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"ABSTRAKTesis ini membahas mengenai dampak variabel makroekonomi dan mikroekonomiyang mempengaruhi perkembangan NPF perbankan syariah di Indonesia. Variabelmakroekonomi yang dianalisis dalam penelitian ini adalah GDP riil, Indeks HargaKonsumen (CPI), Sertifikat Bank Indonesia (SBI), dan base money (MO). Sedangkanvariabel mikroekonomi yang dianalisis dalam penelitian ini adalah Financing toDeposit Ratio (FDR) dan total aset perbankan syariah (SIZE). Penelitian inimenggunakan metode ARDL (Autoregressive Distributed Lag). Adapun hasil daripenelitian ini menyatakan bahwa semua variabel makroekonomi dan mikroekonomicukup mempengaruhi perkembangan NPF. Namun GDP berperan penting terhadapperkembangan NPF perbankan syariah karena berpengaruh terhadap sector riil yangbiasanya menjadi fokus perbankan syariah."

"Suatu runtun waktu yang memiliki variabel respon biner disebut runtun waktu biner. Runtun waktu biner dapat dimodelkan menggunakan model umum Autoregressive dengan pendekatan regresi non-linier. Kedem Fokianos 2000 mengenalkan model runtun waktu biner melalui pendekatan Autoregressive dan regresi logistik. Metode yang digunakan untuk penaksiran parameter yaitu metode Partial Likelihood. Metode Partial Likelihood ini dilakukan dengan menentukan fungsi Partial Likelihood yang dibentuk dari probability density function pdf marginal distribusi Bernoulli. Namun, dalam proses penaksiran parameter menggunakan metode Partial Likelihood ditemukan kesulitan untuk mendapatkan solusi secara langsung dikarenakan persamaan yang tidak linier closed form. Oleh karena itu, untuk mengatasi hal tersebut dilakukan iterasi menggunakan metode Fisher Scoring. Aplikasi data pada penaksiran parameter untuk model runtun waktu biner dalam tugas akhir ini menggunakan data kompetisi balap perahu antara Universitas Cambridge dan Universitas Oxford yang dicatat pada tahun 1946 sampai 2011 dengan jumlah data berbeda yaitu 22, 44, dan 66 data. Berdasarkan aplikasi data yang dilakukan, diperoleh hasil bahwa penaksiran parameter untuk model runtun waktu biner menggunakan Partial Likelihood dengan jumlah data yang berbeda menghasilkan penaksir parameter yang relatif sama atau tidak memiliki perbedaan yang signifikan.

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A time series that has binary respon variable is called a binary time series. Binary time series can be modeled using the Autoregressive general model and nonlinear regression approach. Kedem Fokianos 2000 introduced a binary time series model through the Autoregressive and logistic regression approach. The parameters of binary time series are estimated using the Partial Likelihood method. The Partial Likelihood method is performed by determining the Partial Likelihood function derived from the marginal probability density function pdf of Bernoulli distribution. However, in the process of parameter estimation using this method, the form of final function to obtain parameters is not in the closed form equation. To face this problem, Fisher scoring iterations are perfomed.

The application of parameter estimation of the model uses the data about boat racing competition between the University of Cambridge and Oxford University from 1946 to 2011. Based on the data application, parameter estimation of the binary time series model using partial likelihood with different amounts of data resulting in a relatively same or no significant parameter estimator."

"Tujuan penelitian ini adalah untuk mengukur dan mengetahui faktor-faktor yang mempengaruhi volatilitas pergerakan saham individual pada sektor infrastruktur, utilitas, dan transportasi sejak Januari 1998 sampai dengan Desember 2005. Model yang digunakan untuk mengetahui volatilitas tergantung dari hasil uji terhadap varian residual yang digunakan. Jika varian residual tersebut bersifat tidak konstan maka menggunakan model Autoregressive Heteroskedastic (ARCH) dan Generalized Autoregressive Heteroskedastic (GARCH). Namun bila varian dari residual konstan akan menggunakan model Ordinary Least Square, dalam penelitian ini model yang sesuai adalah AutoRegressive Moving Average (ARMA).Berdasarkan hasil pengolahan data selama periode penelitian ditemukan bahwa terdapat enam emiten yang volatilitasnya bersifat hornoscedastic, yaitu PT Berlian Laju Tanker (BLTA), PT Bukaka Utama (BUKK), PT Citra Marga Nusapala (CMNP), PT Petrosea (PTRO), PT Zebra (ZBRA) dan PT Telekomunikasi Indonesia (TLKM). Untuk kondisi demikian maka digunakan model AutoRegressive Moving Average (ARMA).Sementara terdapat enam emiten yang bersifat heteroseedastic yaitu PT Centris Multi Persada (CMPP), PT Humpus Intermoda (HITS), PT Indosat (ISAT), PT Rigs Tender (RIGS), PT Mitra Rajasa (MIRA) dan PT Steady Safe (SAFE), sehingga digunakan model ARCH dan GARCH. Selain itu, terdapat emiten yang memiliki ARCH/GARCH yang volatilitasnya bersifat persisten yang ditandai dengan nilai a + I yaitu RIGS (1998, 1999, 2000, 2001, 2002, 2004, 2005) HITS (1999), CMPP (2000). Adanya persistensi dalam volatilitas mengurangi kestabilan model ARCH/GARCH. Hal ini disebabkan sepanjang periode penelitian data return saham tersebut bersifat stagnan atau pergerakan return yang sangat tinggi dan tajam.

