Econometrics

This study explores the long-term impact of population ageing on labour supply and human capital investment in Canada, as well as the induced effects on productive capacity. The analysis is conducted with a dynamic computable overlapping generations model where ...

It is well known that historically a larger number of firms issue common stock and the proportion of external financing accounted for by equity is substantially higher in expansionary phases of the business cycle. We show that this phenomenon is consistent with firms selling seasoned equity when they face lower adverse selection costs, which occurs in periods with more promising investment opportunities and with less uncertainty about assets in place. Thus, firm announcements of equity issues are predicted to convey less adverse information about equity values in such periods. Empirically, we find evidence that generally supports these predictions. Consistent with historical patterns, firms in recent times have tended to increase equity more frequently in expansionary periods. While business cycle variables have significant explanatory power, interest rate variables are generally insignifi~nt. The adverse selection effects as measured by the average negative price reaction to seasoned common stock offering announcements is significantly lower in expansionary periods and in periods with a relatively larger volume ofequity financing. These offer announcement effects are less negative for smaller stock offerings and for issuers with less uncertainty about assets in place.

Special thanks to the Texas Commission on Environmental Quality and the Lozano Long Foundation for the financial support over the years for my work on this research. At TCEQ, I would like to thank Roger Miranda for his continuous support and encouragement throughout this process, Steve Niemeyer for sharing ideas and knowledge, and Richard Hay for providing insight with the use of NEXRAD data. At the LBJ School of Public Affairs, I would like to thank David Eaton Ph.D. for the sharing of knowledge and Shama Gamkhar Ph.D. and Pat Wong Ph.D. for their steadfast advising. At the Lozano Long Institute of Latin American Studies, I am very grateful for the academic advising of Lorraine Leu Ph.D. and the constant support of Steve Alvarez. Thank you to my friends and colleagues Ross Van Horn and Rachel Daggy for sharing ideas and inspiration during the writing of this report. A very special thank you goes out to my wife, Caitlin, who has remained a pillar of support throughout this journey. vi

Design: B&T Ontwerp en advies www.b-en-t.nl Print: Haveka www.haveka.nl All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the author.

Several states have enacted "three-strikes" laws which call for extremely long sentences if a person is convicted of a third serious crime. Criminals faced with such severe penalties might take additional steps to avoid capture. One such step is murdering arresting officers. Even if this rarely occurs it can have a proportionally large impact on police murders. We estimate fixed-effects Poison models of police murders based on panel data for 1973-1998. We find that the laws increase police murders by more than 40 percent. We use the same model to evaluate the impact of the death penalty, imprisonment rates, "shall issue" concealed weapons permit laws, and laws requiring longer sentences for crimes committed with firearms. Only the latter has a significant impact, reducing police murders by roughly 18 percent Boorstein, Michelle. Five years later, 'three strikes' laws go mostly unused. Associated Press (December 12, 1998).

We explore the relation between individualism and horizons and types of corporate investment, based on individualism's implications for risk taking. We find that firms in individualistic countries invest more in long-term (risky) than in short-term (safe) assets. Moreover, the effect of individualism on long-term investment hinges on R&D: firms in individualistic countries invest more in R&D projects but not more in physical assets. To test whether risk taking is the channel through which individualism works, we employ two-stage ordinary least squares and other analyses to nullify alternative explanations, such as: (1) uncontrolled institutions determine both individualism and R&D; and (2) firms in individualistic countries invest more in R&D because they have higher investment efficiency, or pick less-risky R&D projects. We further find that individualistic firms tend to employ excess cash to increase R&D rather than increase dividends, and R&D decisions are less reliant on internal financing but more responsive to growth opportunities in individualistic countries.

