Econometrics

More than 78% of Bangladesh's export earnings come from the garment industry. The ready-made garments (RMG) sector has a greater potential than any other sector in terms of employment and foreign exchange earnings to reduce poverty and make a contribution to the national economy. In this paper, we attempt an econometric estimation of Cobb-Douglas production function in Bangladesh's ready-made garments industry and also test the hypothesis that ready-made garments industry is expected to reap better economies of scale in a competitive environment. We try to resolve some econometric issues such as heteroscedasticity, autocorrelation and multicollinearity in the production function estimates by adopting different solutions and adjustment procedures with a view to obtain reliable parameter estimates. . This study has been used the annually time series data about ready-made garments production, labour wages, and capital of Tex-Town Group Limited from 2002-2013. Ordinary Least Square (OLS) method is used to estimate the model. The results show that there is a positive relationship between labour wages, capital and ready-made garments production in Bangladesh. The value of R2 is 0.687. This indicates that about 68.7% of the total variation in the ready-made garments production is explained by the labour wages and capital in Bangladesh. The results are statistically significant at 5% level of significance. This study suggests that increase in labour employment and capital to increase the production of ready-made garments in Bangladesh.

The goal of this research is to examine the effect of income smoothing on the cost of debt and the credit rating. The statistic community is the accepted firms in Tehran's stock exchange during 1385-1389, that the statistic sample has chosen from them. Also in this research we used Jones' modified model for measuring the discretionary accruals (measure of income smoothing). And we used regression analysis for testing the research hypothesis. The results of this research show that there is significant and negative relation between income smoothing and cost of debt. And also there is significant and direct relation between income smoothing and firm's credit rating.

Because of the Budget Control Act, budget enforcement procedures known as sequestration will commence January 2013 unless Congress and the Obama Administration act otherwise. The sequester requires cuts in discretionary spending in order to achieve $1.2 trillion in savings from 2013-2021. When compared to 2011 spending levels, this will lead to a cut of 8.8 percent (or $12.5 billion) of federally funded research and development (R&D) in 2013 with similar cuts in the following years.1 This cut to R&D expenditures will affect all government agencies, including the Department of Defense, the National Science Foundation the National Institutes of Health, the Department of Energy, and NASA.

This paper examines the behavior of the interest rate risk management measures for bonds with embedded options and studies factors it depends on. The contingent option exercise implies that both the pricing and the risk management of bonds requires modelling future interest rates. We use the Ho and Lee (HL) and Black, Derman, and Toy (BDT) consistent interest rate models. In addition, specific interest rate measures that consider the contingent cash-flow structure of these coupon-bearing bonds must be computed. In our empirical analysis, we obtained evidence that effective duration and effective convexity depend primarily on the level of the forward interest rate and volatility. In addition, the higher the interest rate change and the lower the volatility, the greater the differences in pricing of these bonds when using the HL or BDT models.

This project is dedicated to my family. Without their encouragement, understanding, support, and unconditional love, completion of this study could not have been possible. appreciation also goes to my moderator and chairman for their invaluable efforts to perfect this work. Further, I am grateful to my colleagues in The University of Nairobi, whose assistance to this research project cannot be overlooked, for their inspirations, encouragements, guidance and helpful recommendations concerning the procedures through academic discussions and always raising the benchmark.

Statistically, change point is the location or the time point such that observations follow one distribution up to the point and then another afterwards. Change point problems are encountered in our daily life and in disciplines such as economics, finance, medicine, geology, Literature among others. In this paper, the power of the likelihood ratio tests for a change point in binomial observations whose mean is dependent on the explanatory variables was investigated. Artificial neural network technique was used to estimate the conditional means. It was shown through simulation that the power of the test increases as the size of sample. The test was found to have less power when the change point was near the edges than when the change point is at the centre. The test was more likely to detect a change if the magnitude of the change was large.

