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My Dissertation Proposal (Modified)


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Slides for my dissertation proposal, presented to my panel on February 22, 2010. These slides have been modified with some comments made during the actual presentation.

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My Dissertation Proposal (Modified)

  1. 1. Properties of Philippine Remittances (An Econometric Analysis) Dissertation Proposal
  2. 2. The aim of this presentation is to convey exactly what it is I want to do, communicate an idea of what I’ve already done and what I’m aware needs to be done, and thereby get some guidance so I know how to proceed from here on out. (I concede it’s possible the paper doesn’t get this across as well as a presentation might.) Anything in these red-trimmed grey boxes won’t appear on the slides. These are just annotations to help read through the presentation since I’m not around to deliver it.
  3. 3. This Presentation will touch upon: The Motivation for the research. A description of the data Treatment. Initial departures and preliminary Results. Issues and Directions to explore.
  4. 4. The Motivation for the research.
  5. 5. Remittances are particularly significant for the Philippines and for Filipinos .
  6. 6. The Filipino Diaspora and Workers’ Remittances 15 The two sets of bars are on different scales, but illustrate the growth in OFW Remittances. 10 5 0 2000 2001 2002 2003 2004 2005 2006 2007 Overseas Filipinos Workers’ Remittances (in Millions of Filipinos) (in Nominal US$ Billions) Sources: International Labor Organisation (2009) and Bangko Sentral ng Pilipinas (2008)
  7. 7. The Seven Largest Remittance Recipient Countries India China Mexico Remittances as a percentage of GDP is Philippines much larger in the Philippines than in any of France the largest remittance recipient economies. Spain Germany 0 15 30 45 60 Remittances % of GDP (in Nominal US$ Billions) (2008 GDP) Source: The World Bank Migration and Remittances Data (November 2009)
  8. 8. “Stylized facts”about remittances SOME tions contrast with findings from individual country cases. Observa e My argument is essentially that the Philippines is an abo ut th interesting case study, as shown in the prior slides. e lite ratur S) Some approaches have yet to be applied TANCE (ON REMIT to the Philippine case. Many studies have approached the topic from a development perspective. Some humor Of the approximately 102,000 articles (haha!) on remittances available on Google Scholar, not a single one was written by me!
  9. 9. Apply new approaches to Philippine Data. Inquire into the cyclicality and other properties of remittances to the Philippines. This was the starting point that led me to explore different directions for this research. Approach the topic from a - macroeconomic perspective. Develop the research like an Wh open-source project. y No t.. .? This idea came up because I decided to use R.
  10. 10. The Treatment applied to the data
  11. 11. Data for Overseas Filipino Workers’ (OFW) remittances and other macroeconomic variables were obtained for 1989Q1 to 2008Q4. Each time series (v) was assumed to have the following components: vt = trend t + cyclical t + seasonal t + et Three techniques were tried to remove the seasonal component of the data. Two of these were performed using the R functions stl( ) and decompose( ). The third involved regression with dummy variables. Two business cycle filters were considered to remove the time trend and isolate the cyclical component of the data. The Hodrick-Prescott Filter was chosen becaused of its use in macroeconomics. The Christiano Fitzgerald filter was also considered since it is a broader filter.
  12. 12. ln(y) (Raw Data) ln(r) (Raw Data) 5.0 7.4 4.5 7.2 4.0 7.0 y r 3.5 6.8 3.0 6.6 1990 1995 2000 2005 1990 1995 2000 2005 Quarterly Frequency, 1989Q1-2004Q4 1989Q1-2008Q4 Quarterly Frequency, 1989Q1-2004Q4 1989Q1-2008Q4 This is what the raw data looks like...
  13. 13. HP Filtered y by Seasonal Adjustment Technique CF Filtered y by Seasonal Adjustment Technique 0.04 0.02 0.02 Growth Rate (Percent) Growth Rate (Percent) 0.00 0.00 -0.02 -0.02 -0.04 -0.04 by LOESS by LOESS by Moving Averages by Moving Averages by Dummy Variables by Dummy Variables 1990 1995 2000 2005 1990 1995 2000 2005 Quarterly Frequency: 1989Q1 to 2008Q4 Quarterly Frequency: 1989Q1 to 2008Q4 Percentages are Normalized to 1 Percentages are Normalized to 1 This is what deseasonalized y (ln GDP) data looks like after filtering...
  14. 14. HP-Filtered r by Seasonal Adjustment Technique CF Filtered r by Seasonal Adjustment Technique 0.4 0.6 0.3 0.4 0.2 Growth Rate (Percent) Growth Rate (Percent) 0.2 0.1 0.0 0.0 -0.1 -0.2 -0.2 by LOESS by LOESS -0.4 by Moving Averages by Moving Averages by Dummy Variables by Dummy Variables -0.3 1990 1995 2000 2005 1990 1995 2000 2005 Quarterly Frequency: 1989Q1 to 2008Q4 Quarterly Frequency: 1989Q1 to 2008Q4 Percentages are Normalized to 1 Percentages are Normalized to 1 While deseasonalized r (ln remittance) looks like this after filtering.