Abstract This paper presents a review of nine theoretical models of foreign direct investment (FDI). Discussed are early studies of determinants of FDI (1) as well as determinants of FDI based on the neoclassical trade theory (2), ownership advantages (3), aggregate variables (4), the ownership, location and internalization advantage framework (5), horizontal and vertical FDI models (6), the knowledge-capital model (7), diversified FDI and risk diversification models (8) and policy variables (9). From each of the nine theories, the relevant determinants of FDI are derived. Empirical studies indicate the importance of these determinants in the real world. The paper shows that there is not one single theory of FDI, but a variety of theoretical models attempting to explain FDI and the location decision of multinational firms. Therefore, any analysis of determinants of FDI should not be based on a single theoretical model. Instead, FDI should be explained more broadly by a combination of factors from a variety of theoretical models such as ownership advantages or agglomeration economics, market size and characteristics, cost factors, transport costs, protection, risk factors and policy variables.

Construction Material Time Series ANN NARNET The price of materials is dependent on different factors such as raw material costs, production costs, and cost of logistics. The construction industry professionals face difficulties in times when there are fluctuations in material prices. This study aimed to model the expectancy of trends in material prices through a time series analysis, as the expectancy of trend is a time dependent dataset.. In this context, the study is focused on utilization of a special type of ANN (and special type of RNN) architecture known as Nonlinear Autoregressive Neural Network (NARNET). Ten different NARNET configurations were implemented in MATLAB and their performance were tested in the study. The results have shown that NARNETs are able to model the expectancy of trend accurately.

Construction Material Price ARIMA Time Series Construction materials has a key impact on the cost of construction. In construction industry it is important to foresee the trends of material prices to prevent, cost overruns during the construction stage and bankruptcy of the contractors. The material price trends have a time dependent nature, and time series analysis methods can be utilized to model and estimate them. This study focuses on modeling and forecasting the trends in material prices through Box-Jenkins methodology. In this context, an economic indicator named General Trend in Construction Materials Industry is modelled with an ARIMA (1,1,0) model. The forecasts done with the model indicate that the model can successfully predict the future values of the indicator.

Rice is one of the main foods in Indonesia. A change of rice price will cause a major effect in the lives of consumers. On the other hand, there are so many factors that influence the rice price. Thus finding key factors which are significant to the rice price, as well as forecasting the consumer's rice price are needed in order to maintain the stabilization of rice price. The second objective is to find key factors which influence the rice price by using multiple linear regression models. The parameters were estimated by ordinary least square methods. There are 6 variables that are significant at α=5%, which are the consumer's rice price at the previous period, rice production at the current and previous period, farmer's GKP price, realization of domestic stock, and total rice import. The rice price will increase if the GKP price and realization of domestic stock increase whereas total rice import and the consumer's rice price at the previous period have negative influences towards the rice price. In this model rice production at the current and previous period have positive signs, contradictory to the microeconomic theory where when the rice production increases, there will be an excess supply and the price will drop. That condition will occur only if the commodity is a free commodity and the rice is at the sufficiency level but in Indonesia, rice is affected by the government's policy and the rice productivity is left behind by the demand. Forecasting the consumer's rice price for the next five years was the last objective of this research. ARIMA Box-Jenkins Method, X-12 ARIMA, Winter's Method, and Trend Analysis were compared to find the best statistical model to forecast the consumer's rice price. X-12 ARIMA turns out to be the best method because it has the smallest MAPE, MAD, and MSD value. This result is a satisfactory because according to Findley et al. (1998) X-12 ARIMA has the capability to adjust seasonal and trading day factors which usually causes fluctuations in an economic time series data. Besides that, the X-12 ARIMA method also enhances the lack of other forecasting techniques used in this research to add regression effects. The regARIMA makes it possible to add the user defined parameters, in this case the length of month parameter. The length of month parameter rescales the monthly observation by a weight corresponding to the month relative length with respect to the average length. The seasonal adjusted data from the original time series data is aimed to simplify the data without loosing important information.

The types of investment that are made abroad are known as foreign and have majorly affected countries that engage in these activities of business. The various factors which are to be considered such as -political factor, social factor and economical factor as these factors affect the allocation of international capital. This research paper will examine and analyze the literature review which is focusing on movement of capital under foreign direct investment. Existence of a positive effect on the economy of a country that is injected with these substitutes has led to a better performance of the overall GDP and increase in national output. The same is related to economic growth. It is argued that the study based upon verified documents involves all the countries irrespective of their geographical area whereas the fact is that many of them are concerned with largest recipients of foreign direct investment such as-Asian and Latin American countries.

Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients. Inverse gamma prior distributions are placed on the penalty parameters. We treat the hyperparameters of the inverse gamma prior as unknowns and estimate them along with the other parameters. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a prostate cancer dataset, we compare the performance of the BALQR method proposed with six existing Bayesian and non-Bayesian methods. The simulation studies and the prostate cancer data analysis indicate that the BALQR method performs well in comparison to the other approaches.

In 1950 Markowitz first formalized the portfolio optimization problem in terms of mean return and variance. Since then, the mean-variance model has played a crucial role in single-period portfolio optimization theory and practice. In this paper we study the optimal portfolio selection problem in a multi-period framework, by considering fixed and proportional transaction costs and evaluating how much they affect a re-investment strategy. Specifically, we modify the single-period portfolio optimization model, based on the Conditional Value at Risk (CVaR) as measure of risk, to introduce portfolio rebalancing. The aim is to provide investors and financial institutions with an effective tool to better exploit new information made available by the market. We then suggest a procedure to use the proposed optimization model in a multi-period framework. Extensive computational results based on different historical data sets from German Stock Exchange Market (XETRA) are presented.

This paper investigates Day-of-the-Week Effect in stock returns in the primary equity market Karachi Stock Exchange (KSE) of Pakistan by employing OLS regression approach. Data consists of daily closing prices of KSE-100 Index from January 01, 2004 to December 30, 2011. A traditional method of finding Day-of-the-Week Effect has been comprised of only one regression equation. Contrary to this plausible methodology, this paper proposes five separate models to statistically find significant effect on each trading day of the week. Non-parametric Kolmogorov-Smirnov (K-S) test confirms abnormal distribution of returns. Robust Standard Error addresses heteroscedasticity of returns; proved by abnormal distribution. The t-statistics tests significance of β coefficients and One Factor ANOVA tests the hypotheses related to significant difference of mean returns. Findings conclude mixed results due to the effect of political instability on the anomaly. No effect found in Sub Period I. While, negative Monday and Positive Friday effects revealed in Sub Period II; result consistent with the findings of Fields (1931), Cross (1973), French (1980) and Haroon (2005).

This study utilizes panel data analysis over the 1996 to 2015 period to investigate the impact of governance quality (including democratic quality and technical quality) on income inequality in ten Asain countries, classified as "advanced economies" and "emerging market and developing economies". The empirical results show that the impacts of democratic quality and technical quality on income inequality are significantly negative within "emerging market and developing economies". However, for the "advanced economies", the effects of democratic quality and technical quality on income inequality are nonsignificantly positive and significantly positive, respectively. These findings imply that promoting good governance is useful to reduce income inequality for "emerging market and developing economies" but the effect may not be effective for "advanced economies.

A continuous-time model that incorporates several key elements in tumor dynamics is analyzed. More precisely, the form of proliferating and quiescent cell lines comes out from their relations with the whole tumor mass, giving rise to a two-dimensional diffusion ...

This paper models and predicts how the strengthening of intellectual property (IP) protection will impact R&D in developing economies. International agreements such as TRIPs and free trade agreements are enhancing the level of international control on IP. This is changing deeply the R&D environment in developing economies by restraining illegal channels of knowledge accumulation such as imitation, reverse engineering and piracy. An asymmetric and non-cooperative two-stage (R&D-Production) game is proposed to model a developing market where two local firms compete with a more innovative foreign firm. Equilibrium R&D expenditures and profits of the competing firms are compared for different levels of: market technology, technological gaps and IP protection. The proposed model shows clearly that a stringent enforcement of IP agreements will dramatically decrease the innovative abilities of developing economies especially in high technological sectors. The maintain and increase of their...