We implement momentum strategies using reward-risk measures as ranking criteria based on classical tempered stable distribution. Performances and risk characteristics for the alternative portfolios are obtained in various asset classes and markets. The reward-risk momentum strategies with lower volatility levels outperform the traditional momentum strategy regardless of asset class and market. Additionally, the alternative portfolios are not only less riskier in risk measures such as VaR, CVaR and maximum drawdown but also characterized by thinner downside tails. Similar patterns in performance and risk profile are also found at the level of each ranking basket in the reward-risk portfolios. Higher factor-neutral returns achieved by the reward-risk momentum strategies are statistically significant and large portions of the performances are not explained by the Carhart four-factor model.

calculation of forecasts using the ARDL/Bounds Testing Methodology proposed by Pesaran et al.,. We will only adopt two variables: The high daily exchange rate Y t as a dependent variable and the opening price X t . Why is this choice? Since the opening price is known, it will be used as an exogenous variable, while Y t will be considered as an endogenous variable. In the end, the parameters stability of the Error Correction Model will be tested through Chow Predictive Test and the use of recursive residuals to effectuate Cumulative Sum (CUSUM). The research has three objectives a) Is there long-term equilibrium between the variables Y t and X t ? b) Is there co-integration between these two variables? c) Is it possible to have excellent forecasts under the ARDL model with co-integration? The methodology of the research depends first on the analytical statistical approach to the time series, and on the technique of cointegration according to the ARDL/Bounds Testing Methodology proposed by Pesaran et al. [4]. The unexpected sudden fluctuations in the EURO-dollar exchange rate result from sudden economic and political shocks causing significant financial losses or gains. Does the model envisaged in this study allow a safety cover to avoid or, at least, reduce losses? Or does it allow an increase in the probability of obtaining significant profits or at least no losses?

This paper studies how the views of sophisticated traders are impounded into stocks and bonds around macroeconomic news announcements. I find evidence that sophisticated traders trade on predictions of macroeconomic news reports before announcements and obtain their informational advantage using public information. Specifically, consensus forecasts of upcoming data releases suffer from anchoring bias and overweight past data releases. By correcting this bias, sophisticated traders can predict news reports. The results suggest that stock and bond markets are inefficient in this setting. Over time, there is a late trading puzzle: sophisticated traders can predict news reports days before announcements but appear to trade these predictions into stock and bond prices just hours before announcements. Across assets there is a related puzzle: the predictable component of news reports is eventually fully impounded into bonds but only partially impounded into stocks. Stocks but not bonds rea...

In this study we aim to identify and measure the external returns to human capital in Brazil using information from the Ministry of Labor’s Annual Social Information Report (RAIS) on all urban agglomeration in the country for the period 2002-2014. With a two step estimation and using instrumental variables for identification we find a considerable effect of local human capital concentration on local wages. The set of results indicates that, in the case of brazilian labor market areas, the external returns to human capital are a 0.86% increase in local wages for an increase of one percentage point in college graduates. Consistent with theoretical expectations and similar to literature results, we also found that the impact of local human capital is stronger for unskilled than for skilled workers. Finally, results are robust and not equally distributed across sectors

This study checks the hypothesis that sustainable well-being is a determinant factor of fertility through the application of a multiversal method based on the assumptions of Vibration of Effects (VoE) model of multiversal sampling on the Italian Equitable and Sustainable Well-being (BES) dataset. For 48 social indicators of well-being, adopting the Province as statistical unit, the estimate of linear effect has been systematically sampled 1,024 times. This multiversal sampling allows to estimate the component of the hypothetical variability of the estimate of the effect size that depends on the theoretical choices of the analysis. This component is commonly underestimated, hidden, or ignored in regular multivariate analysis or classical bootstrapping. Results do not allow to claim that any of the 48 social indicators is a robust linear predictor of fertility. Indicators associated with high statistical significance are also at risk of misleading the causal relationship. Implications for policy-making and the future direction of the research on BES are discussed in the conclusive remarks.

This study checks the hypothesis that sustainable well-being is a determinant factor of fertility through the application of a multiversal method based on the assumptions of Vibration of Effects (VoE) model of multiversal sampling on the Italian Equitable and Sustain-able Well-being (BES) dataset. For 48 social indicators of well-being, adopting the Province as statistical unit, the estimate of linear effect has been systematically sampled 1,024 times. This multiversal sampling allows to estimate the component of the hypothetical variability of the estimate of the effect size that depends on the theoretical choices of the analysis. This component is commonly underestimated, hidden, or ignored in regular multivariate analysis or classical bootstrapping. Results do not allow to claim that any of the 48 social indicators is a robust linear predictor of fertility. Indicators associated with high statistical significance are also at risk of misleading the causal relationship. Implication...