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The objectives of this research are to recognize and measure factors affecting the volatility of individual shares movement in infrastructure, utility and transportation sector during January 1998 - December 2005. Model used to identify volatility depend on the result of the test of the varian of residual on the used data. Should the varian of the residual act inconstantly then we use Autoregressive Heteroskedastic (ARCH) and Generalized Autoregressive Heteroskedastic (GARCH) model. But if the varian of the residual act constantly then we use Ordinary Least Square model, which in this research the model that fit the data is AutoRegressive Moving Average (ARMA) model.Base on the result of the data processing on the research period, it is found that there are six emitens whose volatility is homoscedastic, which are PT Berlian Laju Tanker (BLTA), PT Bukaka Utama (BUKK), PT Citra Marga Nusapala (CMNP), PT Pelrosca (PTRO), PT Zebra (ZBRA) dan PT Telekomunikasi Indonesia (TLKM). Therefore model used for these emitens is Auto Regressive Moving Average (ARMA) model.While there are six emitens whose volatility is heteroscedastic which are PT Centris Multi Persada (CMPP), PT Humpus lntermoda (HITS), PT Indosat (ISAT), PT Rigs Tender (RIGS), PT Mitra Rajasa (MIRA) dan PT Steady Safe (SAFE). Therefore model used for these emitens are either Autoregressive Heteroskedastic (ARCH) or Generalized Autoregressive Heteroskedastic (GARCH) model.It is also found within the research that there are six emitens whose models has a value of a + b ≥ 1, which depicts a persistency. They are RIGS (1998, 1999, 2000, 2001, 2002, 2004, 2005) HITS (1999), CMPP (2000). The presence of a persistency of a model may decrease the stability of the ARCHIGARCH model. This problem may occur due to the movement of the data which are very fluctuate or too stagnant."

"This paper analyzes the relationship between the exchange rate and stock market in Jakarta,Singapore,malaysia,Thailand,Philippine and Hongkong using high frequency data....."

"ABSTRACTModel Generalized Space Time Autoregressive adalah suatu model spatio temporal yang merupakan pengembangan model Space Time Autoregressive (STAR). Model STAR adalah model spatio temporal dengan asumsi parameter-parameter sama untuk setiap lokasi. Model GSTAR dibentuk sebagai pengembangan dari model STAR yang memungkinan untuk mengestimasi parameter yang berbeda untuk setiap lokasinya. Borovkova, Lopuhaa dan Ruchjana (2002) mengembangkan model Generelized Space Time Autoregressive (GSTAR) dimana paramater-parameter pada model, berbeda untuk setiap lokasi. Metode kuadrat terkecil (least square method) digunakan untuk mengestimasi parameter pada model GSTAR. Metode kuadrat terkecil merupakan metode pendugaan parameter yang meminimumkan jumlah kuadrat error. Penggunaan model GSTAR pada data penyakit Hepatitis A di 5 kotamadya DKI Jakarta pada tahun 2011-2017 menghasilkan model GSTAR(4,1) sebagai model yang dipilih.

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ABSTRACTGeneralized Space Time Autoregressive(GSTAR) model is a spatio temporal model which is the development of the Space Time Autoregressive (STAR) model. The STAR model is a spatio temporal model assuming the same parameters for each location. The GSTAR model was formed as a development of the STAR model which makes it possible to estimate different parameters for each location. Borovkova, Lopuhaa and Ruchjana (2002) developed the Generelized Space Time Autoregressive (GSTAR) model where parameters in the model differ for each location. The least square method is used to estimate the parameters in the GSTAR model. The least squares method is a parameter estimation method that minimizes the number of squared errors. The use of the GSTAR model on Hepatitis A data in 5 DKI Jakarta municipalities in 2011-2017 produced the GSTAR (4.1) model as the chosen model."

"ABSTRAKThis paper evaluates monetary policy transmission in both tranquil and turbulent periods for Mexico, Indonesia, Nigeria, and Turkey. Using a structural vector autoregressive model, we find that the effect of structural shocks from supply, demand,and financial sources tend to fizzle out faster for Nigeria and Mexico compared toIndonesia and Turkey. Another important finding is that while monetary authorities in Indonesia and Turkey are more responsive to inflation those in Mexico and Nigeria are more influenced by the exchange rate. We also observe differences in the conduct of monetary policy between the tranquil and turbulent periods."

