How Much Intraregional Exchange Rate Variability Could a Currency Union Remove? The Case of ASEAN+3

A multilateral currency union removes the intraregional exchange rates but not the union rate variability with the rest of the world. The intraregional exchange rate variability is thus latent. A two-step procedure is developed to measure the variability. The measured variables are used to model inflation and intraregional trade growth of individual union members. The resulting models form the base for counterfactual simulations of the union impact. Application to ASEAN+3 shows that the intraregional variability consists of mainly short-run shocks, which have significantly affected the inflation and trade growth of major ASEAN+3 members, and that a union would reduce inflation and promote intraregional trade on the whole but the benefits facing each member vary and may not be significant enough to warrant a vote for the union.Currency union, Latent variables, Dynamic factor model, Simulation

Unit Root Tests with Markov-Switching

Diba and Grossman (1988) and Hamilton and Whiteman (1985) recommended unit root tests for rational bubbles. They argued that if stock prices are not more explosive than dividends, then it can be concluded that rational bubbles are not present. Evans (1991) demonstrated that these tests will fail to detect the class of rational bubbles which collapse periodically. When such bubbles are present, stock prices will not appear to be more explosive than the dividends on the basis of these tests, even though the bubbles are substantial in magnitude and volatility. Hall et al. (1999) show that the power of unit root test can be improved substantially when the underlying process of the sample observations is allowed to follow a first-order Markov process. Our paper applies unit root tests to the property prices of Hong Kong and Seoul, allowing for the data generating process to follow a three states Markov chain. The null hypothesis of unit root is tested against the explosive bubble or stable alternative. Simulation studies are used to generate the critical values for the one-sided test. The time series used in the tests are the monthly price and rent indices of Seoul's housing (1986:1 to 2003:6) and Hong Kong's retail premise (1980:12 to 2003:1). The investigations show that only one state appears to be highly likely in both cases. The switching unit root tests failed to find explosive bubbles in the price series, which might be due to the fact that the power of test is weak in the presence of heteroscedasticityunit root, three states markov switching, explosive rational bubbles

Signal Extraction with Kalman Filter: A Study of the Hong Kong Property Price Bubbles

Since Flood and Garber (1980), the debate surrounding speculative bubbles has never subsided. A key obstacle to resolve this issue is the identification problem. A bubble is usually inferred from some assumed fundamental determinants of a price. These assumptions could be over-simplified. Furthermore, there might be data measurement errors. In this paper, we attempt to capture such errors with a latent state variable. This variable is extracted with Kalman filter. Based on our empirical comparisons, we find that it is possible to attribute the observed large price swings in the property market of Hong Kong during the 1980s and 1990s to a periodically collapsing rational speculative bubble.rational speculative bubble, misspecification or measurement error, Kalman filter

Unit Root Tests With Markov-Switching

Diba and Grossman (1988) and Hamilton and Whiteman (1985) recommended unit root tests for rational bubbles. They argued that if stock prices are not more explosive than dividends, then it can be concluded that rational bubbles are not present. Evans (1991) demonstrated that these tests will fail to detect the class of rational bubbles which collapse periodically. When such bubbles are present, stock prices will not appear to be more explosive than the dividends on the basis of these tests, even though the bubbles are substantial in magnitude and volatility. Hall et al. (1999) show that the power of unit root test can be improved substantially when the underlying process of the sample observations is allowed to follow a first-order Markov process. Our paper applies unit root tests to the property prices of Hong Kong and Seoul, allowing for the data generating process to follow a three states Markov chain. The null hypothesis of unit root is tested against the explosive bubble or stable alternative. Simulation studies are used to generate the critical values for the one-sided test. The time series used in the tests are the monthly price and rent indices of Seoul’s housing (1986:1 to 2003:6) and Hong Kong’s retail premise (1980:12 to 2003:1). The investigations show that only one state appears to be highly likely in all series under investigation and the switching unit root procedure failed to find explosive bubbles in both prices.unit root, bootstrap, Markov-Switching

Markov-switching Unit Root Test: A study of the Property Price Bubbles in Hong Kong and Seoul