We examine a simple measure of portfolio performance based on prospect theory, which captures not only risk and return but also reflects differential aversion to upside and downside risk. The measure we propose is a ratio of gains to losses, with the gains and losses weighted (if desired) to reflect risk-aversion for gains and risk-seeking for losses. It can also be interpreted as the weighted ratio of the value of a call option to a put option, with the benchmark as the exercise price. When applying the loss-aversionperformance measure to closed-end funds, we find that it gives significantly different rankings from those of conventional measures (such as the Sharpe ratio, Jensen's alpha, the Sortino ratio, and the Higher Moment measure), and gives the expected signs for the odd and even moments of tracking errors. However, loss-aversion performance is not more closely related to discounts on funds than are the conventional performance measures, so we have not found evidence that loss-aversion attracts investors to particular funds in the short-term.

This thesis builds on the paper of Reinhart and Rogoff, named 'Growth in a Time of Debt'. However, this study is focused on Western Countries. It uses another database than the one analyzed by Reinhart and Rogoff. This thesis starts with a simple analysis similar to that of the above mentioned authors. Subsequently, the research is extended by using Panel Data Models. The influence of debt on annual GDP growth is examined. Tax revenues, government expenditure, inflation and real interest rate are taken into account. This is done for an unbalanced panel, a balanced panel and a panel without outliers. The general conclusion is that based on this study it cannot be determined whether debt has a significant impact on the annual GDP growth. All other added variables have a regression coefficient which is expected based on economic theory.

This study aims to analyze the influence of macro economy between composite stock prices index. The dependent variable in this study was represented by the composite stock price index, while the independent variables consist of exchange rate, interest rate, inflation and oil price. The sampling used in this research observation period were 6 years during 2010-2015, then the data was analyzed by Multiple Linear Regression Test. The results of this study indicate that there were three variables that affect the composite stock price index, it’s a exchange rate, interest rate and oil prices, while inflation have no effect on composite stock price index. Anova test result indicates exchange rate, interest rate, inflation and oil price simultaneously influence the composite stock price index. The test result show that these variables have significant positive impact on stock prices, i.e 0,000.

Exchange rate is known as the one of the crucial element for the development of the economy, it is seen that volatility hassignificant influence on the international trade. In this paper analyzed that impact of inflation rate and money supply onthe volatility of exchange rate of India. We collected the monthly data to estimate the short and long run relationshipbetween these variables. For this purpose, we have applied theJohansen Cointegration, VECM, GCT and IRF for analysisthe response of different shocks on the variables. This paper is showing that high money supply and interest rate increasethe inflation rate, which leads to increase the volatility in exchange rate on India.

The multicollinearity and error-prone variables in linear regression models cause problems in parameter estimation in that they both impair the estimation and statistical analysis. Consideration of both problems simultaneously has shown that the error-prone variables mask the multicollinearity. The variance inflation factor has been proven to be the most common diagnostic tool for multicollinearity. This paper theoretically gives valuable information on the variance inflation factor that it decreases as the reliability ratio decreases. The existence of the explanatory variables with measurement error affects the parameter estimation attenuated toward zero and the same time camouflage multicollinearity seriously as if there was no multicollinearity among the explanatory variables. This has been supported by a simulation study with two explanatory variables.

According to general asset pricing theory, options should reward their holders for the systematic risk they are bearing. In this paper, we study the returns of foreign exchange options. We find that, by sorting options according to the distance of their implied volatility from the historical volatility, we obtain portfolios with positive average monthly returns. These returns are not explained by standard aggregate risk factors, which suggest either that additional risk factors should be accounted for, or that investors behavior differs from the traditional paradigm of rational agents.

This paper presents the integration of principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to assess the impact of bad loans on technical efficiency of banks. Bad loans or non-performing loans (NPLs) include past due loans, bankrupt and quasi-bankrupt assets and doubtful assets. Bad loans are considered as a bad output for calculation of the technical efficiency through PCA. ANFIS is used to model the relationship between bad loans and technical efficiency. ANFIS modeling is used to capture the nonlinearity and fuzziness existed in the modeling environment. In the ANFIS model, technical efficiency is considered as the output which is modeled with respect to bad loans, profit and costs. The results of the proposed model are illustrated through a case study in Iranian governmental banks. It is evidenced that the effects of bad loans on technical efficiency of banks are not linear but a nonlinear negative impact.

