Scientific literature has been seemingly enriched by various theoretical and empirical studies analysing the relationship between stock market returns and macroeconomic forces during the last few decades. It is often argued that stock prices are determined by some fundamental macroeconomic variables. This implies that macroeconomic variables can influence investors’ investment decisions and motivates many researchers to investigate the relationships between stock market prices and macroeconomic variables. Different authors select different macroeconomic variables seeking to detect their relationship with stock market prices in various countries. Simultaneously, a number of econometric techniques such as the arbitrage pricing theory, the impulse response function, the error variance decomposition analysis, the vector error correction model, the cointegration analysis, the Granger causality tests and others may be applied for checking the existence of relationship between stock market prices and macroeconomic variables. The current paper attempts to present several classifications of macroeconomic variables, then to select macroeconomic variables for seeking their relationship with stock market prices, and, finally, to define what macroeconomic variables have positive and what macroeconomic variables have negative effects on stock market prices in Lithuania in the short run. Augmented Dickey Fuller test has been employed to check the stationarity of the selected time series since a spurious regression may occur if a time series is not stationary. The Impulse response function has been chosen to test the existence of the short-run relationship between stock market prices and macroeconomic variables. As the results of the Impulse response function are reliable only with a stationary time series the data has been turned into stationary after the second difference. The study embraces six macroeconomic variables (seasonally adjusted gross domestic product at previous year prices, harmonised consumer price index as compared to 2005, the narrow money supply, unemployment rate, short-term interest rates, and exchange rate of the Litas against the US dollar) and the main Lithuanian stock market index – the OMX Vilnius index. The data are monthly and extend from the January of 2000 to the June of 2009. In general, the results of the paper clearly indicate that macroeconomic variables are significant determinants for stock market prices in Lithuania. Gross domestic product and money supply have a positive effect on stock market prices while most of the time unemployment rate, exchange rate, and short-term interest rates negatively influence stock market prices. The findings of the paper are similar to the results of some other empirical studies. If harmonised consumer price index is considered, then it is the best example of an unstable relationship between a macroeconomic variable and stock market prices in Lithuania.

/pdf/the-short-run-relationship-between-stock-market-prices-and-46h682w5xc.pdf

Composite Indexes of Leading, Coinciding, and Lagging Indicators, 1948–67

The Short-Run Relationship between Stock Market Prices and Macroeconomic Variables in Lithuania: An Application of the Impulse Response Function

Economic statisticians do not enjoy an untarnished reputation for accurate forecasting. We have managed, over the years, to come up with some memorable failures. While we have also had our share of successes, they are not as well remembered nor as numerous as we should like. Recently, however, we have begun to pay more attention to the record, and a substantial body of evidence on forecasting performance has accumulated. In this paper I propose to review this record, try to arrive at a balanced appraisal, and offer some suggestions for improvement.

Forecasting Short-Term Economic Change

: The threat of gun misuse occurs against the backdrop of historical, cultural, and legal perspectives within which guns are immersed. This thesis explores the potential for collaboration among the medical, legal, and homeland security disciplines when confronting a commonly held threat, such as gun misuse.Currently, each discipline has a unique and sometimes conflicting definitional view of what the term gun control actually means, as well as how it is to be operationally approached. What emerges within this thesis is the position that gun control is not even the correct term to use and should be avoided when addressing issues involving gun misuse. Rather, the use of the term gun misuse prevention appears to be more appropriate. Such a shift in perspective is not only more consistent with the gun misuse data presented, but also serves to dampen the polarization imposed by the use of the term control.Further, it provides a clearer path for stakeholders from both the public and private sectors to approach and make recommendations when confronting the issue of gun misuse that includes legislative, policy, and technological approaches. This type of approach may pave the way for addressing other issues of common interdisciplinary concern.

/pdf/analysis-of-a-multi-disciplinary-approach-to-gun-misuse-and-4egulcrpm1.pdf

Analysis of a Multi Disciplinary Approach to Gun Misuse and the Need for a Paradigm Shift in Terminology

This paper reviews major features of the development of economic indicator analysis (EIA), notably its contribution to identifying, understanding, explaining and forecasting business cycles. The paper highlights the substantial pioneering role of Dr Geoffrey H. Moore in this development. The paper reviews some key issues regarding the selection and classification of economic indicators; and the methodologies developed to use these indicators to identify and measure business cycles on national, regional and sectoral bases. After making an overall assessment of EIA, acknowledgement is given to the widespread development and applications of EIA around the world to study the co-movements of key economic variables and to forshadow changes in them.

https://www.jstor.org/stable/pdfplus/29794223.pdf

Composite Indexes of Leading, Coinciding, and Lagging Indicators, 1948–67

Citations

The Short-Run Relationship between Stock Market Prices and Macroeconomic Variables in Lithuania: An Application of the Impulse Response Function

Forecasting Short-Term Economic Change

Forecasting Short-Term Economic Change

Analysis of a Multi Disciplinary Approach to Gun Misuse and the Need for a Paradigm Shift in Terminology

The Contribution of Economic Indicator Analysis to Understanding and Forecasting Business Cycles

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