Evans (1991) demonstrates that the unit root tests recommended by Hamilton and Whiteman (1985) and Diba and Grossman (1988) will fail to detect periodically collapsing rational bubbles. Hall et al. (1999) however show that the power of this test procedure can be improved by incorporating a Markov-switching state variable. In this paper, we apply both procedures to selected data from Hong Kong and Seoul. Both point to the possible existence of a periodically-collapsing bubble in each price series investigated, with the second procedure more precise on timing the bubble. Our Markovswitching model is validated using a symmetry test and a Wald test.Markov-switching, unit root test, periodically-collapsing bubble, real-estate

Full-waveform inversion of ground-penetrating radar data and its indirect joint petrophysical inversion with shallow-seismic data

Both full-waveform inversion (FWI) of ground-penetrating radar (GPR) and shallow-seismic data have received special attention in the past decade because they allow the reconstruction of seismic and electromagnetic (EM) properties at high resolution. Research on the two FWIs includes: FWI of single geophysical data and joint FWI of multiple geophysical data. In this dissertation, I focus on GPR FWI in the former and joint petrophysical inversion (JPI) in the latter. In GPR FWI, the high computational costs and frequency-independent assumption are two problems that limit its development. To reduce computational costs, I apply a subset FWI (SFWI) to multi-offset GPR data. SFWI uses the data simulated on a model subset to approximate the data simulated on an entire model. Thus it obtains theoretical speedup and memory saving factor equal to the size ratio of the model and its subset. SFWI has higher or lower than expected speedups when combined with the source parallelization and model domain parallelization, respectively. The model subset depends on the illumination of the acquisition geometry used, for which I give rules of thumb by quantifying its effect on the simulation and inversion. Both 2-D synthetic and field data validate that SFWI provides results comparable to FWI but requires much lower computational costs than FWI. This study makes FWI an affordable technique for general users and promotes its application in addressing near-surface problems. The second problem means that dielectric permittivity and electrical conductivity are supposed to be frequency independent in most GPR FWI, which may lead to false estimates if they strongly depend on frequency. I develop frequency-dependent GPR FWI to solve this problem. Using the $\tau$-method introduced from the seismic community, I define the permittivity attenuation parameter to quantify the attenuation resulting from the complex permittivity. The new parameter acts as a low-pass filter, distorting the waveform and decaying the amplitude of the EM waves. The use of permittivity attenuation reduces the number of reconstructed parameters in frequency-dependent GPR FWI. The 2-D synthetic examples illustrate that permittivity attenuation is necessary for reconstructing permittivity and conductivity models in frequency-dependent media. The 2-D field example shows that frequency-dependent GPR FWI provides a better fit to the observed data and a more robust conductivity reconstruction in a high permittivity attenuation environment than frequency-independent GPR FWI. This research greatly expands the application of GPR FWI in more complicated media. Shallow-seismic and GPR FWI can provide complementary information for each other. Based on the sensitivity difference of the two data to petrophysical parameters, I propose indirect JPI, where seismic data are used for porosity reconstruction and GPR data are used for saturation reconstruction. Unlike conventional JPI, I first update the seismic and GPR parameters using non-petrophysical parametrizations and then transform the most reliable estimates to petrophysical parameters. The 2-D synthetic tests show that indirect JPI can provide more accurate and consistent results than conventional JPI. In addition, due to the rational use of the sensitivity of geophysical data to parameters, indirect JPI is more robust when uncertainties exist in petrophysical $a\;priori$ knowledge. More importantly, indirect JPI is flexible to integrate different types of seismic and EM waves and acquisition geometries depending on the target of interest, resulting in the best solution. Indirect JPI has been proven to be a promising approach for multiparameter reconstructions. To validate if indirect JPI can solve the real problem, I apply it for the first time to Love-wave and multi-offset surface GPR field data. It provides consistent imaging of near-surface targets with good accuracy by estimating seismic, EM, and petrophysical models. The inversion results are validated by direct-push technology and borehole measurements. This application suggests that indirect JPI can avoid conflicting geological interpretations that may arise in individual inversions and shows higher efficiency of information exchange than joint structural inversion. Furthermore, this method is robust with different petrophysical initial models and coefficients, for instance, Archie\u27s coefficients. The study verifies the feasibility of using indirect JPI to invert multiple geophysical field data and promotes the broader applicability of petrophysical methods. In summary, this dissertation (1) reduces the computational costs of GPR FWI and extends GPR FWI to frequency-dependent media, and (2) proposes a new joint strategy to combine GPR and shallow-seismic data and validates its performance through 2-D synthetic and field examples