This paper presents the integration of principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to assess the impact of bad loans on technical efficiency of banks. Bad loans or non-performing loans (NPLs) include past due loans, bankrupt and quasi-bankrupt assets and doubtful assets. Bad loans are considered as a bad output for calculation of the technical efficiency through PCA. ANFIS is used to model the relationship between bad loans and technical efficiency. ANFIS modeling is used to capture the nonlinearity and fuzziness existed in the modeling environment. In the ANFIS model, technical efficiency is considered as the output which is modeled with respect to bad loans, profit and costs. The results of the proposed model are illustrated through a case study in Iranian governmental banks. It is evidenced that the effects of bad loans on technical efficiency of banks are not linear but a nonlinear negative impact.

One strand of business cycle research focuses on the analysis of the comovement of business cycle variables at the aggregate and sectoral levels. Following this line of research, this article examines the volatility and inter‐industry comovement of output in the context of India for the post‐liberalisation period, that is, April 1992–December 2019. The study employs the dynamic conditional correlation‐multivariate generalised autoregressive conditional heteroscedasticity framework and thereby estimates the conditional variance and the time‐varying correlations. The results indicate that the volatilities of the capital goods sector and consumer durables sector relative to that of the aggregate industrial sector are higher, suggesting a higher amplitude of growth rates in these sectors. The conditional correlation estimates reveal that the use‐based sectors are positively correlated with the aggregate industry, and the capital goods sector has the highest correlation with the aggregat...

This paper tests a model in which responses to disease risks can be "fatalistic": higher risk beliefs can lead to more risk-taking rather than less. This occurs because high risk beliefs raise the perceived chance that you are already infected, lowering the marginal cost of risk-taking. I test for fatalism by randomly providing information about the true (low) average risk of HIV transmission in Malawi. Just as the model predicts, the treatment causes sexual activity to rise slightly on average but decline sharply for people with high initial risk beliefs-especially those with high baseline levels of sexual activity.

This paper demonstrates the acute sensitivity of education program effectiveness to the choices of inputs and outcome measures, using a randomized evaluation of a mother-tongue literacy program. The program raises reading scores by 0.64SDs and writing scores by 0.45SDs. A reduced-cost version instead yields statistically-insignificant reading gains and some large negative effects (-0.33SDs) on advanced writing. We combine a conceptual model of education production with detailed classroom observations to examine the mechanisms driving the results; we show they could be driven by the program initially lowering productivity before raising it, and potentially by missing complementary inputs in the reduced-cost version.

This paper reviews the use of the traditional ARDL and the ARDL approach to cointegration for the analysis of short-run dynamic and long run relationship when series are difference stationary (series can be integrated of different orders). The two models were used to estimate the short-run dynamics and the long run relationships between selected Nigeria's macroeconomic series. The results compares favorably with the theory that the ARDL is equivalent to the short-run dynamics of the error correction model (the resultant model from the ARDL approach to cointegration).

The growth of an economy is determined largely by the growth of its Gross Domestic Product (GDP) over time. However, GDP and some economic series are characterized by nonstationarity, structural breaks and outliers. Many attempts have been made to analyze these economic series assuming unit root process even in the presence of changes in the mean level without considering possible fractional integration. This paper aims at examining the structural breaks and nonstationarity in the GDP series of some selected African countries with a view to determining the influence of structural breaks on the level of stationarity of these series. These series are found to be nonstationary with some evidence of long memory. They were found to experience one or more breaks over the years and this may be due to instability in the government and economic policies in the selected African countries. The measure of relative efficiency shows that autoregressive fractional integrated moving average (ARFIMA) models is better than the corresponding autoregressive integrated moving average (ARIMA) models for the series considered in this study.

This paper reviews the use of the traditional ARDL and the ARDL approach to cointegration for the analysis of short-run dynamic and long run relationship when series are difference stationary (series can be integrated of different orders). The two models were used to estimate the short-run dynamics and the long run relationships between selected Nigeria’s macroeconomic series. The results compares favorably with the theory that the ARDL is equivalent to the short-run dynamics of the error correction model (the resultant model from the ARDL approach to cointegration).

This paper examined the macroeconomic determinants of inflation in Ghana using a cointegration approach. The main purpose of the paper is to investigate whether population growth, foreign direct investment, foreign aid, agricultural and service's output have a significance influence on the inflationary situations experienced in Ghana over the study period. The method of analysis was the cointegration analysis. The stationarity properties of all the variables of interest were checked and established. All the variables under consideration were found to be integrated of order one, that is, I (1). Johansen cointegration approach showed that there is both long and short run relationship among the variables; therefore, the vector error correction model was estimated. The study identified population growth, foreign direct investment, foreign aid, and service's output as major long run determinants of inflation in Ghana.

