The Philippine Review of Economics

The Editor, The Philippine Review of Economics, Rm 237, School of Economics, University of the Philippines, Diliman, Quezon City, 1101. Based on the BSP's publication policy, the latest statistical reports (which include DCS) are treated as preliminary information (Table 1).

TABLE 1. Depository corporations survey SRF-based* (in million pesos) Levels (as of end period)Changes in levelsPercent change Aug-20Sep-20Aug-21r,pSep-21pm-o-my-o-ym-o-my-o-y Sep 21- Aug21Aug 21-Aug 20Sep 21-Sep20Sep 21pAug 21 r,pSep 21p 1

Review of related literature

Using these input variables, Boluis and Rayner [2020] and Soybilgen and Yazgan [2021] found that tree methods provide better predictions than benchmark models. 2018] used regularization and tree-based methods to construct a model that could accurately predict the movement of GDP growth in New Zealand.

Data

Data points between each period of average data of the input variables (eg, mid-month data) are treated as missing values ​​and interpolated using a spline interpolation method commonly used for nonlinear data estimation. 4 BSP claims for other sectors Monthly input variable 15 5 Foreign investment input variable (in) weekly 30 6 Foreign portfolio investment input variable (out) 30.

TABLE 2. List of data

Research methodology

It then uses the prediction errors from the decision tree to train another decision tree. 32 Decision Tree is the basic structure of any tree-based machine learning method used for classification and regression problems (James et al. [2013]; Fan [2019]).

FIGURE 1. Decision tree growing process Recursive binary splitting of two-dimensional feature space

Results and analysis 34 1. Univariate models

Additionally, this particular outcome can be similarly observed in the overall prediction errors of DFM. These figures are significantly lower than the overall prediction errors registered by the univariate and multivariate models performed in this study (Figure 9).

TABLE 3. RMSE of autoregressive models 36

Conclusion

2018] “Current inflation rate in the Philippines using mixed frequency models”, University of the Philippines - School of Statistics. Soleh [2020] "Nowcasting Indonesia's GDP Growth Using Machine Learning Algorithms", RePEc Munich Personal Archive [MPRA] No.

TABLE 12. RMSE of benchmark and machine learning models (summary)

Does bank competition affect bank risk-taking differently?

Introduction

The impact of the COVID-19 pandemic on banking operations has contributed to the decline in banking competition. Measures of bank competition are then regressed on the Z-score to estimate the effect of these measures of competition on bank risk.

Survey of empirical findings

Martinez-Miera and Repullo [2010] show that theoretically there is a non-linear relationship between bank competition and risk taking in the credit market. As of the end of December 2021, there were 29 foreign banks licensed by the BSP to operate in the Philippines.

TABLE 1. Current phase in digital transformation

Data and empirical strategy

The focus of the discussions is on the dynamics between bank competition and measures of bank risk, such as the H-statistic (Table A1), the Lerner Index (Table A2) and the Boone Indicator (Table A4). I use the components of the Z-score in Equation 1 to shed light on the impact of competition on bank risk.

Table 3 shows the descriptive statistics of the major variables used in the final estimation

Results

Data is based on the balance sheet of the Philippine banking system as of August 9, 2021 on the BSP website. Based on the Philippine Financial System Report (Second Semester 2020) on the BSP website. In the database, I consider 28 mergers and consolidations in the U/KB, TB and R/CB sectors from March 2010 to December 2020.33.

Straughan [2020] “The link between banking competition and risk in the UK: two views on policy making”, Bank of England Staff Working Paper No.

TABLE A1. Bank competition and bank risk using H-Statistic, March 2010 to December 2020

Insights on inflation expectations in the Philippines from a household survey

Survey-based expectations in the Philippines: forecast accuracy and rationality

The shift led to an increased importance of the expectations channel in the transmission of monetary policy in the country. Inflation expectations are thus modeled adaptively (i.e. using distributed lags of actual inflation) in the expectations-based Phillips curve analysis [Friedman 1968]. We use the quarterly inflation rate (annualized) and the quarterly BSPCES results for expected household inflation.

These variables relate to survey questions relating respectively to (i) the current level of household income (compared to twelve months ago) and (ii) expectations about household income in the next twelve months.

