors by players is introduced.Journal of Economic LiteratureClassification Numbers: C72, C92.     

TESTING FOR STATIONARITY WITH A BREAK IN PANELS WHERE THE TIME DIMENSION IS FINITE

In this paper, we consider the case of finite time dimension in the panel stationarity tests with structural breaks. By fixing T, the finite sample properties of the tests for both micro (T small and N large) and macro (both T and N large) panel data are generally greatly improved. More importantly, the derivation of the tests for finite T and , as opposed to joint asymptotic where N and simultaneously, avoids the imposition of the rate condition making the test valid for any (T, N) blend. Four models corresponding to the usual combination of breaks are considered. The asymptotic distributions of the test are derived under the null hypothesis and are shown to be normally distributed. Their moments for T fixed are derived analytically employing Ghazal’s corollary 1. The case with unknown breaks is also considered. The proposed tests have generally empirical sizes that are very close to the nominal size. The Monte Carlo simulations show that the power of the test statistics increases substantially with N and T.     

Nonparametric Tests of Differences in Medians: Comparison of the Wilcoxon–Mann–Whitney and Robust Rank-Order Tests

The nonparametric Wilcoxon–Mann–Whitney test is commonly used by experimental economists for detecting differences in central tendency between two samples. This test is only theoretically appropriate under certain assumptions concerning the population distributions from which the samples are drawn, and is often used in cases where it is unclear whether these assumptions hold, and even when they clearly do not hold. Fligner and Pollicello's (1981, Journal of the American Statistical Association. 76, 162–168) robust rank-order test is a modification of the Wilcoxon–Mann–Whitney test, designed to be appropriate in more situations than Wilcoxon–Mann–Whitney. This paper uses simulations to compare the performance of the two tests under a variety of distributional assumptions. The results are mixed. The robust rank-order test tends to yield too many false positive results for medium-sized samples, but this liberalness is relatively invariant across distributional assumptions, and seems to be due to a deficiency of the normal approximation to its test statistic's distribution, rather than the test itself. The performance of the Wilcoxon–Mann–Whitney test varies hugely, depending on the distributional assumptions; in some cases, it is conservative, in others, extremely liberal. The tests have roughly similar power. Overall, the robust rank-order test performs better than Wilcoxon–Mann–Whitney, though when critical values for the robust rank-order test are not available, so that the normal approximation must be used, their relative performance depends on the underlying distributions, the sample sizes, and the level of significance used.     

The NIRCU and the Phillips curve: an approach based on micro data

Abstract In this paper we propose a straightforward method to derive a non‐inflationary rate of capacity utilization (NIRCU) based on micro data. We condition the current capacity utilization of firms on their current and planned price adjustments. The non‐inflationary capacity utilization rate is then defined as the rate where a firm feels no price adjustment pressure. One of the main advantages is that this methodology uses structural aspects and does not make it necessary to operate with – often rather arbitrary – statistical filters. We show that our aggregate NIRCU performs remarkably well as an indicator of inflationary pressure in a Phillips curve estimation.