This paper presents a novel approach to time series forecasting, an area of significant importance across diverse fields such as finance, meteorology, and industrial production. Time series data, characterized by its complexity involving trends, cyclicality, and random fluctuations, necessitates sophisticated methods for accurate forecasting. Traditional forecasting methods, while valuable, often struggle with the non-linear and non-stationary nature of time series data. To address this challenge, we propose an innovative model that combines signal decomposition and deep learning techniques. Our model employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) for learning the volatility in time series changes, followed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for data decomposition, significantly simplifying data complexity. We then apply Graph Convolutional Networks (GCN) to effectively learn the features of the decomposed data. The integration of these advanced techniques enables our model to fully capture and analyze the intricate features of time series data at various interval lengths. We have evaluated our model on multiple typical time-series datasets, demonstrating its enhanced predictive accuracy and stability compared to traditional methods. This research not only contributes to the field of time series forecasting but also opens avenues for the application of hybrid models in big data analysis, particularly in understanding and predicting the evolution of complex systems.

The intermediation provided by banks depends on the efficiency of the bank. Commercial banks in South Sudan have continued to experience poor performance. This has been a significant source of concern for the South Sudan's financial sector over the years. Studies on financial performance and macroeconomic factors have largely not been based on South Sudan Banks. The general objective of the study was to establish the effect of selected macro-economic variables on financial performance of commercial banks in South Sudan. The study adopted explanatory research design. A census of 29 commercial banks listed in South Sudan that were in operation from 2012 to 2020 was targeted. The audited financial statements of the listed commercial banks served as the source of secondary panel data. Data collection was by use of a document review guide. Data analysis for the study was by use of regression model and correlation. The study was based on Vector autoregressive (VAR) model to assess relationship between macro-economic variables and financial performance of commercial banks. From the findings of the study, it was established that interest rate has no significant effect on financial performance of commercial banks in South Sudan; Gross Domestic Product has no significant effect on financial performance of commercial banks in South Sudan; inflation rate has no significant effect on financial performance of commercial banks in South Sudan and exchange rate has no significant effect on financial performance of commercial banks in South Sudan. Macroeconomic variables such as interest rate Gross domestic product, inflation rate and exchange rate. The study suggested that commercial banks' management committees continuously monitor inflation rates in order for them to adjust their loan products and services in line with the inflation rate; commercial banks should design dynamic interest rate policies that would lead to growth by attracting many customers; and commercial banks should ensure that the business environment is favorable in order to encourage investment in them since they have the ability to offer a variety of financial services.

В статье рассматриваются недостатки использования зарубежных моделей диагностики риска банкротства для прогнозирования банкротства предприятий авиационно-промышленного комплекса (авиапредприятий), а также обосновываются преимущества использования логистических регрессионных моделей (logit-модели) по сравнению с моделями, построенными с помощью множественного дискриминантного анализа (MDA-модели). Разрабатывается logit-модель для диагностики риска банкротства авиапредприятий.

Авторы: Василий Юрьевич Жданов НИУ ВШЭ Ольга Анатольевна Афанасьева В статье рассматриваются недостатки использования зарубежных моделей диагностики риска банкротства для прогнозирования банкротства предприятий авиационно-промышленного комплекса (авиапредприятий), а также обосновываются преимущества использования логистических регрессионных моделей (logit-модели) по сравнению с моделями, построенными с помощью множественного дискриминантного анализа (MDA-модели). Разрабатывается logit-модель для диагностики риска банкротства авиапредприятий.

In the regression discontinuity design (RDD), it is common practice to assess the credibility of the design by testing the continuity of the density of the running variable at the cut-off, e.g., . In this paper we propose a new test for continuity of a density at a point based on the so-called g-order statistics, and study its properties under a novel asymptotic framework. The asymptotic framework is intended to approximate a small sample phenomenon: even though the total number n of observations may be large, the number of effective observations local to the cut-off is often small. Thus, while traditional asymptotics in RDD require a growing number of observations local to the cut-off as n → ∞, our framework allows for the number q of observations local to the cut-off to be fixed as n → ∞. The new test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods, exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity, and has favorable power properties against certain alternatives. In a simulation study, we find that the new test controls size remarkably well across designs. We finally apply our test to the design in Lee ( ), a well-known application of the RDD to study incumbency advantage.