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A standard method for fitting the multinomial logit model. used in some statistic-al packages, is to represent it in terms of the equivalent Poisson log-linear model. The constraint necessary for this equivalence requires the inclusion of a set of nuisance parameters in the Poisson model, of dimension equal to the number of distinct values of the set of covariates. In such packages the model is therefore restricted to the analysis of categorical covariates. i.e. contingency tables. This paper describes a method for fitting the multinomial logit model which requires only a simple scoring algorithm. but does not use the equivalent Poisson model, and can be used with continuous covariates with an unlimited number of distinct values. The method is implemented 9s a set of GLIM macros. An example is discussed.

Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with nonelliptical, possibly, multimodal target distributions. A key step is a radial-based transformation to directions and distances. After the transformations a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution by means of the numerical inverse transformation method. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way.

Perhaps the most active area of research in the field of computable general eqwlibrium tax models involves the addition of genuine dynamics to the behavior of both consumers and producers. Such model enhancement in the direction of dynamics is necessary to satisfactorily capture the effects of such tax alternatives as the adoptitsn of an expenditure tax or the integration of the corporate and personal tax systems. This paper surveys 1 I models that include at least some dynamics in their structure. It treats the issue of incorporating dynamics into the models, and also discusses different computational and implementation approaches. Finally, it includes a dynamic computational genera1 equilibrium model of corporate tax integration that indicates the potential importance of modeling dynamic choice.

A trend in actuarial finance is to combine technical risk with interest risk. If Y t , t ¼ 1, 2, . . . denotes the time-value of money (discount factors at time t) and X t the stochastic payments to be made at time t, the random variable of interest is often the scalar product of these two random vectors V ¼ RX t Y t . The vectors * X and *

The impact of class size on student achievement remains an open question despite hundreds of empirical studies and the perception amongst parents, teachers, and policymakers that larger classes are a significant detriment to student development. This study sheds new light on this ambiguity by utilizing nonparametric tests for stochastic dominance to analyze unconditional and conditional test score distributions across students facing different class sizes. Analyzing the conditional distributions of test scores (purged of observables using class-size specific returns), we find that there is little causal effect of marginal reductions in class size on test scores within the range of 20 or more students. However, reductions in class size from above 20 students to below 20 students, as well as marginal reductions in classes with fewer than 20 students, increase test scores for students below the median, but decrease test scores above the median. This non-uniform impact of class size suggests that compensatory school policies, whereby lower-performing students are placed in smaller classes and higher-performing students are placed in larger classes, improves the academic achievement of not just the lower-performing students, but also the higher-performing students.

This paper examines the asymmetrical relationship between exchange rate and consumer prices in 40 Sub-Saharan African (SSA) countries from 1990q1 to 2017q4. The exchange rate pass-through (ERPT) to consumer prices is estimated for each country by using the nonlinear autoregressive distributed lags (NARDL) framework and dynamic panel estimators robust to cross-sectionally correlated errors. Firstly, our findings suggest an asymmetrical ERPT in the SSA region during the short-term, whereas there are mixed results across sub-regions in the long-term. Next, we find incomplete and significant ERPT to consumer prices in the entire SSA region which is higher during the depreciation of the local currency than after appreciations. Third, we find nonlinear ERPT with respect to the size of the exchange rate. The pass-through is higher during large exchange rate changes than after small changes.  Finally, we find that the pass-through is greater in the countries with fixed exchange rate re...

In this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on statistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. Misspecification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented.

This paper analyzes the impacts and the welfare cost of building-height restrictions, providing a concrete welfare-cost estimate for the city of Bangalore. Relying on several theoretical results, the analysis shows that the welfare cost imposed on its residents by Bangalore's FAR restriction ranges between 3 and 6 percent of household consumption. This burden represents a signi¯cant share of individual resources, and its presence may push many marginal households

This paper examines the corporate governance (CG) practices in emerging markets with special reference to the listed firms in the Gulf Cooperation Council's (GCC) oil rich countries. It develops an un-weighted Corporate Governance Index (CGI) model for non-financial firms using recent data. The usefulness of the model is demonstrated with a specific country example. The index identifies thirty internal governance attributes which are abridged in three categories of all the selected firms to form the best CG practices in the region. The results demonstrate that GCC companies adhere to 69% of the attributes addressed in the CGI. The results also show that the firms listed in the United Arab Emirates stock markets exhibit the best adherence to the CG attributes examined in the study followed by Oman, Saudi Arabia, Qatar and Kuwait, respectively. The current paper offers valuable recommendations to policy makers to gradually embed strong and specific governance practices. Special emphasis is placed to board effectiveness and structural and organizational frameworks in order to ensure a sustainable quality of CG practices in the region.

