Why You Should Use High-Frequency Data to Test the Impact of Exchange Rate on Trade

Shaar, Karam; Khaled, Mohammed

doi:10.1007/978-3-319-70055-7_21

Cited by 2 publications

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“…Error Correction models were estimated in the reported studies which provided separate estimates for long‐run and short‐run periods. However, most of the studies considered annual data with small sample sizes and cointegration analysis using small sample might be misleading (see Shaar & Khaled, 2017; Zhou, 2001). Some studies used quarterly data where GDP annual data were converted into quarterly data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, the only limitation of the study is that it does not make distinction between the short‐run and long‐run policy recommendations. This distinction may be made by estimating error correction model which requires high frequency data (Shaar & Khaled, 2017; Zhou, 2001), that is, at least quarterly data. However, Bayesian estimates along with PDHI are important for policy steps even on quarterly basis as the mean estimates along with PDHI estimates are obtained from posterior probability distribution under the assumption that parameters are random.…”

Section: Limitations Of the Studymentioning

confidence: 99%

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Effects of inflation uncertainty and exchange rate volatility on money demand in Pakistan: Bayesian econometric analysis

Akbar

2021

Int. J Fin Econ

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The study explores determinants of money demand (M2) in a developing economy of Pakistan using annual data ranging 1976-2017. The study contributes by two ways. Firstly, it specifically examines the potential impact of exchange rate volatility and inflation uncertainty along with conventional determinants on money demand in Pakistan. Secondly, Bayesian Statistical inference is employed and the estimates are interpreted along with highest posterior density intervals (HPDI) with probability statements in order to cope with the issue of the instability of parameters. The analysis explores the positive impact of real income as well as share prices and the negative effect of interest rate on money demand as predicted by theory and earlier findings. Significant negative estimate of exchange rate shows that substitution effect overcomes wealth effect during exchange rate variations. But magnitude of exchange rate elasticity seems to depend upon strength of depreciation rate and/or future expectations, which may be considered an important research question for future work. Insignificance of exchange rate volatility for money demand in Pakistan is found which requires further research as to know about the behaviour of wealth effect and expectation effect during forex market uncertainty. Most importantly, inflation uncertainty is found to be a potential determinant of money demand in Pakistan. Rising uncertainty of inflation rate seems to have direct effects on precautionary and transaction motives of the demand for money. Findings of this study provides useful insights for policymaking in Pakistan as well as in other developing economies by suggesting inflation uncertainty as the potential determinant of money demand. Finally, Bayesian estimates along with HPDI estimates provide new information about the effects and particularly about the possible range of elasticities.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Limitations Of the Studymentioning

confidence: 99%

Effects of inflation uncertainty and exchange rate volatility on money demand in Pakistan: Bayesian econometric analysis

Akbar

2021

Int. J Fin Econ

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Essays on modern economic issues in international trade, exchange rates and housing

Shaar¹

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<p>The common theme of the three papers in this thesis is the focus on the impact of data choices on empirical research in Economics. Such choices can be about the source of data; should we source the data from country A or country B in a bilateral trade relation? Is there a way to reconcile the discrepancies in international trade data? In investigating the impact of exchange rate on trade, should we choose high-frequency or low-frequency data? What does the choice of a certain frequency imply for the econometric analysis? In assessing the impact of housing wealth on household consumption, what are the benefits of choosing household-level data? How can we take advantage of aggregate data on house prices to circumvent the endogeneity arising from household-specific confounding factors? This thesis shows that data choices can strongly affect our conclusions regarding several modern economic issues. The first paper is titled ‘Reconciling International Trade Data.’ International trade data are filled with discrepancies–where two countries report different values of trade with each other. We develop an index for ranking countries’ data quality based on the following notion: the more a country’s reports on bilateral trade differ from the corresponding reports of its partners, the more likely it is a low-quality reporter. We calculate the comparative quality for each country’s imports and exports separately for every year from 1962 to 2016. We reconcile international trade data through picking the value reported by the country with higher quality in every bilateral flow. The findings include: (a) global trade was under-reported by roughly 5% over the past five years as countries with low data quality under-report both, their imports and exports; (b) erroneous reporting is prevalent among low-quality reporters; (c) importers’ data are less likely to be in error; (d) the level of development and corruption are possible determinants of trade data quality; (e) low-quality reporters are 14% more open to trade using reconciled data than using self-reported data (f) China tends to under-report its exports and over-report its imports, while there is only a small difference between US self-reported and reconciled data. The reconciled trade dataset is made freely available for future studies to use. The second paper is titled ‘Why You Should Use High Frequency Data to Test the Impact of Exchange Rate on Trade.’ The paper suggests that testing the impact of exchange rate on trade should be done using high frequency data. Using different data frequencies for identical periods and specifications between the US and Canada, the paper shows that low frequency data suppresses and distorts the evidence of the impact of exchange rate on trade in the short-run and the long-run. The third paper is titled: ‘Housing Leverage and Consumption Expenditure: Evidence from New Zealand Microdata.’ The paper investigates how household debt affects the marginal propensity to consume out of housing wealth. The paper uses New Zealand household-level data on spending, income, and debt over the period 2006–2016. The main empirical challenge is to identify exogenous variation in house prices to determine how consumption evolves with movements in household wealth. This identification problem is complicated by the presence of unobserved household characteristics that are correlated with housing wealth. The paper uses a detailed house sale dataset to derive local average house prices and use it as an instrument. The empirical results show that the estimated elasticity of consumption spending to housing wealth is about 0.22%. In dollar terms, the average marginal propensity to consume out of a one-dollar increase in housing wealth is around 2.2 cents. The empirical results confirm that household indebtedness, especially mortgage debt, acts as a drag on consumption spending, not only through the debt overhang channel, but also through influencing the collateral channel of the housing wealth effect.</p>

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Why You Should Use High-Frequency Data to Test the Impact of Exchange Rate on Trade

Cited by 2 publications

References 4 publications

Effects of inflation uncertainty and exchange rate volatility on money demand in Pakistan: Bayesian econometric analysis

Effects of inflation uncertainty and exchange rate volatility on money demand in Pakistan: Bayesian econometric analysis

Essays on modern economic issues in international trade, exchange rates and housing

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