This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control. As in , covariate-adaptive randomization refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve "balance" within each stratum. In contrast to , however, we allow for the proportion of units being assigned to each of the treatments to vary across strata. We first study the properties of estimators derived from a "fully saturated" linear regression, i.e., a linear regression of the outcome on all interactions between indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity-consistent estimator of the asymptotic variance are invalid in the sense that they may have limiting rejection probability under the null hypothesis strictly greater than the nominal level; on the other hand, tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact in the sense that they have limiting rejection probability under the null hypothesis equal to the nominal level. For the special case in which the target proportion of units being assigned to each of the treatments does not vary across strata, we additionally consider tests based on estimators derived from a linear regression with "strata fixed effects," i.e., a linear regression of the outcome on indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity-consistent estimator of the asymptotic variance are conservative in the sense that they have limiting rejection probability under the null hypothesis no greater than and typically strictly less than the nominal level, but tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact, thereby generalizing results in for the case of a single treatment to multiple treatments. A simulation study illustrates the practical relevance of our theoretical results.

Single-agent dynamic discrete choice models are typically estimated using heavily parametrized econometric frameworks, making them susceptible to model misspecification. This paper investigates how misspecification affects the results of inference in these models. Specifically, we consider a local misspecification framework in which specification errors are assumed to vanish at an arbitrary and unknown rate with the sample size. Relative to global misspecification, the local misspecification analysis has two important advantages. First, it yields tractable and general results. Second, it allows us to focus on parameters with structural interpretation, instead of "pseudo-true" parameters. We consider a general class of two-step estimators based on the K-stage sequential policy function iteration algorithm, where K denotes the number of iterations employed in the estimation. This class includes Hotz and Miller (1993)'s conditional choice probability estimator, Aguirregabiria and Mira ( )'s pseudo-likelihood estimator, and Pesendorfer and Schmidt-Dengler (2008)'s asymptotic least squares estimator. We show that local misspecification can affect the asymptotic distribution and even the rate of convergence of these estimators. In principle, one might expect that the effect of the local misspecification could change with the number of iterations K. One of our main findings is that this is not the case, i.e., the effect of local misspecification is invariant to K. In practice, this means that researchers cannot eliminate or even alleviate problems of model misspecification by changing K.

This paper studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve "balance" within each stratum. Our main requirement is that the randomization scheme assigns treatment status within each stratum so that the fraction of units being assigned to treatment within each stratum has a well behaved distribution centered around a proportion π as the sample size tends to infinity. Such schemes include, for example, Efron's biased-coin design and stratified block randomization. When testing the null hypothesis that the average treatment effect equals a pre-specified value in such settings, we first show the usual two-sample t-test is conservative in the sense that it has limiting rejection probability under the null hypothesis no greater than and typically strictly less than the nominal level. We show, however, that a simple adjustment to the usual standard error of the two-sample t-test leads to a test that is exact in the sense that its limiting rejection probability under the null hypothesis equals the nominal level. Next, we consider the usual t-test (on the coefficient on treatment assignment) in a linear regression of outcomes on treatment assignment and indicators for each of the strata. We show that this test is exact for the important special case of randomization schemes with π = 1 2 , but is otherwise conservative. We again provide a simple adjustment to the standard error that yields an exact test more generally. Finally, we study the behavior of a modified version of a permutation test, which we refer to as the covariate-adaptive permutation test, that only permutes treatment status for units within the same stratum. When applied to the usual two-sample t-statistic, we show that this test is exact for randomization schemes with π = 1 2 and that additionally achieve what we refer to as "strong balance." For randomization schemes with π = 1 2 , this test may have limiting rejection probability under the null hypothesis strictly greater than the nominal level. When applied to a suitably adjusted version of the two-sample t-statistic, however, we show that this test is exact for all randomization schemes that achieve "strong balance," including those with π = 1 2 . A simulation study confirms the practical relevance of our theoretical results. We conclude with recommendations for empirical practice and an empirical illustration.

This paper studies the problem of specification testing in partially identified models defined by a finite number of moment equalities and inequalities (i.e., (in)equalities). Under the null hypothesis, there is at least one parameter value that simultaneously satisfies all of the moment (in)equalities whereas under the alternative hypothesis there is no such parameter value. While this problem has not been directly addressed in the literature (except in particular cases), several papers have suggested implementing this inferential problem by checking whether confidence intervals for the parameters of interest are empty or not. We propose two hypothesis tests that use the infimum of the sample criterion function over the parameter space as the test statistic together with two different critical values. We obtain two main results. First, we show that the two tests we propose are asymptotically size correct in a uniform sense. Second, we show our tests are more powerful than the test that checks whether the confidence set for the parameters of interest is empty or not.

