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Exchange rate changes and money demand in Albania: a nonlinear ARDL analysis

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Abstract

A good part of the empirical literature on money demand focuses on searching for a stable long-run money demand function, an essential part of a successful monetary policy. Beyond the typical money demand specification, which includes income and interest rates, Mundell (Can J Econ Polit Sci 29:475–485, 1963) made a case for the exchange rate as an important determinant of money demand working through currency substitution. This paper contributes a new approach to test the short- and long-run effects of currency fluctuations on money demand in Albania, a small open economy without deep financial markets. More specifically, we examine the case for asymmetric effects of exchange rate fluctuations on money demand by using a nonlinear adjustment mechanism within an ARDL model. Using data for 1996–2016 from Albania, we show that the money demand is stable in both the linear and nonlinear specifications. The nonlinear model reveals an asymmetric effect of exchange rates on money demand, with depreciations reducing money demand, likely due to a substitution effect amplified by a relatively dollarized economy.

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Notes

  1. Once normalization takes place we have \(b = \frac{{\rho_{1} }}{{ - \rho_{0} }},\;c = \frac{{\rho_{2} }}{{ - \rho_{0} }},\;d = \frac{{\rho_{3} }}{{ - \rho_{0} }}\).

  2. Note that Pesaran et al.’s (2001) critical values are for large samples. For small samples such as ours, we use critical values supplied by Narayan (2005).

  3. See Pesaran et al. (2001).

  4. Studies by Pesaran and Shin (1995a, b), Pesaran et al. (2001) posit that when a system contains both I(0) and I(1) series, use of conventional cointegration tests can bias the results of the long-run equation.

  5. See Shin et al. (2014, p. 291).

  6. Under this alternative test, following Bahmani-Oskooee and Tanku (2008) we use normalized long-run estimates from Panel B and long-run model (1) and generate the error term called ECM. We then replace linear combination of lagged level variables in (2) by ECMt−1 and estimate the new specification after imposing the same optimum lags from Panel A. A significantly negative coefficient attached to ECMt−1 will support cointegration. Since the t test with new critical values is used to judge significance of this estimate, the test is also known as the t test for cointegration. Like the F test, Pesaran et al. (2001, p. 303) also tabulate an upper and a lower bound critical values for this test. However, since their critical values are for large samples, we will rely up on Banerjee et al. (1998) who introduced this test within Engle–Granger method and who tabulated critical values for small samples too.

  7. Other diagnostics are similar to those of the linear model with no need for additional analysis. For some other application of these and other nonlinear methods see Delatte and Lopez-Villavicencio (2012), Nusair (2012), Wimanda (2014), McFarlane et al. (2014), Kisswani and Nusair (2014), Gogas and Pragidis (2015), Baghestani and Kherfi (2015), Zemami and Ben-Salha (2015), Bahmani-Oskooee and Durmaz (2016), Bahmani-Oskooee et al. (2015, 2017a, b, 2018, Bahmani-Oskooee et al. 2019a), Al-Shayeb and Hatemi-J.(2016), Lima et al. (2016), Arize et al. (2017), Gregoriou (2017), Olaniyi (2019), and Istiak and Alam (2019).

  8. The six parliamentary elections during our study period we held on 5-26-1996; 6-23-1997; 6-24-2001; 7-3-2005; 6-28-2009; and 6-23-2013. The six local elections were held on 10-13-1996; 10-1-2000; 10-12-2003; 2-18-2007; 5-8-2011; and 6-21-2015. Each dummy took a value of 1 during the quarter in which the election was held and zero otherwise. For election details and dates see https://www.osce.org/odihr/elections/albania?page=2.

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Authors and Affiliations

  1. University of Wisconsin – Milwaukee, Milwaukee, USA

    Mohsen Bahmani-Oskooee

  2. University of Michigan – Dearborn, Dearborn, USA

    Ilir Miteza

  3. Bank of Albania, Tirana, Albania

    Altin Tanku

Authors
  1. Mohsen Bahmani-Oskooee
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  2. Ilir Miteza
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  3. Altin Tanku
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Correspondence to Mohsen Bahmani-Oskooee.

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Appendix: Data definition and sources of data

Appendix: Data definition and sources of data

Quarterly data over the period 1996I–2016IV are used to carry out estimation. The data come from the following two sources:

  1. a.

    Bank of Albania.

  2. b.

    Albanian Institute of Statistics (INSTAT).

1.1 Variables

Ons of money and exchange rates are obtained from the Bank of Albania, while income (GDP) and CPI data are obtained from Albanian institute of Statistics (INSTAT): 


Real money (M2) Nominal M2 is deflated by CPI to obtain real money. While nominal M2 data come from source a, the CPI data come from source b.

Real income (Y) This variable is constructed by dividing the nominal GDP by the CPI. The data for both variables come from source b.

Inflation rate (π) CPI is used to generate this variables as Ln(CPIt/CPIt−1). The CPI data come from source b.

Exchange rate (EX) this variable is represented by the nominal effective exchange rate (NEER) defined as units of foreign currency per unit of Albanian LEK. Therefore, by way of construction, a decline reflects a depreciation of the LEK. It is constructed as weighted average of currencies of five major trading partners. The weights are based on the sum of Albanian imports from and exports to each partner as a percent of sum of Albanian aggregate imports from and exports to the world. The average weight for each partner over the study period is as follows: 6.24% for Chinese Yuan; 5.74% for German Mark; 8.86% for Greek Drachma; 35.68% for Italian Lira; and 5.80% for Turkish Lira. Note that we used conversion factors to convert euro to Mark, Drachma, and Italian Lira to Lek during post-euro era.

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Bahmani-Oskooee, M., Miteza, I. & Tanku, A. Exchange rate changes and money demand in Albania: a nonlinear ARDL analysis. Econ Change Restruct 53, 619–633 (2020). https://doi.org/10.1007/s10644-019-09261-9

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