  15. 15. Preliminary Results thus far from work on the topic
  16. 16. First, the research explores the cyclicality of workers’ remittances to the Philippines. Based on the literature, cyclicality unearths questions of whether remittances are sent out of altruistic or profit-seeking motives. In keeping with the literature, the idea was to develop regression models using variables relevant to remittance sending: r = f( GDP , Past remittance , Exchange Rate ) I would apply and extend this model subsequently to consider different cases.
  17. 17. Applied to Philippine remittances in general, the model yielded different results depending on the filtering technique. Thus, I proceeded to consider whether it would be useful to disaggregate remittance by source country.
  18. 18. Remittances from the US appeared to indicate that all variables were statistically significant – if the CF-Filtered data were used.
  19. 19. For Japanese remittances, the real effective exchange rate was a significant regressor regardless of the filtering method, though the relationship (sign) varied for regressors using CF-Filtered data.
  20. 20. Remittances from the UK appeared to behave consistently (based on the model) regardless of filtering method employed. However, regressors did show different relationships compared to US and JP cases.
  21. 21. Applying the model to the case of Canadian remittances to the Philippines...
  22. 22. ...and to Australian remittances to the Philippines.
  23. 23. Conjecture: Perhaps the different remittance-sending behavior described by the models is due to the type of OFW deployed in each particular country. Because of the different results, I looked up worker deployment in the different countries, and found that there is normally a particular type of worker who finds employment in a specific country.
  24. 24. In the parlance of the literature, it appeared that the more skilled the type of worker, the more remittances fit a profit-seeking profile (that is, procyclical with the home country economy). In contrast, the remittance-sending behavior of less skilled workers might be described along the lines of altruism (that is, counter-cyclical with the home country economy).
  25. 25. Vector autoregressions were employed with the end in view of understanding remittances’ macroeconomic impact. A lag length of one was used to prevent overfitting using VARs. Impulse-response functions were also employed to model the effect of shocks to the variables. Initially, this was the favored approach to the research, since VARs allow one to model the interrelationship between variables via IRFs. (Not to mention much of what has already been said about VARs and “macroeconomics without theory”.)
  26. 26. IRFS USING HP-FILTERED DATA Orthogonal Impulse Response from hp.ya Orthogonal Impulse Response from hp.ra 0.15 0.02 0.10 hp.ya hp.ya 0.00 0.05 -0.02 0.15 0.00 xy$x xy$x 0.02 0.10 hp.ra hp.ra 0.00 0.05 -0.02 0.00 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10
  27. 27. IRFS USING CF-FILTERED DATA Orthogonal Impulse Response from cf.ya Orthogonal Impulse Response from cf.ra 0.06 0.02 0.04 0.01 cf.ya cf.ya 0.02 0.00 0.00 0.06 -0.02 0.02 xy$x xy$x 0.04 0.01 cf.ra cf.ra 0.02 0.00 0.00 -0.02 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10
  28. 28. In the simple bivariate case, it seemed that a shock to remittance had little effect on the economy. On the other hand, the model was unable to consistently show what effect a shock to the economy would have on remittance receipts (seeing as the IRFs differed depending on filtering technique used). Thinking that this could be attributed to the simplicity of the model, I decided to try out a more complicated model.
  29. 29. Unfortunately, I made a mistake in specifying this model: y appears in both the regressor and regressand (c+i+g+x-m). (Oops!)
  30. 30. Issuesand Directions to explore
  31. 31. Resolved: The problem, apparently, is that an HP-Filter leaves more Why are there different results between noise compared to a CF-Filter. different filtering techniques? However, a Double HP-Filter more closely approximates a CF-Filtered series. Underlying question: what happens if the remittances are valued in different currencies (i.e. host country/dollar/peso)? Note: there Can the exchange rate effects be are also different models that can accounted for in the analysis? be applied to examine this question more thoroughly. How to handle structural breaks in the data? This can be a key section of the How do remittances measure up dissertation, probably following against other inflows like FDI? the work of Vargas-Silva. What other macroeconomic variables would be relevant to the analysis?
  32. 32. END OF PRESENTATION T H A N K Y O U Brian L. Belen @brianbelen