These are the questions that several recent empirical studies have examined, focusing specifically on whether the **relationship** **between** **inflation** and long-run **growth** is a nonlinear one. 3 In other words, at some (low) rate of **inflation**, the rela- tionship is positive or nonexistent, but at higher rates it becomes negative. If such a nonlinear **relationship** exists then it should be possible, in principle, to estimate the inflexion point, or **threshold**, at which the sign of the **relationship** **between** the two variables would switch. The possibility of such a nonlinear **relationship** was first identified by Fischer (1993), who noted the existence of a positive relation- ship at low rates of **inflation** and a negative one as **inflation** rose (which weakened as **inflation** increased). Sarel (1996) specifically tested for the existence of a struc- tural break in the **relationship** **between** **inflation** and **growth** and found evidence of a significant structural break at an annual **inflation** rate of 8 percent. Below that rate, **inflation** does not have a significant effect on **growth**, or it may even show a slightly positive effect. For **inflation** rates greater than 8 percent, the effect is negative, statistically significant, and strong. Ignoring the existence of this **threshold** substantially biases the effect of **inflation** on **growth**. Ghosh and Phillips (1998), using a larger sample than Sarel’s, find a substantially lower **threshold** effect at 2.5 percent annual **inflation** rate. They also find that **inflation** is one of the most important statistical determinants of **growth**. Christoffersen and Doyle (1998) estimate the **threshold** level at 13 percent for transition economies. Bruno and Easterly (1998) argue that the negative **relationship** **between** **inflation** and **growth**, typically found in cross-country regressions, exists only in high- frequency data and with extreme **inflation** observations. They find no cross- sectional correlation **between** long-run averages of **growth** and **inflation** in the full sample, but detect a negative effect of **inflation** and **growth** for **inflation** rates higher than 40 percent. 4 A useful discussion of previous work on this issue is given in Ghosh (2000).

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Finally, closer to our line of work, (Khan and Senhadji 2001) estimate the **threshold** level of **inflation** by using a balanced panel that averages time-series data over nonoverlapping half decades. The authors estimate the **threshold** level of **inflation**, above which **inflation** significantly slows **growth**, to be **between** 0.89% and 1.11% for industrialized countries, and **between** 10.62% and 11.38% for developing countries. The negative and significant **relationship** **between** **inflation** and **growth**, for **inflation** rates above the **threshold** level, is quite robust with respect to the estimation method, perturbations in the location of the **threshold** level, the exclusion of high **inflation** observations, data frequency and alternative specifications. But their results are difficult to interpret because their specification does not fit into the class of models pioneered by (Hansen 1999, Hansen 2000). 3 This implies that Hansen’s methods of inference do not necessarily apply.

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Kremer et al. (2013) indicate that the existing studies using panel data on the **threshold** **effects** of **inflation** on **growth** might have some limitations. Since initial income played as an important variable in **growth** models, but it is normally being excluded among the control variables. Sometimes, even when initial income is included, the endogeneity problem occurred and eventually causing it is not taken into account as in Khan and Senhadji, (2001), Drukker et al. (2005), Bick (2010) and Seleteng et al. (2013). As a result, it might be misleading in the **threshold** estimation. Kremer et al. (2013) therefore propose a methodology, namely Dynamic Panel **Threshold** Regression (DPTR), which improve and overcome some problems building on Hansen (1999), Caner and Hansen (2004).

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It is noticeable from the results as shown by **growth** regression model that **Growth** Rate of State Economy Output is positively related to the Total **Inflation** Rate of State Economy. This variable is highly significant as P-value is just 0.001 percent. It is a well observed from the above equation that one percent increases in output **growth** rate leads to 0.10 percent increase in the **inflation** rate in the Total **Inflation** Rate of State Economy. The results of our regression models have clearly proved the structuralists’ theory of **inflation** which expresses that **inflation** is essential for economic **growth**, and it refutes the monetarists view about **inflation** and economic **growth** who believed that **inflation** is detrimental to economic progress. Although, the **relationship** **between** **growth** rate of output and **inflation** is positive, but it is not so strong as Structuralists believed as it is evident from the R 2 - value which is just 15.1%

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This study examines the steady-state **growth** effect of **inflation** in an endogenous **growth** model in which Calvo-type nominal rigidity with endogenous contract duration and monetary friction via wage-payment-in-advance constraint are assumed. On the balanced-**growth** path in this model, the marginal **growth** effect of **inflation** is weakly negative or even positive at low **inflation** rates because the effect on average markup offsets the negative marginal **growth** effect through the monetary friction, but the **growth** effect of **inflation** is negative and convex at higher **inflation** rates because the frequency of price adjustment approaches that of the flexible-price economy and the **growth** effect through the nominal rigidity is dominated by the **growth** effect through the monetary friction. With a plausible calibration of the structural parameters, this model generates a **relationship** **between** **inflation** and **growth** that is consistent with empirical evidence, particularly in industrial countries.

