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The question whether real money causes real output appears to
be important for many economists working in the area of macroeconomics
and, has been subjected to a variety of modern econometric techniques,
producing conflicting results. One often applied method to investigate
the empirical relationship between money and real activity is Granger
causality analysis [Granger (1969)]. Using this approach, the causality
question can be sharply posed as whether past values of money help to
predict current values of output. This concept, however, should be
clearly distinguished from any richer philosophical notion of causality
[cf. Holland (1986)]. Present paper examines the relationship between
money (both M1 and M2) and income (Real GDP) for 15 developing countries
using a newly developed heterogeneous dynamic panel data approach.1 Sims
(1972) postulated "the hypothesis that causality is unidirectional from
money to income agrees with the post war U.S. data, whereas the
hypothesis that causality is unidirectional from income to money is
rejected". Since then a voluminous literature has emerged testing the
direction of causality.2 Some studies have tested the relationship
between these variables and the direction of causality for a particular
country using time series techniques [e.g., Hsiao (1979) for Canada,
Stock and Watson (1989) for U.S. data, Friedman and Kuttner (1992, 1993)
for U.S. data, Thoma (1994) for U.S. data, Christiana and Ljungquist
(1988) for U.S. data, Davis and Tanner (1997) for U.S. data, Jusoh
(1986) for Malaysia, Zubaidi, et al. (1996) for Malaysia, Biswas and
Saunders (1998) for India, and Bengali, et al. (1999) for Pakistan].
Other studies have tested the above on a number of countries, for
example Krol and Ohanian (1990) used the data for Canada, Germany, Japan
and the U.K. Hayo (1999) using data from 14 European Union (EU)
countries plus Canada, Japan, and the United States. More recently Hafer
and Kutan (2002) used a sample of 20 industrialised and developing
countries. This paper contributes to this later strand of the
literature, which it extends in three directions. First, it employed a
newly developed panel cointegration technique [Larsson, et al. (2001)],
to examine the long-run relationship between money and income. Second,
the study performs panel causality test, recently developed by Hurlin
and Venet (2001), to explore the direction of causality between the said
variables. Third, the important contribution of the present study is to
test whether relationship between money and income is homogeneous or
heterogeneous across countries.

Reality-based modeling for today's unique economic recovery. 'Economic Modeling in the Post Great Recession Era' presents a more realistic approach to modeling, using direct statistical applications to address the characteristics and trends central to current market behaviors, Reality-based modeling for today's unique economic recovery Economic Modeling in the Post Great Recession Era presents a more realistic approach to modeling, using direct statistical applications to address the characteristics and trends central to current market behaviors. This book's unique focus on the reality of today's markets makes it an invaluable resource for students and practitioners seeking a comprehensive guide to more accurate forecasting. While most books treat the economy as if it were in a vacuum, building models around idealized or perception-biased behaviors, this book deals with the economy as it currently stands-in a state of recovery, limited by financial constraints, imperfect information, and lags and disparities in price movements. The authors identify how these characteristics impact various markets' behaviors, and quantify those behaviors using SAS as the primary statistical tool. Today's economy bears a number of unique attributes that usual modeling methods fail to consider. This book describes how to approach modeling based on real-world, observable data in order to make better-informed decisions in today's markets. Discover the three economic characteristics with the greatest impact on various markets Create economic models that mirror the current post-recession reality Adopt statistical methods that identify and adapt to structural breaks and lags Factor real-world imperfections into modeling for more accurate forecasting The past few years have shown a clear demarcation between policymakers' forecasts and actual outcomes. As the dust settles on the Great Recession, after-effects linger-and impact our current recovery in ways that diverge from past experience and theoretical expectations. Economic Modeling in the Post Great Recession Era provides comprehensive guidance grounded in reality for today's economic decision-makers

The role of exchange rate policy in economic development has
been the subject of much debate and controversy in the development
literature. Interest rates and exchange rates are usually viewed as
important in the transmission of monetary impulses to the real economy.
In the short run the standard view of academics and policy-makers is
that a monetary expansion lowers the interest rate and rises the
exchange rate, with these price changes then affecting the level and
composition of aggregate demand. Frequently, these influences are
described as the liquidity effects of monetary expansion, viewed as the
joint effect of providing larger quantities of money to the private
sector. Popular theories of exchange-rate determination also predict a
link between real exchange rates and real interest rate differentials.
These theories combine the uncovered interest parity relationship with
the assumption that the real exchange rate deviates from its long-run
level only temporarily. Under these assumptions, shocks to the real
exchange rate—which are often viewed as caused by shocks to monetary
policy—are expected to reverse themselves over time. This study
investigates the long-run relationship between real exchange rates and
real interest rate differentials using recently developed panel
cointegration technique. Although this kind of relationship has been
studied by a number of researchers,1 very little evidence in support of
the relationship has been reported in the case of developing countries.
For example, Meese and Rogoff (1988) and Edison and Pauls (1993), among
others, used the Engle-Granger cointegration method and fail to
establish a clear long-run relationship in their analysis.