Relationship Between Government Size and Trade Openness: Evidence from Pakistan

In this paper the effect of trade openness on government’s role in the economy is investigated in Pakistan using data for the period 1947–2009. The results demonstrate that there is a significant positive association between trade openness and government size. The explanation appears that as openness increases the size of government will inflate. This association is robust to the inclusion of a wide range of control variables and model specifications. The results support the compensation hypothesis and imply that government consumption plays a risk-reducing role in Pakistan. The empirical findings also highlight the role of other variables in determining the government size. Government size increases with income, democracy, foreign debt and investment, while it decreases with the increase in inflation and urbanization.     

Augmented Box-Behnken Designs for Fitting Third-Order Response Surfaces

Box-Behnken designs are popular with experimenters who wish to estimate a second-order model, due to their having three levels, their simplicity and their high efficiency for the second-order model. However, there are situations in which the model is inadequate due to lack of fit caused by higher-order terms. These designs have little ability to estimate third-order terms. Using combinations of factorial points, axial points, and complementary design points, we augment these designs and develop catalogues of third-order designs for 3–12 factors. These augmented designs can be used to estimate the parameters of a third-order response surface model. Since the aim is to make the most of a situation in which the experiment was designed for an inadequate model, the designs are clearly suboptimal and not rotatable for the third-order model, but can still provide useful information.     

A new class of ratio-type estimators for improving mean estimation of nonsensitive and sensitive variables by using supplementary information

ABSTRACT

Motivated by some recent improvements for mean estimation in finite sampling theory, we propose, in a design-based approach, a new class of ratio-type estimators. The class is initially discussed on the assumption that the study variable has a nonsensitive nature, meaning that it deals with topics that do not generate embarrassment when respondents are directly questioned about them. Under this standard setting, some estimators belonging to the class are shown and the bias, mean square error and minimum mean square error are determined up to the first-order of approximation. The class is subsequently extended to the case where the study variable refers to sensitive issues which produce measurement errors due to nonresponses and/or untruthful reporting. These errors may be reduced by enhancing respondent cooperation through scrambled response methods that mask the true value of the sensitive variable. Hence, four methods (say the additive, multiplicative, mixed and combined additive-multiplicative methods) are discussed for the purposes of the article. Finally, a simulation study is carried out to assess the performance of the proposed class by comparing a number of competing estimators, both in the sensitive and the nonsensitive setting.     

A new approach to choose acceptance cutoff for approximate Bayesian computation

The approximate Bayesian computation (ABC) algorithm is used to estimate parameters from complicated phenomena, where likelihood is intractable. Here, we report the development of an algorithm to choose the tolerance level for ABC. We have illustrated the performance of our proposed method by simulating the estimation of scaled mutation and recombination rates. The result shows that the proposed algorithm performs well.     

Structural Decomposition of Exchange Rate Shocks in Pakistan: An Empirical Investigation using SVAR Methodology

This study is an attempt to decompose nominal and real exchange rate movements in Pakistan into components induced by real and nominal shocks. Using the Blanchard and Quah (1989), famously known as B-Q, methodology for the period 2000:1–2009:12, the study finds that real shocks affect both nominal and real exchange rate. Similarly, the nominal shocks have a permanent effect on nominal exchange rate but have temporary effect on real exchange rate. Moreover, speed of convergence is different between the two rates affected by the same shock. These results put forward that nominal devaluation is not followed by real devaluation and resultantly will not improve trade balance situation. The policy makers, therefore, need to be careful while taking any decision regarding nominal devaluation as it may also lead to worsening of, instead of improvement in, trade balance.     

Zero Lower Bound and negative interest rates: Choices for monetary policy in the UK

There have been relatively few analyses of the policy context and consequences of a Zero Lower Bound (ZLB) for nominal interest rates. This paper sets out monetary policy alternatives, including negative interest rates, a revision of the inflation target, and rendering unconventional policy instruments such as QE conventional (permanent). Following extensive discussion of policy options, we set out a model that explores the impacts of the real policy rate on economic growth, employment and inflation, with particular attention to the British economy. We use a Time-Varying Structural Vector Auto-regressive (TVSVAR) Model where the sources of time variation are both the coefficients and variance–covariance matrix of the innovations. It was found that real rates have significant implications for real growth, the labour market and price stability even when monetary policy was constrained at the ZLB in nominal terms. The study additionally applies a discrete break in the data to focus on the Post-Global Financial Crisis and ZLB period. This indicates that the effectiveness of real rates did not diminish and this has important implications in terms of a policy approach which seeks to exploit real negative rates.     

Robust Multivariate Outlier Labeling

A criterion for robust estimation of location and covariance matrix is considered, and its application in outlier labeling is discussed. This method, unlike the methods based on MVE and MCD, is applicable to large and high-dimension data sets. The method proposed here is also robust and has the same breakdown point as the MVE- and MCD-based methods. Furthermore, the computational complexity of the proposed method is significantly smaller than that of other methods.     

Multivariate Process Variability Monitoring

Understanding multivariate variability is a difficult task because there is no single measure that can be properly used. This article presents a new measure that features good properties. If this measure is simultaneously used with generalized variance, it will give a better understanding of multivariate variability. It can also efficiently be used for large data sets with high dimensions. Furthermore, when it is used for constructing a Shewhart-type chart to monitor multivariate variability, the resulting chart has a much better out-of-control ARL than the generalized variance chart. An example illustrates its advantage.     

Bayes Estimation of Weibull Distribution Parameters Using Ranked Set Sampling

Estimation of the parameters of Weibull distribution is considered using different methods of estimation based on different sampling schemes namely, Simple Random Sample (SRS), Ranked Set Sample (RSS), and Modified Ranked Set Sample (MRSS). Methods of estimation used are Maximum Likelihood (ML), Method of moments (Mom), and Bayes. Comparison between estimators is made through simulation via their Biases, Relative Efficiency (RE), and Pitman Nearness Probability (PN). Estimators based on RSS and MRSS have many advantages over those that are based on SRS.