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This study examines the steady-state **growth** effect of **inflation** in an endogenous **growth** model in which Calvo-type nominal rigidity with endogenous contract du- ration and monetary friction via wage-payment-in-advance constraint are assumed. On the balanced-**growth** path in this model, the marginal **growth** effect of infla- tion is weakly negative or even positive at low **inflation** rates because the effect on average markup offsets the negative marginal **growth** effect through the monetary friction, but the **growth** effect of **inflation** is negative and convex at higher **inflation** rates because the frequency of price adjustment approaches that of the flexible- price economy and the **growth** effect through the nominal rigidity is dominated by the **growth** effect through the monetary friction. With a plausible calibration of the structural parameters, this model generates a **relationship** **between** **inflation** and **growth** that is consistent with empirical evidence, particularly in industrial countries.

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The results also show that the per capita real GDP **growth** rate also significantly and positively affects the saving rate. Thus along with higher income, at higher **growth** rate of income, too, the saving rate is higher. The effect of dependency ratio is statistically insignificant but the negative sign provides support to the findings of Loayza et al. (2000) about the lifecycle theory that, as dependency ratio increases, saving rate would decrease. The insignificant parameter of real interest rate rejects the classical theory of positive **relationship** of savings with real interest rates. This is contrary to the finding of Athukorala and Sen (2004) who found a positive impact of the real interest rates on the savings in India. This is perhaps because they did not adjust for the simultaneity bias. **Inflation** is also found positively but statistically insignificantly related to savings in the present study. This is contrary to the findings of Chopra (1988) who obtained a significant positive impact of **inflation** on savings in the case of the Indian economy prior to 1982. The positive **inflation** coefficient can be explained in terms of the people wanting to preserve the real value of their wealth in presence of **inflation**, perhaps because the social security and health concerns are not adequately addressed by the existing institutional network in these countries. As a result, the **inflation** induced increased macroeconomic uncertainty forces people to save more. This is contrary to the findings of Heer and Suessmuth (2006).

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Above studies have exogenously determined the **threshold** and tested for empirical signiﬁcance. Recent authors use PSTR model to resolve the drawback of the external **threshold** determina- tion. Omay and Öznur Kan (2010) analyze empirical **relationship** **between** inﬂation and **growth** using PSTR model for six indus- trial countries in the period of 1972-2005. They provide evidence that there exists the inﬂation **threshold** level of 2.52%. Relation- ship **between** inﬂation and **growth** is negative when inﬂation rates are above this **threshold** level. After testing the robustness of this **relationship** by using Seemingly Unrelated Regression (SUR), the **threshold** levels change slightly from 2.42% to 3.18%, respectively. A similar study is carried by López-Villavicencio and Mignon (2011) , who estimate the **growth** **effects** of inﬂation on a sample of 44 countries in the period of 1961-2007. Based on PSTR and GMM models, they ﬁnd that inﬂation non-linear impacts the **growth** with **threshold** level of 5% for whole sample, 1.23% for developed countries, 14.54% for emerging countries, 10.273% for upper middle countries and 19.64% for lower middle and low countries, respec- tively. They conclude that above the **threshold**, which inﬂation has a negative effect on **growth** and below which it enhances **growth** for developed countries. In addition, Seleteng et al. (2013) use PSTR model to estimate the inﬂation and-**growth** **relationship** in the South African Development Community (SADC) region over the period of 1980–2008. The study ﬁnds a **threshold** level of 18.9% in the SADC region. Eggoh and Khan (2014) examine the **threshold** **effects** in the inﬂation-**growth** **relationship** by a panel of 102 devel- oped and developing countries over the period of 1960-2009 apply- ing PSTR and GMM models. They also control some country-based macroeconomic characteristics such as ﬁnancial development, capital accumulation, trade openness and government expendi- tures, which inﬂuence this **relationship**. The results specially show that a **threshold** level of 10.5% for global, 3.4% for high-income countries, 10% for upper middle-income countries, 12.9% for lower middle-income countries and 19.5% for lower income countries, respectively. Using data with a balanced panel of 92 developing countries from 1975 to 2004, Baglan and Yoldas (2014) estimate a ﬂexible semi-parametric panel data model and ﬁnd that inﬂa- tion becomes a signiﬁcant detriment to **growth** only after it reaches about 12%. Moreover, they also ﬁnd that the **relationship** ceases to be statistically signiﬁcant at very high levels of inﬂation.