The main purpose of this research paper is to explore and understand the nature of association and the possible existence of a short run and long run relationship between US Dollar, EURO, British Pound and Japanese Yen. To find out the relationship among currencies USD/INR, EUR/INR, GBP/INR and JPY/INR pairs are considered. The main idea is to know how these selected indicators are related to each other. The daily basis 2781 observations for all four variables from year 2007 to 2018 are taken into consideration. Data are collected from website of Reserve Bank of India. The stationarity of time series is checked and differentiated as per requirement. Johansen co-integration test to know the long run relationship between variables is used. The result shows that there is no co-integration equation among the variables. The short run relationship is examined with help of Vector Auto-regression (VAR) model and the short run relationship within different lags of variables has been identifi...

This paper investigates a computationally simple variant of boosting, L 2 Boost, which is constructed from a functional gradient descent algorithm with the L 2-loss function. As other boosting algorithms, L 2 Boost uses many times in an iterative fashion a pre-chosen fitting method, called the learner. Based on the explicit expression of refitting of residuals of L 2 Boost, the case with (symmetric) linear learners is studied in detail in both regression and classification. In particular, with the boosting iteration m working as the smoothing or regularization parameter, a new exponential bias-variance trade off is found with the variance (complexity) term increasing very slowly as m tends to infinity. When the learner is a smoothing spline, an optimal rate of convergence result holds for both regression and classification and the boosted smoothing spline even adapts to higher order, unknown smoothness. Moreover, a simple expansion of a (smoothed) 0-1 loss function is derived to reveal the importance of the decision boundary, bias reduction, and impossibility of an additive bias-variance decomposition in classification. Finally, simulation and real data set results are obtained to demonstrate the attractiveness of L 2 Boost. In particular, we demonstrate that L 2 Boosting with a novel component-wise cubic smoothing spline is both practical and effective in the presence of high-dimensional predictors.

Multiplant monopoly models generally assume that firm demand is unaffected by the number of plants established. This assumption is inappropriate when transportation costs are significant. Because of this, a multiplant spatial monopolist may choose to operate on the downsloping, constant, or upsloping segment of plant average production cost curves, depending upon the impact of additional plants on cost levels. However, such a monopolist will choose a long-run firm size which is associated with increasing firm level average costs as long as economic profits are available. Long-run average costs are minimized if the monopolist just breaks even.

Based on reinforced urn process introduced by Muliere, Secchi and Walker we propose a Bayesian nonparametric approach to analyse a design determining the maximum tolerated dose in Phase I clinical trials for new drug development when intrapatient dose escalation is allowed. A predictive distribution of maximum tolerated dose is obtained and its point estimation may consist in the corresponding expected value.

We analyze the global imbalances and the required adjustments for rebalancing in current accounts and real exchange rates. We set up a two-country two-sector model for the US-China with two asymmetries. First, we assume that the size of China initially is one third of the US but its size becomes half of the US in the next ten years consistent with the fast growth expectations in China. Secondly, we assume that China initially runs a net export surplus against the US. Then we quantitatively study two adjustment scenarios. First scenario, called Slow Adjustment, assumes that in the process of growth, Chinese demand composition moves more towards domestic non-tradable sector. In this case, Chinese real exchange rate appreciates gradually and net export surplus also decreases slowly. Second scenario, called Quick Adjustment, assumes that in addition to the higher non-tradable share in output, net export surplus against US goes to zero quickly in …ve years. In this case, net export adjustment happens quickly and real exchange rates in China also appreciate faster and at a higher rate than Slow Adjustment case. Even though, global imbalances are eliminated faster in the Quick Adjustment case, high real appreciation in China hurts importers in the US. A comparison in terms of output shows that Slow Adjustments is preferred for both countries.