FIGURE 1. Inflation, inflation expectations, and inflation target (in percent; year-on-year)

Drivers of household inflation expectations

The variables for expected economic conditions of the household for the current quarter and in the next 12 months refer to households' perception of the current economic condition of the country in relation to the previous 12 months (ie, the same, better or worse ) and expectations about the economic state of the country in the next 12 months (ie, the same, better or worse). 15 Households' perceptions of unemployment, interest rates and exchange rates in the future relate to their expectations of whether these variables will increase or decrease in the current quarter (relative to 12 months ago) and whether these variables will they increase or decrease or not in the next 12 months. Depending on the other factors considered in the regressions, women have lower inflation forecasts compared to men (ie, the fourth column regression results in Table 7).

Thus, they are more exposed to changes in the prices of goods and services than men.

TABLE 6. Regression of aggregated CES results and perceptions on economic conditions Dependent variable: πe t (expectations of inflation formed at period t-h with h = 12 months)

Decline in inflation expectations

Announcing an explicit inflation target from a central bank contributes to the stronger anchoring of inflation expectations to the target which reduces uncertainty about future inflation. In the post-GFC period, the Philippines experienced strong economic growth that was broad-based and more resilient to shocks.

FIGURE 4. Mean inflation forecast of CPI commodity groups (in percent)

Expectations and their implications central bank communication Central bank communication is crucial in managing expectations. The

Davig [2011] “Decomposing the declining volatility of long-term inflation expectations,” Journal of Economic Dynamics and Control. Weber [2019] “Monetary policy communications and their effects on household inflation expectations”, NBER Working Paper No. Swanson [2007] “Inflation targeting and the anchoring of inflation expectations in the Western Hemisphere”, FRBSF Economic Review 2007.

1998] “Inflation Expectations and the Transmission of Monetary Policy,” Finance and Economics Discussion Series 1998-43, Board of Governors of the Federal Reserve System.

Heterogenous impact of monetary policy on the Philippine rural banking system

Empirical methodology

This is measured by the overnight reverse repurchase rate (RRP), the BSP's monetary policy rate. If any of the parameters ∅, ψ and ρ are negative, it can be concluded that the bank credit channel (BLC) of monetary policy in the country functions through the rural banks. In estimating the model, this study takes into account the persistence of bank loans by using a linear dynamic panel Generalized Method of Moments (GMM) technique.6 Therefore, the lagged dependent variable is included as one of the regressors.

This bias appears in Equation 5 because the lagged value of loans is related to eit-1, which is a function of unobserved bank-specific fixed effects ϑi.

Empirical results

Differential impact of monetary policy Dependent variable: first difference log of the total loan portfolio. 0.81) Existence of a bank credit channel for monetary policy. The heterogeneous effects of monetary policy are more evident in the lending behavior of smaller rural banks. 2013] “Monetary Policy Transmission through the Bank Credit Channel in the Philippines,” International Journal of Trade, Economics and Finance 4(1).

Bondoc [2018] “The Impact of Monetary Policy on Bank Lending in the Philippines,” Bangko Sentral Review 2018:34.

TABLE 3. Results of baseline model and bank indicator regressions Dependent variable: first difference log of total loan portfolio

How do exchange rates affect the Big One?

An empirical analysis of the effect of exchange rates on RCEP exports using the gravity model

Exchange rates and international trade

In the long term, the volatility of exchange rates has a significant impact on the volume of bilateral exports of most trading partners. The exporter's risk index and the importer's risk index significantly increase real bilateral exports; Real exchange rate volatility. TA*The rate of exchange rate depreciation significantly increases average bilateral trade; Exchange rate volatility and.

An empirical study by Hayakawa and Kimura [2009] found that exchange rate volatility significantly reduces a country's bilateral exports.

TABLE 1. Summary of previous empirical studies on exchange rates and international trade PaperMethodologyCountryPeriod

Methodology and data

The dummy variable is equal to 1 if countries i and j observe the floating exchange rate regime ratio (actual classification) at time t. An increase in the value of the hypothetical "real" exchange rate indicates a depreciation of the domestic currency, while an increase in the REER index indicates an appreciation of the domestic currency. This was calculated by regressing a hypothetical "real" exchange rate on a country's real GDP per capita with time fixed effects.

Third, the degree of exchange rate mismatch was calculated by considering the difference between the actual hypothetical "real" exchange rate and the estimated /.