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better to give a different theoretical explanation for this empirical result. In empirical literature, which deals with the bi-directional **relationship** **between** output **growth** and **inflation**, there is considerable amount of study which finds negative causal effect of output **growth** on **inflation** for Turkey (Kökocak and Arslan, 2006). Thus, with this new finding, we can construct a new theoretical reasoning for our empirical result. In this case, more output **growth** would be accompanied by more average **inflation** on the contrary to Short-run Phillips Curve. Furthermore, decreasing **inflation** rates leads to less **inflation** uncertainty due to Friedman’s and Okun’s hypothesis. In summary, increasing output **growth** leads to less nominal uncertainty which shows that Friedman’s and Okun’s hypothesis is still valid and is not contradicted with the above arguments. Thus, we find a negative **relationship** **between** output **growth** and nominal uncertainty by using Friedman’s and Okun’s hypothesis. Furthermore, for the transmission channel of this **relationship** Omay and Hasanov (2010) can be examined for Turkey.

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of scale, increasing returns or induced technological change; in contrast to exogenous factors such as population **growth** increases (Solow, 1994). The studies of Lucas (1982), Svensson (1985) and Lucas and Stokey (1987) represent blueprint works which depict the negative effect **inflation** within an endogenous **growth** model whereby **inflation** acts as a tax on the return to all forms of capital and ultimately economic **growth**. Furthermore, these endogenous models are responsible for the emergence of dynamic nonlinear effect of **inflation** on **growth**, with the study of Gillman ad Kejak (2004) being amongst the first to depict such a nonlinear **relationship** in which the Tobin effect (i.e. positive **inflation**-**growth** **relationship**) is found at low levels, whereas at higher levels the negative Stockman effect comes into play. The models main attribute is the ability for the representative agent to choose **between** two competitive mechanism, money and credit and an increased use of credit such that an initial increase in marginal cost of money (i.e. **inflation**)causes an initial increase in the return to capital which later turns negative hence dictating the nonlinear **inflation**-**growth** **relationship**. Moreover, other theoretical studies presented by Huybens and Smith (1999) and Bose (2002). Low **inflation** does not distort information or interfere with resource allocation and economic activity up to certain **inflation** **threshold** of which crossed, **inflation** aggravates the credit market through distorted flow of information.

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Based on the results presented in Table 7, hypotheses (a), (b), (c), (e) and (f) were not rejected at 5% level of significance because their p-values are individually greater than 0.05. But the null hypothesis (d) was rejected at 5% level of significance since the p-value is less than 0.05. This means that during the period under study, there was no causality **between** agricultural **growth** and economic **growth** because the null hypothesis that agriculture GDP does not Granger cause GDP was not rejected. This result is inconsistent with the findings of Jatuporn, Chien, Sukprasert, and Thaipakdee (2011) who found a bi-directional **relationship** running from agriculture to economic **growth** and from economic **growth** to agriculture in Thailand. There was also no causality detected **between** **inflation** and agricultural **growth** at 5% level of significance, meaning that **inflation** does not Granger cause agriculture. The results also show that, within the sample of the study, there was unidirectional causality from economic **growth** to **inflation** in Swaziland.

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In line with the study of Ahmed and Mortaza (2005) and Chowdury and Ham (2009), the paper restricts the empirical analysis of **threshold** **effects** in the **inflation**-**growth** correlation to the bivariate case. Ahmed and Mortaza (2005) investigate **threshold** **effects** in the bivariate **inflation**-**growth** correlation by estimating Hansen‟s (1997) **threshold** model whereas Chowdury and Ham (2009) opt for the use of a bivariate **threshold** autoregressive (BTVAR) model. Derivation of the BTVAR model is instigated through an augmentation of Hansen (1997) TAR model, which according to Chowdury and Ham (2009), is achieved by replacing the dependent and independent variables in the TAR model with vectors of bivariate endogenous variables. This paper builds on Chowdury and Ham (2009) by specifying a vector of bivariate endogenous (**inflation** and economic **growth**) **threshold** variables, which is directly incorporated into the BTVAR model specification. In limiting the study to a bivariate case study the paper is able to follow in pursuit Lo and Zivot (2001) in deriving an associated bivariate **threshold** vector error correction (BTVEC) mechanism directly from the BTVAR model. Developments by Lo and Zivot (2001) provide a unique approach into accommodating equilibrium adjustment mechanisms as a means of eliminating spurious relations which could possibly arise within the **threshold** vector autoregressive (TVAR) models. Our baseline BTVAR model is specified as:

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To summarize, most of empirical research supports the idea of negative **relationship** **between** **growth** and **inflation**, but various types of findings exist. The general finding is that **between** **growth** and **inflation** there is a negative **relationship**. In some cases even low or moderate **inflation** rate have an impact on **growth** rate. In developed countries there isn’t evidence that such a correlation exists. **Relationship** **between** **inflation** and economic **growth** might be nonlinear. Developing countries and developed countries show different forms of nonlinearity in the **inflation**-**growth** **relationship** and the size of the negative effect of **inflation** on **growth** declines as the **inflation** rate increases. Some authors advance the idea of a **threshold** in the dependence **growth**-**inflation**. In a case, higher **inflation** is associated with moderate gains in GDP **growth** up to a roughly 15-18 percent **inflation** **threshold**. In a case, association **between** **growth** and **inflation** is negative but stronger at lower levels of **inflation**. Control of **inflation**, as recommended by IMF, (Albania is one case), has been effective vis-à-vis **growth** in some countries. In some cases **inflation** could be in short run **relationship** with **growth**, but in some other also in long run **relationship**.

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The **inflation**-output **relationship** has usually been described using the Phillips curve, which assumes that **inflation** rate and output **growth** rate are positively correlated, at least in the short run. Such a positive **relationship** **between** **inflation** and output is a common feature of many macroeconomic models, including sticky-wage models (Fischer, 1977; Taylor, 1979; 1980), sticky-price (or menu costs) models (Parkin, 1986; Blanchard and Kiyotaki, 1987; Caplin and Spulber, 1987), as well as misperception model of Lucas (1972). In sticky-wage models, higher **inflation** rate reduces average real wages and thus increases employment and hence output. In menu costs models, **inflation** rate reduces relative prices of monopolistic firms and as a result, demand for their products and hence output rise in response to nominal demand shocks. According to Friedman (1968), if commodity prices respond to nominal shocks faster than prices of production factors, then **inflation** will reduce real wages and increase employment in the short run. However, in the long run, the economy shall return to natural unemployment rate. Lucas (1972) argued that if the information content of market prices is not sufficient to distinguish **between** real and nominal shocks, then producers shall increase their outputs in response to all price increases in the short run. Tobin (1965) and Mundell (1963) argued that **inflation** might permanently increase output **growth** rate by stimulating capital accumulation, because in response to **inflation** households would hold less in money balances and more in other assets. Fischer (1979), on the other hand, argued that although steady-state capital accumulation is independent of **inflation** rate, **inflation** might increase capital accumulation on the transition path to steady-state. However, as Temple (2000) argues, it would now be hard to find much support for the view that **inflation** can raise output in the long run. Indeed, real business cycle models (Long and Plosser, 1983; King and Plosser, 1984; Plosser, 1989) viewed fluctuations in real economic variables, including output, as results of variations in the real opportunities of the individual agents.

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315 model he developed. Van der Ploeg and Alogoskoufis (1994) addressed Sidrauski's (1967) model, in which utility was used instead of cash, and researched inflation's **effects** on **growth** (Blackburn and Hung,1996:2). Barro (1995) concludes that there is a significant negative **relationship** **between** **inflation** and economic **growth**. He reaches to the conclusion that 10% **inflation** increase reduces real gross national product at a rate **between** 0.2-0.3% annually. In a study he conducted for South Africa, Nell (2000) concluded that when **inflation** is at single-digit numbers, it can be profitable in terms of **growth**, however, when it reached to double digit numbers, it imposes costs and slow down the **growth**. In a study conducted for Brasil, Faria and Carneiro (2001) reached to the conclusion that **inflation** may have real **effects** on the output in the short term, but it doesn't have any real **effects** in the long term. In a study conducted for Bangladesh, India, Pakistan and Sri Lanka, Malik and Chowdury (2001) reached to the conclusion that there is a long-term positive **relationship** **between** **inflation** and **growth** rate, and moderate **inflation** may have positive **effects** on **growth**. Gokal and Hanif (2004) concluded that there is a weak and negative correlation **between** **inflation** and **growth**. According to McCandless and Weber(1995) while there is a high correlation **between** money supply and **inflation**, there is no correlation **between** money supply and **growth** and there is no correlation **between** **inflation** and **growth**. Paper of Rolnick and Weber(1998) finds that a high correlation **between** money **growth** and **inflation**.

