Relative Price Adjustment and the UK Philips Curve

We find that measures of the distribution of relative price changes are significant when included in a standard model of the UK Phillips curve based on time dependent price adjustment. Since the inclusion of these variables is not implied by this model but is implied by a state-dependent model of price adjustment or by a time-dependent model with allowance for heterogeneity among price-setters, we conclude that the familiar time-dependent model does not provide a complete account of the Phillips curve.New Keynesian Phillips curve, Relative Price Changes, Labour share

A spatial approach to measure productivity spillovers of foreign affiliated firms in Turkish manufacturing industries

In this paper we aim to analyze the productivity spillovers of foreign affiliated and domestic firms in Turkish manufacturing industries. As a novelty inter-sectoral linkages are modeled through the use of spatial models. Our results indicate the existence of positive and significant productivity spillovers among the neighborhood firms. We also find that an increase in the share of foreign affiliated firms in a given industry has positive impact on the productivity level of vertically related industries. However, our results do not provide any clear evidence that domestic firms benefit from the foreign affiliated firms either operating in the same industry or in the neighborhood industries. The findings suggest that unlike the effects of foreign affiliated firms, research and development expenditures significantly contribute to the productivity levels of domestic firms. --Productivity,foreign direct investment,research and development,spatial econometrics

The bank lending channel in Turkey: Has it changed after the low inflation regime?

In this paper we aim to analyze the role of credit channel in the monetary transmission mechanism under different inflationary environments in Turkey covering the period 1986:1 - 2009:10. Our results suggest that traditional interest rate channel is only valid for the postinflation targeting period. This variable is also more effective monetary policy tool in terms of its impacts on economic activity in the both regimes. Credit shocks itself have significant power on economic activity and prices. However, the effect of monetary shocks on credit volume is very limited especially in the low inflation regime. --

Regression and multivariate models for predicting particulate matter concentration level

The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies

Application of step wise regression analysis in predicting future particulate matter concentration episode

Particulate matter is an air pollutant that has resulted in tremendous health effects to the exposed populace. Air quality forecasting is an established process where air pollutants particularly, particulate matter (PM10) concentration is predicted in advance, so that adequate measures are implemented to reduce the health effect of PM10 to the barest level. The present study used daily average PM10 concentration and meteorological parameters (temperature, humidity, wind speed and wind direction) for 5 years (2006–2010) from three industrial air quality monitoring stations in Malaysia (Balok Baru, Tasek and Paka). Time series plot was used to assess PM10 pollution trend in the industrial areas. Additionally, step wise regression (SWR) analysis was used to predict next day PM10 concentrations for the three industrial areas. The SWR method was compared with a persistence model to assess its predictive capabilities. The results for the trend analysis showed that, Balok Baru (BB) had higher PM10 concentration levels, having high values in 2006, 2007 and 2009. These values were higher than the Malaysian Ambient Air Quality Guideline (MAAQG) of 150 μg/m3. Subsequently, the other two industrial areas Tasek (TK) and Paka (PK) had no record of violating the MAAQG. The results for the SWR analysis had significant R 2 values of 0.64, 0.66 and 0.60, respectively. The model performance results for variance inflation factor (VIF) were less than 5 and Durbin-Watson test (DW) had value of 2 for each of the study areas, which were significant. The comparative analysis between SWR and persistence model showed that the SWR had better capabilities, having lower errors for the BB, TK and PK areas. Using root mean square error (RMSE), the results showed error differences of 7, 12 and 16 %, and higher predictability using index of agreement (IA), having a difference of 17, 19 and 16 % for BB, TK, and PK areas, respectively. The results showed that SWR can be used in predicting PM10 next day average concentration, while the extreme event detection results showed that 100 μg/m3 were better detected than the 150 μg/m3 bench marked levels

Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models

Prediction of particulate matter (PM10) episode in advance enables for better preparation to avert and reduce the impact of air pollution ahead of time. This is possible with proper understanding of air pollutants and the parameters that influence its pattern. Hence, this study analysed daily average PM10, temperature (T), humidity (H), wind speed and wind direction data for 5 years (2006–2010), from two industrial air quality monitoring stations. These data were used to evaluate the impact of meteorological parameters and PM10 in two peculiar seasons: south-west monsoon and north-east monsoon seasons, using principal component analysis (PCA). Subsequently, lognormal regression (LR), multiple linear regression (MLR) and principal component regression (PCR) methods were used to forecast next-day average PM10 concentration level. The PCA result (seasonal variability) showed that peculiar relationship exists between PM10 pollutants and meteorological parameters. For the prediction models, the three methods gave significant results in terms of performance indicators. However, PCR had better predictability, having a higher coefficient of determination (R2) and better performance indicator results than LR and MLR methods. The outcomes of this study signify that PCR models can be effectively used as a suitable format in predicting next-day average PM10 concentration levels