This paper studies the behavior, under local misspecification, of several confidence sets (CSs) commonly used in the literature on inference in moment (in)equality models. We propose the amount of asymptotic confidence size distortion as a criterion to choose among competing inference methods. This criterion is then applied to compare across test statistics and critical values employed in the construction of CSs. We find two important results under weak assumptions. First, we show that CSs based on subsampling and generalized moment selection (Andrews and Soares ( )) suffer from the same degree of asymptotic confidence size distortion, despite the fact that asymptotically the latter can lead to CSs with strictly smaller expected volume under correct model specification. Second, we show that the asymptotic confidence size of CSs based on the quasi-likelihood ratio test statistic can be an arbitrary small fraction of the asymptotic confidence size of CSs based on the modified method of moments test statistic.

Economic data are frequently generated by stochastic processes that can be modeled as realizations of random functions (functional data). This paper adapts the specification test for functional data developed by Bugni, Hall, Horowitz, and Neumann (2009, Econometrics Journal12, S1–S18) to the presence of missing observations. By using a worst case scenario approach, our method is able to extract the information available in the observed portion of the data while being agnostic about the nature of the missing observations. The presence of missing data implies that our test will not only result in the rejection or lack of rejection of the null hypothesis, but it may also be inconclusive.Under the null hypothesis, our specification test will reject the null hypothesis with a probability that, in the limit, does not exceed the significance level of the test. Moreover, the power of the test converges to one whenever the distribution of the observations conveys that the null hypothesis is ...

We develop methods for testing the null hypothesis that a given functional dataset was generated by a specific parametric model, where parameters must be estimated. Approaches based on two-sample hypothesis-testing for multivariate data are initially considered, with those founded on permutation tests being substantially easier to implement than techniques that require reduction to a finite-dimensional problem. However, the level-acccuracy of permutation-based methods is found to be particularly sensitive to estimation of unknown parameters, so much so that bootstrap calibration is not sufficient to make the tests competitive with those based on Cramér-Von Mises and Kolmogorov-Smirnov ideas. For this reason, and because Kolmogorov-Smirnov tests often have inferior performance in low-dimensional settings, Cramér-Von Mises methods are proposed. Methodology for implementation, involving Monte Carlo techniques for computing the test statistic, and bootstrap algorithms for calibration, is suggested. Importantly, the proposed method does not rely on statistical dimension reduction or smoothing. The method is applied to data on labour-market modelling, which provided the initial motivation for this research. Simulation studies are used to show that the proposed test has good level accuracy and power.

This study aims to predict the value of Indonesian oil and gas imports using the Fuzzy Time Series method of determining average-based intervals (Average Based Length). The data used in this research is annual periodic data from 2013-2021 which was downloaded from the bps.go.id website. The research results show that the volume value of oil and gas imports in Indonesia in January 2023 will reach 4125,802 thousand tons. This research can be concluded that the accuracy of the estimation is very good, producing a MAPE value of 8.04%. The MAPE value is defined as having very good estimation results if the MAPE value is <10%.

Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models Evidence accumulation is arguably the most dominant theory of how people make speeded decisions (see Donkin & Brown, 2018, for a review), and it is typically instantiated in sequential sampling models (e.g., Brown & Heathcote, 2008). These models provide an accurate account of correct and error response time (RT) distributions as well as the corresponding accuracy rates in speeded decision making tasks. The models also allow researchers to translate the data into the meaningful psychological parameters that generate the data. Sequential sampling models assume a simple cognitive architecture consisting of stimulus encoding, response selection, and overt response execution. To make a

