An empirical model for the Turkish trade balance: new evidence from ARDL bounds testing analyses

In this paper, the determinants of the Turkish trade balance are tried to be analyzed in an empirical modelling approach. For this purpose, the contemporaneous ARDL-based bounds testing has been used to examine the existence of a long run co-integration relationship between the variables of our interest. The estimation results indicate that real exchange rate depreciations improves the trade balance in a strong and significant way, that domestic real income affects the trade balance negatively, and that trade balance is strongly improved due to an increase in foreign real income. No significant effect of crude oil prices can be observed on trade balance. The error correction modeling gives results in line with the long run findings of the co-integration analysis.Trade Balance; ARDL Bounds Testing Approach; Turkish Economy;

An Empirical Model for the Turkish Trade Balance: New Evidence from ARDL Bounds Testing Analyses

In this paper, the determinants of the Turkish trade balance are tried to be analyzed in an empirical modelling approach. For this purpose, the contemporaneous ARDL-based bounds testing has been used to examine the existence of a long run co-integration relationship between the variables of our interest. The estimation results indicate that real exchange rate depreciations improves the trade balance in a strong and significant way, that domestic real income affects the trade balance negatively, and that trade balance is strongly improved due to an increase in foreign real income. No significant effect of crude oil prices can be observed on trade balance. The error correction modeling gives results in line with the long run findings of the co-integration analysis.Trade Balance, ARDL Bounds Testing Approach, Turkish Economy

Analysing the Trade-GDP Nexus in Iran: A Bounds Testing Approach

This paper examines the major sources of economic growth in Iran using annual time series data (1960 to 2003). The time series properties of the data are analysed by Perron’s innovational outlier and additive outlier models. The empirical results based these models show that there is not enough evidence against the null hypothesis of unit root for all of the variables under investigation. Moreover, we found that the most significant structural breaks over the last four decades which have been detected endogenously in fact correspond to the regime change (e.g the 1979 Islamic revolution) and the Iraqi war in the 1980s. Finally, an ARDL methodology is employed to obtain the short and long-term determinants of economic growth. The results show that while the effects of gross capital formation and oil exports are highly significant, as expected, non-oil exports and human capital have an even smaller effect than had been anticipated.structural break, unit root tests, ARDL method, Iranian economy

Crime and economic conditions in Malaysia: An ARDL Bounds Testing Approach

Economists recognized that economic conditions have an impact on crime activities. In this study we employed the Autoregressive Distributed Lag (ARDL) bounds testing procedure to analyze the impact of economic conditions on various categories of criminal activities in Malaysia for the period 1973-2003. Real gross national product was used as proxy for economic conditions in Malaysia. Our results indicate that murder, armed robbery, rape, assault, daylight burglary and motorcycle theft exhibit long-run relationships with economic conditions, and the causal effect in all cases runs from economic conditions to crime rates and not vice versa. In the long-run, strong economic performances have a positive impact on murder, rape, assault, daylight burglary and motorcycle theft, while on the other hand, economic conditions have negative impact on armed robbery.Bounds Testing; Malaysia; Crime

Consecutive Sequential Probability Ratio Tests of Multiple Statistical Hypotheses

In this paper, we develop a simple approach for testing multiple statistical hypotheses based on the observations of a number of probability ratios enumerated consecutively with respect to the index of hypotheses. Explicit and tight bounds for the probability of making wrong decisions are obtained for choosing appropriate parameters for the proposed tests. In the special case of testing two hypotheses, our tests reduce to Wald's sequential probability ratio tests.Comment: 29 pages, no figure; The main results of this paper have appeared in Proceedings of SPIE Conferences, Baltimore, Maryland, April 24-27, 201

Goodness-of-fit testing and quadratic functional estimation from indirect observations

We consider the convolution model where i.i.d. random variables $X_i$ having unknown density $f$ are observed with additive i.i.d. noise, independent of the $X$'s. We assume that the density $f$ belongs to either a Sobolev class or a class of supersmooth functions. The noise distribution is known and its characteristic function decays either polynomially or exponentially asymptotically. We consider the problem of goodness-of-fit testing in the convolution model. We prove upper bounds for the risk of a test statistic derived from a kernel estimator of the quadratic functional $\int f^2$ based on indirect observations. When the unknown density is smoother enough than the noise density, we prove that this estimator is $n^{-1/2}$ consistent, asymptotically normal and efficient (for the variance we compute). Otherwise, we give nonparametric upper bounds for the risk of the same estimator. We give an approach unifying the proof of nonparametric minimax lower bounds for both problems. We establish them for Sobolev densities and for supersmooth densities less smooth than exponential noise. In the two setups we obtain exact testing constants associated with the asymptotic minimax rates.Comment: Published in at http://dx.doi.org/10.1214/009053607000000118 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Testing Export-led Growth Hypothesis in Kenya: An ADRL Bounds Test Approach

Over the years, there has been extensive research on the relationship between a country’s export and economic growth with ambiguous and mixed results. Instead of using the conventional cointegration approach, this paper re-examines the export-led growth hypothesis for Kenya using autoregressive distributed lag (ADRL) bounds technique. This approach is capable of testing for the existence of a long-run relationship regardless of whether the underlying time series are individually I(1) or I(0). This enhances the stability and robustness of our results. In addition, we examine the Granger causality between exports and economic growth over the sample period. The results indicate that there exists a long-term relationship between GDP growth and exports, and it is unidirectional, running from exports to GDP growth. Hence, in the case of Kenya, export enhancing policies are recommended in promoting and sustaining economic growth.Exports; economic growth and causality

Occam's hammer: a link between randomized learning and multiple testing FDR control

We establish a generic theoretical tool to construct probabilistic bounds for algorithms where the output is a subset of objects from an initial pool of candidates (or more generally, a probability distribution on said pool). This general device, dubbed "Occam's hammer'', acts as a meta layer when a probabilistic bound is already known on the objects of the pool taken individually, and aims at controlling the proportion of the objects in the set output not satisfying their individual bound. In this regard, it can be seen as a non-trivial generalization of the "union bound with a prior'' ("Occam's razor''), a familiar tool in learning theory. We give applications of this principle to randomized classifiers (providing an interesting alternative approach to PAC-Bayes bounds) and multiple testing (where it allows to retrieve exactly and extend the so-called Benjamini-Yekutieli testing procedure).Comment: 13 pages -- conference communication type forma

A smooth entropy approach to quantum hypothesis testing and the classical capacity of quantum channels

We use the smooth entropy approach to treat the problems of binary quantum hypothesis testing and the transmission of classical information through a quantum channel. We provide lower and upper bounds on the optimal type II error of quantum hypothesis testing in terms of the smooth max-relative entropy of the two states representing the two hypotheses. Using then a relative entropy version of the Quantum Asymptotic Equipartition Property (QAEP), we can recover the strong converse rate of the i.i.d. hypothesis testing problem in the asymptotics. On the other hand, combining Stein's lemma with our bounds, we obtain a stronger (\ep-independent) version of the relative entropy-QAEP. Similarly, we provide bounds on the one-shot \ep-error classical capacity of a quantum channel in terms of a smooth max-relative entropy variant of its Holevo capacity. Using these bounds and the \ep-independent version of the relative entropy-QAEP, we can recover both the Holevo-Schumacher-Westmoreland theorem about the optimal direct rate of a memoryless quantum channel with product state encoding, as well as its strong converse counterpart.Comment: v4: Title changed, improved bounds, both direct and strong converse rates are covered, a new Discussion section added. 20 page