Threshold effects in panel data stochastic frontier models of dairy production in Canada

One of the most enduring problems in econometrics is how to properly account for heterogeneity among firms. Threshold regression models are intuitively appealing methods to deal with this issue. We consider a fixed-effect panel data stochastic frontier model (Schmidt and Sickles, 1984; Martin-Marcos and Suarez-Galvez, 2000) and, relying on Hansen (1999, 2000a), we propose an estimator that accommodates multiple thresholds. Our model assumes absence of any unmeasured time invariant heterogeneity across firms as in Greene (2005, p. 277). Slope and threshold parameters can be estimated using a within estimator combined with a grid search over the threshold parameters. Testing for threshold effects is problematic because threshold parameters are not identified under the null hypothesis, a case of the so-called Davies' problem. We apply the bootstrap procedure proposed by Hansen (1999, 2000a) to test for the presence of thresholds. An asymptotic confidence set for the threshold parameter can be obtained by inverting an LR test, using the distribution result presented in Hansen (1999, 2000a). Our empirical application features a panel of Quebec dairy farms. We use farm size as the threshold variable. The presence of a trend in the specification matters for the determination of the number of thresholds. Technical efficiency scores and rankings of farms estimated from competing model specifications are highly correlated and do not vary significantly across groups of farm sizes defined by the threshold parameter values.     

A Pearson's test for symmetry with an application to the Spanish business cycle

In this paper, we look for asymmetries in the Spanish business cycle. To that end, we firstly propose an easy nonparametric testing procedure to test for symmetry based on a Pearson's chi-squared statistic, which we call P-test. Then, we test for two popular forms of asymmetry, deepness and steepness, using a battery of nonparametric tests. In addition, we analyse possible complementarities between the tests used in this paper, and we compute p-value adjustments for multiple tests.     

Now, whose schools are really better (or weaker) than Germany's? A multiple testing approach

Using PIRLS (Progress in International Reading Literacy Study) data, we investigate which countries' schools can be classified as significantly better or weaker than Germany's as regards the reading literacy of primary school children. The ‘standard’ approach is to conduct separate tests for each country relative to the reference country (Germany) and to reject the null of equally good schools for all those countries whose p-value satisfies p_i ? 0.05. We demonstrate that this approach ignores the multiple testing nature of the problem and thus overstates differences between schooling systems by producing unwarranted rejections of the null. We employ various multiple testing techniques to remedy this problem. The results suggest that the ‘standard’ approach may overstate the number of significantly different countries by up to 30%.     

On the application of the rank tests for nonlinear cointegration to PPP: The case of Papua New Guinea

Much interest has been paid recently to the nonlinear cointegrating relations existing among economic variables. Various testing procedures are already available to test for the existence of nonlinear cointegration. For example, Breitung (2001) proposes rank tests and his testing procedure has been broadly applied. In this study, we warn against a blind application of the rank cointegration tests, particularly to economic variables that evidence certain behavior. As an illustration, we employ the nominal exchange rates and relative prices of Papua New Guinea against her major trading partners with the objective of testing the validity of purchasing power parity for the country. Our simulation results also confirm our warnings. Additionally, we provide some simple solutions to the problem we encounter herein.     

A new test for linear inequality constraints when the variance–covariance matrix depends on the unknown parameters

We extend Hansen’s (2005) test to testing hypotheses involving general inequality constraints where the variance–covariance matrix of the functions in the constraints depends on the unknown parameters. The test can be applied to a wider class of problems than Wolak’s (1991).     

A robust test for multivariate normality

The size of the Jarque-Bera test for multivariate normality can be severely distorted in small samples. An alternative test procedure, that turns out to have good size and power properties, is suggested.     