This paper provides a general method to translate a standard economic model with a large number of agents into a field-formalism model. This formalism preserves the system's interactions and microeconomic features at the individual level but reveals the emergence of collective states. We apply this method to a simple microeconomic framework of investors and firms. Both macro and micro aspects of the formalism are studied. At the macro-scale, the field formalism shows that, in each sector, three patterns of capital accumulation may emerge. A distribution of patterns across sectors constitute a collective state. Any change in external parameters or expectations in one sector will affect neighbouring sectors, inducing transitions between collective states and generating permanent fluctuations in patterns and flows of capital. Although changes in expectations can cause abrupt changes in collective states, transitions may be slow to occur. Due to its relative inertia, the real economy is bound to be more affected by these constant variations than the financial markets. At the micro-scale we compute the transition functions of individual agents and study their probabilistic dynamics in a given collective state, as a function of their initial state. We show that capital accumulation of an individual agent depends on various factors. The probability associated with each firm's trajectories is the result of several contradictory effects: the firm tends to shift towards sectors with the greatest long-term return, but must take into account the impact of its shift on its attractiveness for investors throughout its trajectory. Since this trajectory depends largely on the average capital of transition sectors, a firm's attractiveness during its relocation depends on the relative level of capital in those sectors. Moreover, the firm must also consider the effects of competition in the intermediate sectors that tends to oust under-capitalized firm towards sectors with lower average capital. For investors, capital allocation depends on their short and long-term returns and investors will tend to reallocate their capital to maximize both. The higher their level of capital, the stronger the re-allocation will be.

Recommended Citation Al-Omari, Emad Khalid and Muqabalah, Nasr Yousuf (2020) "The Big Five Personality Traits and the Learning Environment as Predictors of the Surface and Deep Learning Styles among a Sample of Students at Yarmouk University," Journal of Al-Quds Open University for Educational & Psychological Research & Studies: Vol. 10 : No. 28 , Article 2. Available at: https://digitalcommons.aaru.edu.jo/jaqou_edpsych/vol10/iss28/2

Using different combinations of culture, development and openness to international trade, we test the variability in the incidences of corruption at different stages of development or in other words the non-linearities in the relationship between corruption and development. We employ formal threshold model developed by , and unlike the existing literature, we find that: (1) non-linear models that search for the break points in the relationship between corruption and development are statistically preferable than linear regressions; (2) the effect of development at any stage is much lower than that has been suggested by studies using linear regressions approach; (3) both culture and openness do not affect corruption directly; rather they have an effect on the location of break points in the relationship between corruption and development.

Abstract: In this paper, the benefits of applying the Structural equation modelling (SEM) technique to economic research are methodologically analyzed. SEM is a second-generation multivariate analysis technique for simultaneously measuring and analyzing complex cause-effect relationships. Interest in this technique has grown since a large amount of software has been developed for applied SEM, where the theoretical model is created using simple drawing tools (Arbuckle, 2017). SEM allows
simultaneous standard and non-standard modeling of relationships between latent and observed variables with the ability to process longitudinal data, time series, elimination of autocorrelation and analysis of non-normally distributed variables. Techniques such as multiple and logistic regression and analysis of variance, have certain initial disadvantages that can affect the accuracy of the measurement: 1. simplification of the model structure, 2. they require that all variables be considered observable from the start, and 3. they assume that all variables are measured without error. With the SEM technique, often unobservable constructs, that are indirectly measured by several indicators, can be modelled simultaneously, taking into account measurement errors,
so that the results are more accurate compared to standard techniques. To illustrate the methodological features of SEM, this paper uses a theoretical background, a small graphical manual and an own applied empirical analysis from practice. The study presents the results of an applied empirical research on corporate social responsibility conducted on a sample of 105 companies in the Italian region of Campania. The data were collected using a questionnaire, and the methodological technique applied was an adapted SEM that involved a wider range of factors. PCA was used to identify preliminary factors, which were rotated to reduce the number of variables. The generated principal components were integrated into SEM, where the testing of various inter-associations and the measurement of the impact on the company’s profit were simultaneously carried out. Company profit was measured on a Likert scale, for the period covering the last two years. All three principal components, GEWR, APG
and EFO had a significant and positive impact on the growth of company profits. An increase in GEWR by one point leads to an increase in profit by 0.57. A jump in APG by one point leads to an increase in profit by 0.45. The growth of EFO by one point, causes an increase in profit by 0.3. Following the size of the company, the type of activity, the size of the budget and the
ecological situation in the local community, this study gives recommendations to managers for an effective choice of strategy.

The main goal of this work is to analyze the potential implications of the balance-of-payments constraint theory for business cycles. Although this theory is oriented towards long-run growth, it implicitly predicts the existence of cycles in any open economy. As we try to show, according to this theory, business cycles are generated by two factors: capital flows and trade shocks. Yet the standard model can overestimate cycles due to its specification of trade equations. In the paper we first develop the implications of the balance-of-payments constraint theory with regard to business cycles, then build an augmented model that can correct the potential over-estimation of cycles: Finally we use that model to analyze the sources of business cycles in Portugal and Spain and to show that the standard model does indeed overestimate cycles, at least in these two cases.