Quality control using continuous monitoring from images is emerging as an active research area. These applications require adaptive statistical techniques in order to detect and isolate process abnormalities. A novel approach is introduced for monitoring schemes in the setting of image data when the quality is associated with uniform pixel gray-scales. The proposed approach requires the definition of a statistic which takes into account both the spatial dependency and the changes in local variability. An application on paper surface demonstrates how the monitoring scheme performs in practical applications.

In this paper, a new life test plan called a progressive first-failure-censoring scheme is introduced. Maximum likelihood estimates, exact and approximate confidence intervals and an exact confidence region for the parameters of the Weibull distribution are discussed for the new censoring scheme. A numerical example is provided to illustrate the proposed censoring scheme. Some simulation results are presented and used to assess the performance of the proposed estimation methods developed here. The expected time required to complete the proposed life test plan is derived. Finally, a numerical study for comparing among different censoring schemes in terms of expected test time is given.

A problem frequently encountered by the practitioner in Discriminant Analysis is how to select the best variables. In mixed discriminant analysis (MDA), i.e., discriminant analysis with both continuous and discrete variables, the problem is more di cult because of the di erent nature of the variables. Various methods have been proposed in recent years for selecting variables in MDA. In this paper we use two versions of a generalized Mahalanobis distance between populations based on the Kullback-Leibler divergence for the ÿrst and on the Hellinger-Matusita distance for the second. Stopping rules are established from distributional results. A simulation experiment is used to compare the two proposed selection methods and a third based on a modiÿed version of the Akaike Information Criterion (AIC). Since the simulations focus on situations with just one continuous and one binary variable, they can only give indications concerning a few variables and caution is recommended if extended to more usual situations.

The idea of transferability is to employ in model estimation, fitted model parameters computed from a different data set. The combined estimator approach to the transferability problem is expressed as a linear combination of the unbiased direct estimators on the two data sets. The major gain is in variance reduction. The combined estimator is shown to have superior accuracy, in a Mean Square Error sense, to a unbiased direct estimator whenever the transfer bias is relatively small. A test that indicates if the combined estimator is superior to the direct estimator is provided. Variances of the direct estimators are assumed to be known. Monte Carlo experiments are performed to assess the quality of the approximations. The results show that the approximations used are highly conservative. An empirical example of the combined estimator applied to a discrete choice problem is presented.

... a function that is assumed to have two continuous derivatives, adding the side condition guarantees positivity and produces, uniquely, the Epanechnikov kernel. ... It will be clear from the previous paragraphs that, in the problem of noise-free density estimation, the side constraints ...

We present a new specification for the multinomial multiperiod Probit model with autocorrelated errors. In sharp contrast with commonly used specifications, ours is invariant with respect to the choice of a baseline alternative for utility differencing. It also nests these standard models as special cases, allowing for data based selection of the baseline alternatives for the latter. Likelihood evaluation is achieved under an Efficient Importance Sampling (EIS) version of the standard GHK algorithm. Several simulation experiments highlight identification, estimation and pretesting within the new class of multinomial multiperiod Probit models.

In this paper, we modeled individual consumption as a function of income generated by a cow under one-cow-one family program. The sample data of one-cow-one family program was collected from Rwanda. The model was applied on this sample data thereby estimating the poverty level and its variance. The confidence intervals of the poverty levels were also computed and compared to already established results.

The paper represents the research of matching Hurst exponent to Elliott wave models for financial time series. It illustrates that there are some values of Hurst exponent which match the most efficient using of Elliot Wave Principle for time series prediction.
Keywords: wave models, Hurst exponent, financial time series, stock market, R/S-analysis

One di culty with classiÿcation studies is unobserved or missing observations that often occur in multivariate datasets. The mixture likelihood approach to clustering has been well developed and is much used, particularly for mixtures where the component distributions are multivariate normal. It is shown that this approach can be extended to analyse data with mixed categorical and continuous attributes and where some of the data are missing at random in the sense of Little and Rubin (Statistical Analysis with Mixing Data, Wiley, New York).