Efektivitas Herbisida Monoamonium Glifosat untuk Pengendalian Gulma di Bawah Tegakan Sengon di Parung Panjang, Jawa Barat

Efektivitas Monoamonium Glifosat sebagai bahan aktif herbisida telah diuji untuk mengendalikan gulma di bawah tegakan Paraserianthes falcataria (L.) Nielsen di Parung Panjang, Jawa Barat. Percobaan dilaksanakan melalui aplikasi herbisida Monoamonium Glifosat dengan dosis 3, 4,5, 6 dan 9 liter per ha serta membandingkannya dengan herbisida dengan bahan aktif Glifosat 4,5 liter per ha, perlakuan manual and kontrol (tanpa perlakuan). Hasil percobaan menunjukkan bahwa Herbisida Monoamonium Glifosat dapat digunakan sebagai sarana pemeliharaan tanaman kehutanan dari gangguan gulma di bawah tegakan Paraserianthes falcataria. Selanjutnya Herbisida Monoamonium Glifosat dengan minimum dosis 4,5 liter/ha efektif untuk mengendalikan gulma Imperata cylindrica Beauv., Borreria latifolia DC. dan Mikania micrantha Will. Meskipun demikian herbisida ini tidak efektif untuk mengendalikan pertumbuhan gulma Chromolaena odorata DC. Pada tanaman Paraserianthes falcataria (L.) Nielsen tidak tampak gejala keracunan akibat penggunaan herbisida Monoamonium Glifosat pada semua tingkat dosis yang dicobaka

An assessment of cost escalation in building construction project

Estimating of cost for building construction projects with minimum error at the conceptual stage of project development is quite  essential for planning. This study seeks to evaluate factors responsible for cost escalation of building construction projects.  Questionnaires were administered to examine and assess these factors. Subsequently, the mean score value of each factor was determined. In addition, Correlation and Linear regression analyses were used to establish the relationship between these factors. Factors responsible for cost escalation in projects were examined as well as the impact of those factors, and occurrence of those factors on project cost. The result of the analysis showed that, the most agreed factors responsible for project cost escalation were; inadequate supervision, irregular payment, and design error, having high mean values of 4.25, 4.20, and 4.15, respectively. Also, correlation analysis result established that the factors responsible for cost escalation and the impact of cost escalation had significant R and R2 of 0.81 and 0.70 respectively. Addressing these factors would go a long way in reducing the escalation of building project cost. Never the less, an effective cost management strategy is absolutely necessary to safeguard and sustain the construction  industry. Keywords: cost escalation, building project, construction, regression analysi

Appraisal of environmental sustainability studies in the quantity surveying curriculum

The construction industry requires professionals that are environmentally sensitive to sustain the built environment for future generation. This is necessary due to the rising environmental degradation from various construction activities. Tertiary institutions could be the appropriate platform to instil these ideas in aspiring construction professionals. This study assessed Quantity Surveying (QS) curriculum of Polytechnics to examine the courses taught and to assess whether there are courses in the curriculum that are related to environmental sustainability studies. Also, questionnaires administered to lecturers and students of QS departments was used to examine the concept and awareness of environmental sustainability taught in the institution. The study established that generally, taught courses in QS departments of Polytechnics have little or no environmental sustainability factored into them. Furthermore, cumulative mean and regression analysis were used to evaluate the data obtained from the questionnaire. The analysis established that the respondents affirmed that the QS curriculum does not create awareness of environmental sustainability, having a cumulative mean score of 2.04 for lecturers and 2.28 for students which is within the cut-off point of 1.5 to 2.5 (Disagree). Based on the regression analysis, it was established that the correlation between the lecturers’ and the students’ responses was significant having a R result of 0.95. The regression analysis explicated that the lecturers can explain the students’ views having a R2 value of 0.90. The review of QS curriculum to include environmental sustainability studies is paramount in producing QS professionals that are environmentally inclined in training. Keywords: Environmental Sustainability, Quantity surveying, Higher institutions and Curriculu