TABLE 2. Definition and sources of empirical variables used

Results and discussion

Moreover, the interaction variable between floating exchange rate regimes and the natural logarithm of exchange rate volatility is significant at the 5 percent level. Countries under a floating exchange rate regime are affected by the trade-reducing impact of exchange rate volatility. The observed negative effect of exchange rate volatility on exports is consistent with that of Clark et al.

Results indicate that the interaction between the natural logarithm of exchange rate volatility and a floating exchange rate regime remains significant.

TABLE 4. Baseline model regression results Method

Summary and conclusion

Hegerty [2009] “Effects of Exchange Rate Volatility on United States-Mexico Merchandise Trade”, Southern Economic Journal. Kimura [2009] "The Effect of Exchange Rate Volatility on International Trade in East Asia", Journal of The Japanese and International Economies. 2020] “Effects of Exchange Rate Volatility on COMESA Exports: A Panel Gravity Model Approach”, Journal of Applied Finance & Banking.

2014] “The Effects of Exchange Rate Volatility on Commodity Trade Flows between the US and Thailand”, PhD Dissertation.

The long and the short of it

Data and context

We use a unique event in the Philippines – when the BSP in 2001 and 2005 issued Circulars Nos. We also use statistical data on the number of banks and MOBs in municipalities collected by BSP for the periods and 2009. There is no statistically significant difference in the share of employed workers between the periods of presence before and after MOB.

Finally, the number of poor households and bank density7 in the municipalities is higher post-MOB presence, while the population is not statistically different between pre- and post-MOB presence periods.

FIGURE 1. Geographical distribution of microfinance-oriented banks in the Philippines

Estimation strategy

Education level of the household head does not differ statistically between pre- and post-MOB presence periods. Household characteristics are gender, age and education of the household head, financial assets owned and home ownership. Results in Appendix Table A2 indicate that there is no significant difference in the means from the baseline.

We now turn to the heterogeneous effects of MOB presence on household poverty levels.

TABLE 1. Summary statistics Pre-MOB PresencePost-MOB Presence Difference (Pre-MOB vs Post-MOB) t-statistics200320062009 Mean Standard Deviation

Tests on omitted variables

Panel A: Treatment Group: Continuing Households (With MOB in 2006 and 2009) Control Group: Never Households (No MOB). Panel B: Treatment Group: Continuing Households (With MOB in 2006 and 2009) Control Group: Never Households (No MOB). Panel C: Treatment Group: Continuing Households (With MOB in 2006 and 2009) Control Group: Never Households (No MOB).

Column (2) reports whether the identified set excludes zero and column (3) reports whether the estimated biased corrected coefficient lies within the confidence interval of the estimated controlled effect β̃.

TABLE 6. Robustness to omitted variable bias of the effects of long-term microfinance-oriented bank presence

Policy insights

The treatment effect of income and consumption expenditure is not expressed in percentage change, but for the arcsinh linear specification with dummy independent variables from the IPW DID-FE regression. 2005] proportionality coefficient that would be necessary to fully attribute the treatment effect to the influence of unobservable substances. On the one hand, another microfinance product – micro-agri loans up to ₱150,000 and loans that start small and increase incrementally based on the banks' policies – are not easily accessible as they can only be obtained on a short-term basis (up to twelve months) . ) by people with multiple income-generating activities (i.e. on-farm and off-farm), with agricultural activities active for at least two years, and by existing borrowers with a good track record based on the banks' policies.

Such initiatives would promote product diversification, integrate microfinance borrowers into broader high-value markets, and improve borrowers' business skills, thereby enabling borrowers to run their businesses profitably, increasing business opportunities and preventing business closures.

Conclusion

Zinman [2015] "Microcredit influences: evidence from a randomized microcredit programming experiment by Compartamos Banco", American Economic Journal: Applied Economics. The Impacts of Microfinance: Evidence from Joint Liability Loans in Mongolia”, American Economic Journal: Applied Economics. Parienté [2015] “Assessing the Impact of Microcredit on Takers: Evidence from a Randomized Experiment in Morocco”, American Economic Journal: Applied Economics.

Khandker [1998] "The impact of group-based credit programs on poor households in Bangladesh: does the gender of participants matter?", Journal of Political Economy.