Forecasting the Distribution of Economic Variables in a Data-Rich Environment

2012 ◽  
Author(s):  
Sebastiano Manzan
Keyword(s):  
Economic Variables

Macroeconomic Determinants of Stock Market Prices in Nepal

2020 ◽  
Vol 2 (1) ◽  
pp. 56-65
Author(s):  
Bhim Prasad Panta
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Market ◽  
Money Supply ◽  
Developing Economies ◽  
Annual Data ◽  
Economic Variables ◽  
Market Prices ◽  
Ardl Model ◽  
Long Run

Background: Stock market plays a crucial role in the financial system of a country. It can be viewed as a channel through which resources are properly channelized. It enables the governments and industry to raise long-term capital for financing new projects. The stock markets of developing economies are likely to be sensitive to various macro-economic factors such as GDP, imports, exports, exchange rates etc., when there is high demand on financial products, as a constituent of financial market, ultimately stock market needs to develop. Many factors can be a signal to stock market participants to expect a higher or lower return when investing in stock and one of these factors are macroeconomic variables and thus, macro-economic variables tend to effect on stock market development. Objective: This study examines the linkage between stock market prices (NEPSE index) and five macro-economic variables, namely; real GDP, broad money supply, interest rate, inflation, and exchange rate using ARDL model and to explain the behavior of the Nepal Stock Exchange Index. Methods: The ECM which is delivered from ARDL model through simple linear transformation to integrate short run adjustments with long run equilibrium without losing long run information. The analysis has been done by using 25 years' annual data from 1994 to 2019. Findings: The result suggests that the fluctuation of Nepse Index in long run is strongly associated with broad money supply, interest rate, inflation, and exchange rate. Conclusion: Though Nepalese stock market is in primitive stage, broad money supply, interest rate, inflation and exchange rate are major factors affecting stock market price of Nepal. So, policies and strategies should be made and directed taking these in to consideration. Implication: The findings of research can be helpful to understand the behavior of Nepalese stock market and develop policies for market stabilization.

scholarly journals Mapping chronic disease prevalence based on medication use and socio-demographic variables: an application of LASSO on administrative data sources in healthcare in the Netherlands

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Koen Füssenich ◽  
Hendriek C. Boshuizen ◽  
Markus M. J. Nielen ◽  
Erik Buskens ◽  
Talitha L. Feenstra
Keyword(s):  
Chronic Disease ◽  
Hospital Admission ◽  
Medication Use ◽  
Disease Prevalence ◽  
Percentage Error ◽  
Economic Variables ◽  
Regional Patterns ◽  
Prevalence Estimates ◽  
Socio Economic Variables ◽  
Chronic Disease Prevalence

Abstract Background Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease. Methods Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant. Results Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age. Conclusion Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy.

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