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The optimal lag of one was selected as indicated by lag length selection criterion (Schwarz information criterion, Final prediction error and Hannan-Quinn information criterion). The result of bound test for cointegration in table 1, indicates that null hypotheses cannot be rejected because the F- statistics(0.655646) is less than upper bound value (4.85) at 5 percent critical value for case III (Unrestricted intercept and no trend) as it is found in M. H. Pesaran, Y. Shin and R.J Smith critical table. Therefore, there is no long run **relationship** **between** **inflation**, unemployment and real GDP **growth** rate in Nigeria. Similar study conducted by Fouzeia, et al. (2015) observed that there is no long run **relationship** **between** unemployment and Gross Domestic Product (GDP) in Egypt.

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The analysis of the **relationship** **between** exchange rate and **inflation** in the economic **growth** process is often achieved using regression analysis which can be explicitly or implicitly stated based on a theoretical framework of endogenous models (King & Levine, 2004). Thus, the level of impact of exchange rate and **inflation** on the economic **growth** of Nigeria is assumed to be influenced by several variables as “y” which represents the official gross domestic product (GDP) and “x” which include among others, **inflation** (INFLA) and Exchange rate (EXCH) .If these assumptions are right, then a multiple linear regression analysis could be adopted and specified thus;

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Barro (1996) analyses the effect of **inflation** and other variables like fertility, democracy and others on economic **growth** in different countries for a period of 30 years. He uses system of regression equation in which other determinants of **growth** are held constant. To estimate the effect **inflation** on economic **growth** without looking at the endogeneity problem of **inflation**, he includes **inflation** as explanatory variable over each period along with other determinants of economic **growth**. The result indicates that there is a negative **relationship** **between** **inflation** and **growth** with a coefficient of -0.024. One problem arising from the above conclusion is that the regression may not show causation from **inflation** to **growth**. **Inflation** is an endogenous variable that my respond to **growth** and other variables related to **growth**. For example an inverse **relationship** **between** **inflation** and **growth** may arise if an exogenous falling down of **growth** rate tended to generate higher **inflation** rate. He uses instrumental variables like independence of the central bank, lagged **inflation** and prior colonial status, each these variables are related to **inflation**, to avoid this problem. The result is statistically significant and strengthens the negative **relationship** **between** the **inflation** and **growth**. Thus, there is some reason to believe that the relation reflect causation from higher long term **inflation** to reduced **growth**. Finally, he concludes that even though the results looks small, the long-term **effects** on standards of living can be substantial.

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This paper investigates the **relationship** **between** **inflation** and output in the context of an economy facing persistently high **inflation** and **inflation** shocks. The authors highlighted various existing theories, stating three possible results of the impact of **inflation** on **growth**; negative, positive or none. The authors also cited a number of studies completed by various other authors including Eckstein and Leiderman (1992), Gillman (1993), Smyth (1992, 1994, and 1995) and De Gregorio (1993). By analyzing the data for Brazil, the authors found that **inflation** does not impact **growth** in the long-run, but in the short-run there exists a significant negative effect from **inflation** on output. The authors imposed minimal structure and made use of the idea that **inflation** shocks can be broken down into permanent and temporary components.

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Friedman’s hypothesis regarding the **relationship** **between** **inflation**, **inflation** uncertainty and output **growth** states that full employment policy objective of the government tends to increase the rate of **inflation** which increases the uncertainty about the future course of **inflation**. Increase in **inflation** uncertainty lowers economic efficiency and reduces output **growth**. There are very few studies for underdeveloped countries particularly for India regarding the **relationship** **between** **inflation**, **inflation** uncertainty and output **growth**. Thornton’s (2006) study regarding the **relationship** **between** **inflation** and **inflation** uncertainty in India is univariate in nature and it cannot establish the **relationship** **between** **inflation** uncertainty and output **growth**. This study intends use the bivariate GARCH model to find out the relation **between** **inflation**, **inflation** uncertainty and output **growth** simultaneously. In this study we use monthly data of wholesale price index (WPI) and index of industrial production (IIP) of India as the proxies of price and output respectively from 1950:1 to 2011:12. Following Fountas, Karanasos and Kim (2002) we have used the following bivariate GARCH model to estimate simultaneously the means, variances and covariances of **inflation** and output **growth**. We use Granger- causality test to know the statistical **relationship** **between** average **inflation**, output **growth**, **inflation** uncertainty and output **growth** uncertainty. We find strong evidence that increase in average **inflation** raises **inflation** uncertainty and increase in **growth** rate increases the **growth** rate uncertainty. But we do not find any statistically significant **relationship** **between** **inflation** uncertainty and output **growth** rate.

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