Business cycle transmission from the US to Germany—A structural factor approach

This paper investigates the transmission of US macroeconomic shocks to Germany using a large-dimensional structural dynamic factor model. This framework allows us to investigate many transmission channels simultaneously, including “new” channels such as stock markets, foreign direct investment, bank lending and the confidence channel. We find that US shocks affect the US and Germany largely symmetrically. Trade seems to be the most relevant transmission channel. Monetary policy reactions to strong price movements seem to play a role as well. No clear conclusion can be drawn yet on the role of financial markets and the confidence channel. Negative domestic influences apparently more than offset positive US influences in the German economy between 1995 and 2000, but the US recession in 2001 appeared to be the main culprit in the German slump.     

Nonparametric Regression and Causality Testing:A Monte-Carlo Study

In this paper we propose a new procedure for causality testing using nonparametric additive models. We argue that the major advantage of our proposed method is that it can be used if the underlying data generation process (DGP) is either linear or nonlinear. Our results show that the nonparametric testing procedure provides a more robust test of causality. Furthermore, we show that the loss of power associated with the nonparametric procedure is minimal if the true DGP is linear.     

Testing for a unit root with covariates against nonlinear alternatives

This paper proposes a new procedure for testing the unit root null against stationary but nonlinear alternatives. This test can be viewed as a generalization of the one developed by Kapetanios et al. (2003) (the KSS test) by incorporating stationary covariates. The asymptotic distribution of the test is derived and the asymptotic critical values are tabulated. A set of Monte Carlo simulations show that our test generally achieves large power improvements over the KSS test. An illustrated empirical application indicates that our proposed test is able to unveil more evidence than the KSS test in favor of no unit root of real exchange rates in 15 Asian countries.     

EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?*

A recursive test procedure is suggested that provides a mechanism for testing explosive behavior, date stamping the origination and collapse of economic exuberance, and providing valid confidence intervals for explosive growth rates. The method involves the recursive implementation of a right‐side unit root test and a sup test, both of which are easy to use in practical applications, and some new limit theory for mildly explosive processes. The test procedure is shown to have discriminatory power in detecting periodically collapsing bubbles, thereby overcoming a weakness in earlier applications of unit root tests for economic bubbles. An empirical application to the Nasdaq stock price index in the 1990s provides confirmation of explosiveness and date stamps the origination of financial exuberance to mid‐1995, prior to the famous remark in December 1996 by Alan Greenspan about irrational exuberance in the financial market, thereby giving the remark empirical content.

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Investigating regional house price convergence in the United States: Evidence from a pair-wise approach

In this paper we examine long-run house price convergence across US states using a novel econometric approach advocated by Pesaran (2007) and Pesaran et al. (2009). Our empirical modelling strategy employs a probabilistic test statistic for convergence based on the percentage of unit root rejections among all state house price differentials. Using a sieve bootstrap procedure, we construct confidence intervals and find evidence in favour of convergence. We also conclude that speed of adjustment towards long-run equilibrium is inversely related to distance.     

An augmented autoregressive distributed lag bounds test for cointegration

An augmented autoregressive distributed lag (ARDL) bounds test for cointegration involves an extra F-test on the lagged levels of the independent variable(s) in the ARDL equation. Originally, this testing strategy was introduced using the bootstrap procedure. This paper provides both the small sample and asymptotic critical values for easier implementation of the test, making it applicable for a broader range of researchers. Two advantages of this augmented ARDL bounds test are that the assumption of an I(1) dependent variable is not necessary, and a clear conclusion on the cointegration status is provided by the three tests. The augmented ARDL bounds test is demonstrated using an empirical study on government taxation and expenditures. The tests support the tax-and-spend hypothesis of the budgetary policy for the US, the UK, and France.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Testing for Granger causality in distribution tails: An application to oil markets integration

This paper proposes an original procedure which allows for testing of Granger-causality for multiple risk levels across tail distributions, hence extending the procedure proposed by Hong et al. (2009). Asymptotic and finite sample properties of the test are considered. This new Granger-causality framework is applied for a set of regional oil markets series. It helps to tackle two main questions 1) Whether oil markets are more or less integrated during periods of extreme energetic prices movements and 2) Whether price-setter markets change during such periods. Our findings indicate that the integration level between crude oil markets tends to decrease during extreme periods and that price-setter markets also change. Such results have policy implication and stress the importance of an active energetic policy during episode of extreme movements.     