"Studi ini menganalisis integrasi pasar saham di beberapa negara di kawasan Asia Pasifik. Penelitian ini berfokus pada pasar saham lima negara, yaitu Jepang, Indonesia, Filipina, vietnam dan Bangladesh, dengan memberi perhatian khusus pada krisis keuangan 2008-2009. Studi ini dilatarbelakangi oleh penelitian mengenai integrasi pasar saham tradisional, yang menunjukkan bahwa pasar saham negara berkembang lebih tersegmentasi dan pasar saham negara maju sangat terintegrasi. Dengan menggunakan metode vector autoregressive (VAR), hasil penelitian ini menunjukkan bahwa pasar saham masih kurang terintegrasi. Pada periode sebelum krisis, hanya pasar saham Filipina dan Indonesia yang terintegrasi, sedangkan pasar saham Jepang terintegrasi dengan pasar saham Indonesia, Filipina dan Bangladesh. Sementara itu, pada periode setelah krisis, pasar saham Bangladesh tidak terintegrasi dengan pasar saham Jepang.

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This study analysis the integration of stock markets in some countries in the Asia-Pacific Region. The study focus on stock market in five county, namely: Japan, Indonesia, Philippines, Vietnam and Bangladesh, with special attention to the 2008-2009 financial crisis period. This study is motivated by the research of traditional stock market integration, which showed that stock markets in developing countries were more segmented while stock markets in developed countries were more integrated. By using vector autoregressive method (VAR), this study reveals that stock markets of those five countries were still less integrated. In the pre-crisis period, only the stock markets of Philippines and Indonesia were integrated, while Japan's stock market was integrated with the stock markets of Indonesia, the Philippines and Bangladesh. Meanwhile, in the period after the crisis, Bangladesh's stock market did not integrated with japan's."

"Donald Trump was elected into US presidential office in November 2016 and his protective approach on international economics blatantly differs with actions taken by politicians before him. Those policies and sentiments are always expressed via his twitter account and received various responses from citizens and governments alike. This research aimed to find out if the US diplomatic hostility towards its partners on social media will have a direct impact on their bilateral trade flows because of diplomatic retaliation. Using monthly import and export data between the US and 10 major trading partners from June 2015 until March 2018, their respective Economic Policy Uncertainty EPU Index, and tweets analysed with VADER sentiment analysis, we estimate an ARDL model of Trump rsquo s twitter sentiments and its effect on trade. We found out that tweets and trade are not causally linked, yet in most cases they are cointegrated in the short and long run. Through ARDL, we can infer that tweets took around 4 months to take effect and they are only significant in several countries.

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Donald Trump terpilih menjadi Presiden Amerika Serikat di bulan November 2016 dan kebijakannya yang protektif sangat berbeda dengan politisi-politisi sebelumnya. Kebijakan dan opini Trump selalu diutarakan melalui akun Twitternya dan mendapat berbagai tanggapan baik dari masyarakat maupun negara lain. Penelitian ini bertujuan untuk mencari tahu apabila ketegangan diplomatis akibat perilakunya di media social akan memiliki efek langsung pada perdagangan bilateral sebagai konsekuensi dari balasan diplomatic. Dengan menggunakan data bulanan impor dan ekspor antara AS dan 10 partner dagang dari Juni 2015 sampai Maret 2018, data Indeks Ketidakpastian Kebijakan Ekonomi EPU dari negara-negara tersebut dan twitnya yang dianalisa dengan metode analisa sentiment VADER, kami mengestimasi model ARDL untuk nilai sentiment twit Trump dan efeknya pada perdagangan. Melalui ARDL, ditemukan bahwa twit membutuhkan sekitar 4 bulan untuk memengaruhi arus perdagangan dan bahwa twit merupakan variabel yang signifikan hanya untuk beberapa negara tertentu."

"The paper discussed about the neural network models at nonlinear autoregressive process which is applied in the composite stock price index data at Surabaya stock exchange. ...."

"This study examines the effect of macroeconomics variables to a mutual fund's return. The macroeconomic variables hypothesized to affect the portfolio performance are change in exchange rate (IDR to US dollar return), change in SBI rate, and growth of money supply. Furthermore, the prediction of expected return is also examined whether related to return of previous periods (lag return) and the level of their volatility. The selected mutual fund is Mawar Mutual Funds issued by PT Dana Reksa and data are collected from June 31 *' 1998 until May 21st 2004. The statistical method to test on the hypothesis is Generalized Autoregressive Conditional Heteroskedasticity (GARCH). The results show that there are negative relationships between Mawar Mutual Fund's return to exchange rate return as well as change in SBI rate. This research also indicates that exchange rate return and change in SBI rate affect Mawar Mutual Fund's volatility. There is significant relationship between first lag return and Mawar's return of this period. But the results do not show any relation between Mawar's return to its volatility as well as the growth of money supply."