Inflation Dynamics in a Small Open Economy

We evaluate the empirical performance of forward‐looking models for inflation dynamics in a small open economy. Using likelihood‐based testing procedures, we find that the exact formulation is at odds with Norwegian data. Moreover, some of the parameters in the model are not well identified. We also find that the inexact formulation is not rejected statistically using a test based on a minimum distance method. However, confidence regions also reveal an identification problem with this model. Instead, we find a well‐specified backward‐looking model with imperfect competition underlying the price setting, which is a model that outperforms an alternative forward‐looking model in‐sample. The backward‐looking model also forecasts somewhat better than the alternative forward‐looking model, during and after the recent financial crisis.     

Fourier ADL cointegration test to approximate smooth breaks with new evidence from Crude Oil Market

Economic data is typically subject to a number of different forms of structural breaks. Ignoring structural breaks in a model can lead to misspecification issues and false conclusions. This paper proposes a new Autoregressive Distributive Lag (ADL) cointegration test in the presence of nonlinear breaks approximated by a Fourier function. The test offers a simple way to capture smooth structural change in time series data. Exact break dates are not required, and the suggested methodology can accommodate unknown number and form of gradual structural change. The testing procedure circumvents the potential power loss which can result from adding more dummy variables in the testing equation. Simulation results show that our procedure has good size and power properties. We demonstrate our test on the empirical example of real oil prices, oil production, and real economic activity, which are subject to structural breaks. The new test suggests that variables are cointegrated, while a conventional ADL test ignores structural breaks and concludes the opposite. This result casts some doubt on conventional oil price models.     

A simple and effective misspecification test for the double-hurdle model

The commonly-used version of the double-hurdle model rests on a rather restrictive set of statistical assumptions, which are very seldom tested by practitioners, mainly because of the lack of a standard procedure for doing so, although violation of such assumptions can lead to serious modelling flaws. We propose here a bootstrap-corrected conditional moment portmanteau test which is simple to implement and has good size and power properties.     

A CUSUM test for a long memory heterogeneous autoregressive model

A CUSUM test is proposed for testing structural breaks in a long-memory heterogeneous autoregressive model. The limiting distribution of the CUSUM test is shown to be a simple function of a standard Brownian bridge, contrasting with the nuisance parameter dependent asymptotics of other CUSUM tests based on fractional integration models. A Monte-Carlo experiment investigates finite sample size and power of the test. The proposed test is applied to a set of daily realized volatilities of the log-return of the Korean Won US Dollar exchange rate to reveal some evidence of a break in addition to a long-memory.     

Some cautions on the use of nonlinear panel unit root tests: Evidence from a modified series-specific non-linear panel unit-root test

The purpose of this paper is to examine the relevance of applying nonlinear panel unit root test to examine the non-linear mean reversion behaviors of real exchange rates. We find that nonlinear panel unit root test may achieve lower power performance as compared to its alternative of linear panel unit test when the data generating process does not contain significant non-linear components. This finding post cautions to researchers in modeling and testing real exchanges behavior. We also develop a modified series-specific nonlinear panel unit root test and find evidence in favor of purchasing power parity hypothesis for China's four ASEAN trading partners in the period of February 1997 to August 2009.     

Tests for structural change,aggregation, and homogeneity

Structural change can be considered by breaking up a sample into subsets and asking if these can be aggregated or pooled. Strategies for constructing tests for aggregation and structural change in this setting have not received sufficient attention in the literature. Our methodology for testing generalizes to multiple regimes a discussion of Pesaran et al. (1985) for the case of two regimes. This treatment permits a unified approach to a large number of testing problems discussed separately in the literature, as special cases or as parts of a test of homogeneity. We also provide a simple alternative to much more complex testing strategies currently being researched and developed in testing for structural change.